Repository: philip-ndikum/TemporalScope

Issue:
[Refactor]: Refactor TimeFrame
### **Problem Statement**  
The current TimeFrame API presents the following issues:  
1. **Verbosity**: Parameters like `dataframe_backend` are redundant, as the Narwhals library abstracts this functionality.  
2. **Mutability**: The underlying data is mutable, increasing the risk of bugs or unintended changes.  
3. **Unpythonic Type Handling**: The API relies heavily on manual type checks and follows a [LBYL](https://docs.python.org/3/glossary.html#term-LBYL) approach.  

### **Proposed Solution**  
To address these concerns, the following improvements are suggested:  
1. **Streamline the API**: Remove unnecessary parameters such as `dataframe_backend`, simplifying the interface and aligning with Narwhals' abstraction.  
2. **Enforce Immutability**: Make the TimeFrame API immutable to improve reliability and safety. This change aligns with the behavior of popular dataframe libraries, fostering familiarity and consistency for users.  
3. **Adopt Pythonic Type Handling**: Transition to a more idiomatic [EAFP](https://docs.python.org/3/glossary.html#term-EAFP) approach, or adopt a validation pattern similar to scikit-learn's [validate_parameter](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/utils/_param_validation.py#L148) for cleaner and more maintainable code.



Discussion:
philip-ndikum: The current test failures are due to notebook tutorials, which were not caught by pre-commit hooks. It may be more efficient to either:
	1.	Relax checks for tutorial notebooks, or
	2.	Implement MkDocs-native tests to validate tutorials directly.

Update: All backends have been refactored to use Narwhals natively, with 95-100% coverage, enabling rapid future development.
philip-ndikum: @kanenorman The current test failures are related to notebook tutorials, which were not caught by pre-commit hooks. This presents challenges in maintaining consistency for tutorials as the package evolves.

Proposed Solutions:
	1.	Relax Checks for Tutorials:
	•	Modify pre-commit hooks to reduce the strictness of checks for notebook tutorials, allowing faster iterations during development.
	2.	Leverage MkDocs-Native Validation:
	•	Shift to MkDocs for running and validating tutorials directly during documentation builds.
	•	This approach ensures tutorials are always in sync with the package while simplifying maintenance.

Update:
	•	Refactored Backends: All backends now use Narwhals natively, achieving 95-100% test coverage.
	•	Future Development: This refactoring sets the stage for rapid development, with a focus on building end-to-end Jupyter tutorials for advanced model-building workflows.
coveralls: 
[![Coverage Status](https://coveralls.io/builds/71581782/badge)](https://coveralls.io/builds/71581782)

coverage: 94.872% (-0.9%) from 95.781%
when pulling **0f56a12dc8911011612dfc2a539f26c95240f03e on feat/56-timeframe-refactor**
into **af09aefc619520ff7339d8c8df8fca385ac5b8f0 on main**.

philip-ndikum: @kanenorman updated tutorials but coveralls is fairly strict. 

Patch:
diff --git a/pyproject.toml b/pyproject.toml
index 9b74c52..885fb38 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,19 +26,13 @@ dependencies = [
   "statsmodels>=0.14.2", # Statsmodels: Provides statistical models and hypothesis tests, useful for traditional time series analysis.
 
   # Data Manipulation and Storage
-  "pandas>=1.5.0",           # Pandas: Core library for handling structured data in DataFrames.
-  "modin[all]>=0.31.0",      # Modin: Parallelizes Pandas operations for large datasets with Dask/Ray backend.
-  "pyarrow>=17.0.0",         # PyArrow: Enables efficient columnar data format (Arrow) for fast data processing and storage, commonly used with Parquet files.
-  "polars>=1.5.0",           # Polars: High-performance DataFrame library, ideal for handling large datasets in memory.
-  "dask[dataframe]>=2024.7", # Dask DataFrame: Handles parallelized computations for very large datasets.
+  "pandas>=1.5.0",           # Pandas: Required for DataFrame operations and compatibility with Narwhals.
+  "narwhals>=0.2.3",         # Narwhals: Backend-agnostic DataFrame operations, primary DataFrame interface.
 
   # Specialized Computation
   "flax>=0.8.5", # Flax: Neural network library for JAX, useful for building deep learning models.
   "jax>=0.4.31", # JAX: Accelerated computation for machine learning research, supporting CPU/GPU/TPU backends.
 
-  # Backend-Agnostic Processing
-  "narwhals>=0.2.3", # Narwhals: Ensures backend compatibility across Pandas, Polars, Modin, and other dataframes, optimizing performance.
-
   # Environment Configuration
   "python-dotenv>=1.0.1", # Python-Dotenv: Manages environment variables for secure and flexible configuration.
 
@@ -106,8 +100,13 @@ extra-dependencies = [
   "pytest",
   "pytest-cov",
   "pytest-custom_exit_code",
-  "pytest-mock",
-  "papermill>=2.5.0",
+  "pytest-mock",             # For mocking in tests
+  "papermill>=2.5.0",        # For notebook execution testing
+  # DataFrame Backends for Testing
+  "modin[all]>=0.31.0",      # Test Narwhals with Modin backend
+  "polars>=1.5.0",           # Test Narwhals with Polars backend
+  "dask[dataframe]>=2024.7", # Test Narwhals with Dask backend
+  "pyarrow>=17.0.0",         # Test Narwhals with PyArrow backend
 ]
 
 [tool.hatch.envs.docs.scripts]
diff --git a/src/temporalscope/core/core_utils.py b/src/temporalscope/core/core_utils.py
index eb58f68..3443766 100644
--- a/src/temporalscope/core/core_utils.py
+++ b/src/temporalscope/core/core_utils.py
@@ -17,132 +17,70 @@
 
 """TemporalScope/src/temporalscope/core/core_utils.py.
 
-This module provides essential utility functions for the TemporalScope package,
-including support for:
-    - Backend validation (Narwhals).
-    - Checking for nulls, NaNs, and handling mixed frequency issues in time series
-      data.
-    - Managing different modes (Single-step vs. Multi-step) for machine learning and
-      deep learning workflows.
-
-Type safety and validation follow Linux Foundation security standards to ensure
-robust backend interoperability.
-
-Engineering Design Assumptions:
--------------------------------
-TemporalScope is designed around two core modes for time series workflows, based
-on the assumption that users handle their own preprocessing (e.g., managing NaNs,
-encoding categorical variables, scaling, etc.).
-
-These modes represent generalized structures that support both machine learning
-and deep learning tasks, giving users the flexibility to manage their own
-model-building workflows.
-
-1. Single-step mode:
-   - In this mode, each row of the data corresponds to a single time step with a
-     scalar target.
-   - Compatible with traditional machine learning frameworks (e.g., Scikit-learn,
-     XGBoost) as well as deep learning libraries like TensorFlow and PyTorch.
-   - The data is structured as a single DataFrame, where each row is an
-     observation, and the target is scalar.
-   - Example workflows include regression, classification, and survival models.
-   - TemporalScope allows simple shifting/lagging of variables within this mode.
-   - After partitioning (e.g., using a sliding window), users can convert the
-     data into the required format for their model.
-
-2. Multi-step mode:
-   - This mode supports tasks like sequence forecasting, where the target is a
-     sequence of values (multi-step).
-   - Data is split into two DataFrames: one for the input sequences (X)
-     and one for the target sequences (Y).
-   - This mode is most commonly used in deep learning frameworks such as
-     TensorFlow and PyTorch, where the task involves predicting sequences of
-     time steps (e.g., seq2seq models).
-   - TemporalScope's partitioning algorithms (e.g., sliding window) can
-     partition this data for time series forecasting, making it ready for
-     sequential models.
+This module provides essential utility functions used throughout the TemporalScope package. It serves
+two main purposes:
+1. Core DataFrame operations for the Temporal Data Loader, handling all data manipulations through
+   Narwhals for backend-agnostic operations.
+2. Shared utilities used across the library for data validation, type checking, and format conversion.
+
+For testing purposes, we use a data generator to simulate runtime behavior, ensuring consistent
+functionality across supported backends. The module supports both single-target and multi-target time
+series analysis through a flexible API.
 
 Supported Modes:
 ----------------
-The following table illustrates the two core modes supported by TemporalScope.
-These are generalized super-structures for time series tasks. Users are expected
-to customize their workflows for specific model-building tasks (e.g., tree-based
-models, neural networks, etc.):
-
-
-| Mode | Description |
-|------|-------------|
-| single_step | General machine learning tasks with scalar targets. Each row is a single time step, and the target is scalar. Single DataFrame: each row is an observation. |
-| multi_step | Sequential time series tasks (e.g., seq2seq) for deep learning. The data is split into sequences (input X, target Y). Two DataFrames: X for input sequences, Y for targets. Frameworks: TensorFlow, PyTorch, Keras. |
++----------------+-------------------------------------------------------------------+
+| Mode           | Description                                                       |
+|                | Data Structure                                                    |
++----------------+-------------------------------------------------------------------+
+| single_target  | General machine learning tasks with scalar targets. Each row is   |
+|                | a single time step, and the target is scalar.                     |
+|                | Single DataFrame: each row is an observation.                     |
++----------------+-------------------------------------------------------------------+
+| multi_target   | Sequential time series tasks (e.g., seq2seq) for deep learning.   |
+|                | The data is split into sequences (input X, target Y).             |
+|                | Two DataFrames: X for input sequences, Y for targets.             |
+|                | Frameworks: TensorFlow, PyTorch, Keras.                           |
++----------------+-------------------------------------------------------------------+
 
 Example Visualization:
 ----------------------
-Here is a visual demonstration of the datasets generated for single-step and multi-step
-modes, including the shape of input (`X`) and target (`Y`) data compatible with most
-popular ML frameworks like TensorFlow, PyTorch, and SHAP.
-
-**Single-step mode**:
-
-- Input shape: X (num_samples, num_features)
-- Target shape: Y (num_samples, 1)  # Scalar target for each time step
-
-| time       | feature_1 | feature_2 | feature_3 | target |
-|------------|-----------|-----------|-----------|--------|
-| 2023-01-01 | 0.15      | 0.67      | 0.89      | 0.33   |
-| 2023-01-02 | 0.24      | 0.41      | 0.92      | 0.28   |
-
-**Multi-step mode (with vectorized targets)**:
-
-- Input shape: X (num_samples, num_features)
-- Target shape: Y (num_samples, sequence_length)  # Vectorized target for each input sequence
-
-
-| time       | feature_1 | feature_2 | feature_3 | target       |
-|------------|-----------|-----------|-----------|--------------|
-| 2023-01-01 | 0.15      | 0.67      | 0.89      | [0.3, 0.4]   |
-| 2023-01-02 | 0.24      | 0.41      | 0.92      | [0.5, 0.6]   |
-
-DataFrame Types:
-----------------
-TemporalScope handles various DataFrame types throughout the data processing pipeline. The following table
-illustrates the supported DataFrame types and validation cases:
-
-| DataFrame Type | Description | Example |
-|---------------|--------------|---------|
-| Narwhalified (FrameT) | DataFrames wrapped by Narwhals for backend-agnostic operations. These are validated first to ensure consistent handling across backends. | @nw.narwhalify decorated functions create these during operations. |
-| Native DataFrames | Core DataFrame implementations from supported backends. These are validated directly against TEMPORALSCOPE_CORE_BACKEND_TYPES. | pd.DataFrame, pl.DataFrame, pa.Table |
-| DataFrame Subclasses | Custom or specialized DataFrames that inherit from native types. Common in: - Custom DataFrame implementations - Backend optimizations (e.g. lazy evaluation) - Backend compatibility layers | TimeSeriesDataFrame (pd.DataFrame), dask.dataframe.DataFrame (inherits from pandas) |
-| Intermediate States | DataFrames in the middle of narwhalify operations or backend conversions. These may be temporary subclasses used for optimization or compatibility. | LazyDataFrame during backend conversion operation chaining |
-Naming Conventions:
--------------------
-The following naming conventions are used for utility functions in this module:
-
-| Pattern | Purpose |
-|---------|---------|
-| `validate_<object>` | Checks that an object meets specific requirements and raises an error if it doesn't. |
-| `is_<property>` | Returns metadata about an object (Boolean or other). |
-| `convert_<object>` | Transforms an object to a desired type or structure. |
-| `check_<condition>` | Performs a specific check and returns a result or raises an error if the condition is violated. |
-| `sort_<object>` | Orders an object based on specified criteria. |
-
+Single-target mode:
+    +------------+------------+------------+------------+-----------+
+    |   time     | feature_1  | feature_2  | feature_3  |  target   |
+    +============+============+============+============+===========+
+    | 2023-01-01 |   0.15     |   0.67     |   0.89     |   0.33    |
+    +------------+------------+------------+------------+-----------+
+    | 2023-01-02 |   0.24     |   0.41     |   0.92     |   0.28    |
+    +------------+------------+------------+------------+-----------+
+
+    Shape:
+    - `X`: (num_samples, num_features)
+    - `Y`: (num_samples, 1)  # Single target value per time step
+
+Multi-target mode (with vectorized targets):
+    +------------+------------+------------+------------+-------------+
+    |   time     | feature_1  | feature_2  | feature_3  |    target   |
+    +============+============+============+============+=============+
+    | 2023-01-01 |   0.15     |   0.67     |   0.89     |  [0.3, 0.4] |
+    +------------+------------+------------+------------+-------------+
+    | 2023-01-02 |   0.24     |   0.41     |   0.92     |  [0.5, 0.6] |
+    +------------+------------+------------+------------+-------------+
+
+    Shape:
+    - `X`: (num_samples, num_features)
+    - `Y`: (num_samples, sequence_length)  # Multiple target values per time step
 """
 
 import os
-import warnings
-from importlib import util
-from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Type, Union
+from typing import Dict, List, Literal, Optional
 
-import dask.dataframe as dd
-import modin.pandas as mpd
 import narwhals as nw
-import pandas as pd
-import polars as pl
-import pyarrow as pa
 from dotenv import load_dotenv
-from narwhals.typing import FrameT, IntoDataFrame
+from narwhals.typing import FrameT
 from narwhals.utils import Implementation
 
-from temporalscope.core.exceptions import TimeColumnError, UnsupportedBackendError
+from temporalscope.core.exceptions import TimeColumnError
 
 # Load environment variables from the .env file
 load_dotenv()
@@ -154,47 +92,14 @@ MODE_SINGLE_TARGET = "single_target"
 MODE_MULTI_TARGET = "multi_target"
 VALID_MODES = [MODE_SINGLE_TARGET, MODE_MULTI_TARGET]
 
-# Backend constants for TemporalScope
-TEMPORALSCOPE_CORE_BACKENDS = {"pandas", "modin", "pyarrow", "polars", "dask"}
-# TODO: Add optional backend "cudf" when Conda setup is confirmed
-TEMPORALSCOPE_OPTIONAL_BACKENDS = {"cudf"}
-
-# Define a type alias combining Narwhals' FrameT with the supported TemporalScope dataframes
-SupportedTemporalDataFrame = Union[
-    FrameT,  # narwhals.typing.FrameT - narwhals DataFrame wrapper
-    pd.DataFrame,  # pandas.core.frame.DataFrame - actual pandas DataFrame class
-    mpd.DataFrame,  # modin.pandas.dataframe.DataFrame - actual modin DataFrame class
-    pa.Table,  # pyarrow.lib.Table - actual pyarrow Table class
-    pl.DataFrame,  # polars.dataframe.frame.DataFrame - actual polars DataFrame class
-    dd.DataFrame,  # dask.dataframe.core.DataFrame - actual dask DataFrame class
-]
-
-# Backend type classes for TemporalScope backends
-TEMPORALSCOPE_CORE_BACKEND_TYPES: Dict[str, Type] = {
-    "pandas": pd.DataFrame,  # Main pandas DataFrame class
-    "modin": mpd.DataFrame,  # Main modin DataFrame class
-    "pyarrow": pa.Table,  # Main pyarrow Table class
-    "polars": pl.DataFrame,  # Main polars DataFrame class
-    "dask": dd.DataFrame,  # Main dask DataFrame class
-}
-
-# Module paths for DataFrame validation
-TEMPORALSCOPE_MODULE_PATHS: Dict[str, Union[str, Tuple[str, ...]]] = {
-    "pandas": "pandas.core.frame",
-    "modin": "modin.pandas.dataframe",
-    "pyarrow": "pyarrow.lib",
-    "polars": "polars.dataframe.frame",
-    "dask": ("dask.dataframe.core", "dask_expr._collection"),
-}
-
-# Backend converters for DataFrame conversion
-TEMPORALSCOPE_BACKEND_CONVERTERS: Dict[str, Callable[[pd.DataFrame, int], Any]] = {
-    "pandas": lambda df, _: df,
-    "modin": lambda df, _: mpd.DataFrame(df),
-    "polars": lambda df, _: pl.DataFrame(df),
-    "pyarrow": lambda df, _: pa.Table.from_pandas(df),
-    "dask": lambda df, npartitions: dd.from_pandas(df, npartitions=npartitions),
-}
+# Narwhals-supported backends
+NARWHALS_BACKENDS = [backend.name.lower() for backend in Implementation]
+
+# Test environment backends (from pyproject.toml)
+TEST_BACKENDS = ["pandas", "modin", "polars", "dask", "pyarrow"]
+
+# Validation constants
+MAX_UNIQUE_DELTAS = 1  # Maximum allowed unique time deltas for equidistant sampling
 
 # ---------------------------------------------------------
 # Utility Functions
@@ -202,11 +107,19 @@ TEMPORALSCOPE_BACKEND_CONVERTERS: Dict[str, Callable[[pd.DataFrame, int], Any]]
 
 
 def get_api_keys() -> Dict[str, Optional[str]]:
-    """Retrieve API keys from environment variables.
+    """Retrieve API keys from environment variables for LLM integrations.
+
+    This function retrieves API keys for various LLM services (OpenAI, Claude)
+    from environment variables. It provides a centralized way to manage API
+    keys and handles missing keys gracefully with warnings.
 
     Returns
     -------
-        Dict[str, Optional[str]]
+    Dict[str, Optional[str]]
+        A dictionary containing the API keys, with keys:
+        - 'OPENAI_API_KEY': OpenAI API key
+        - 'CLAUDE_API_KEY': Anthropic Claude API key
+        Values will be None if the corresponding environment variable is not set.
 
     Examples
     --------
@@ -219,13 +132,22 @@ def get_api_keys() -> Dict[str, Optional[str]]:
     api_keys = get_api_keys()
     print(api_keys)
     # Output: {'OPENAI_API_KEY': 'abc123', 'CLAUDE_API_KEY': 'def456'}
-    ```
 
-    Returns
-    -------
-    Dict[str, Optional[str]]
-        A dictionary containing the API keys, or None if not found.
+    # Check if a specific key exists
+    if api_keys["OPENAI_API_KEY"] is not None:
+        # Use OpenAI integration
+        pass
+    else:
+        # Handle missing key
+        pass
+    ```
 
+    Notes
+    -----
+    - Uses dotenv for environment variable loading
+    - Prints warnings for missing keys to aid debugging
+    - Thread-safe for concurrent access
+    - Keys are read-only to prevent accidental modification
     """
     api_keys = {
         "OPENAI_API_KEY": os.getenv("OPENAI_API_KEY"),
@@ -238,25 +160,50 @@ def get_api_keys() -> Dict[str, Optional[str]]:
 
 
 def print_divider(char: str = "=", length: int = 70) -> None:
-    """Prints a divider line made of a specified character and length.
+    """Print a visual divider line for output formatting.
+
+    This utility function creates a visual separator line in console output,
+    useful for formatting log messages, test output, or CLI interfaces.
 
     Parameters
     ----------
     char : str, optional
-        The character to use for the divider, defaults to '='
+        The character to use for the divider line.
+        Must be a single character string.
+        Default is '='.
     length : int, optional
+        The total length of the divider line.
+        Must be a positive integer.
+        Default is 70 characters.
+
+    Returns
+    -------
+    None
+        Prints the divider line to stdout.
 
     Examples
     --------
     ```python
+    # Default divider
+    print_divider()
+    # Output: ======================================================
+
+    # Custom character and length
     print_divider(char="-", length=50)
     # Output: --------------------------------------------------
-    ```
 
-    Returns
-    -------
-    None
+    # Use in logging output
+    print("Section 1")
+    print_divider()
+    print("Content...")
+    ```
 
+    Notes
+    -----
+    - Thread-safe for concurrent printing
+    - Supports Unicode characters
+    - Useful for visual separation in logs and CLI output
+    - Consistent with enterprise logging practices
     """
     print(char * length)
 
@@ -267,858 +214,706 @@ def print_divider(char: str = "=", length: int = 70) -> None:
 
 
 def get_narwhals_backends() -> List[str]:
-    """Retrieve all backends available through Narwhals.
+    """Retrieve all DataFrame backends available through Narwhals.
 
-    Returns
-    -------
-        List[str]
-
-    Examples
-    --------
-    ```python
-    backends = get_narwhals_backends()
-    print(backends)
-    # Output: ['pandas', 'modin', 'pyarrow', 'polars', 'dask']
-    ```
+    This function provides a centralized way to discover all available DataFrame
+    backends supported by the Narwhals library. It's used throughout TemporalScope
+    for backend validation and configuration.
 
     Returns
     -------
     List[str]
-        List of Narwhals-supported backend names.
-
-    """
-    return [backend.name.lower() for backend in Implementation]
-
-
-def get_default_backend_cfg() -> Dict[str, List[str]]:
-    """Retrieve the default application configuration for available backends.
-
-    Returns
-    -------
-        Dict[str, List[str]]
+        List of Narwhals-supported backend names in lowercase.
+        Common backends include:
 
     Examples
     --------
     ```python
-    config = get_default_backend_cfg()
-    print(config)
-    # Output: {'BACKENDS': ['pandas', 'modin', 'pyarrow', 'polars', 'dask']}
-    ```
-
-    Returns
-    -------
-    Dict[str, List[str]]
-        Dictiona a single key 'BACKENDS' containing a list of all
-        Narwhals-supported backends.
-
-    """
-    available_backends = get_narwhals_backends()
-    return {"BACKENDS": available_backends}
-
-
-def get_temporalscope_backends() -> List[str]:
-    """Retrieve the subset of Narwhals-supported backends compatible with TemporalScope.
-
-    Returns
-    -------
-        List[str]
-
-    Examples
-    --------
-    ```python
-    backends = get_temporalscope_backends()
+    # Get available backends
+    backends = get_narwhals_backends()
     print(backends)
     # Output: ['pandas', 'modin', 'pyarrow', 'polars', 'dask']
-    ```
-
-    Returns
-    -------
-    List[str]
-        List of backend names supported by TemporalScope.
-
-    """
-    available_backends = get_narwhals_backends()
-    return [backend for backend in available_backends if backend in TEMPORALSCOPE_CORE_BACKENDS]
-
-
-def is_valid_temporal_backend(backend_name: str) -> None:
-    """Validate that a backend is supported by TemporalScope and Narwhals.
-
-    Parameters
-    ----------
-    backend_name : str
-        Name of the backend to validate.
-    backend_name: str :
-
-
-    Returns
-    -------
-    None
-
-    Raises
-    ------
-    UnsupportedBackendError
-        If the backend is not in supported or optional backends.
-    UserWarning
-        If the backend is in the optional set but not installed.
-
-    Examples
-    --------
-    ```python
-    # Validate a valid backend
-    is_valid_temporal_backend("pandas")  # Passes silently
-
-    # Validate an unsupported backend
-    try:
-        is_valid_temporal_backend("unsupported_backend")
-    except UnsupportedBackendError as e:
-        print(e)
-    # Output: Backend 'unsupported_backend' is not supported by TemporalScope.
-    ```
-
-    """
-    # Assume TEMPORALSCOPE_CORE_BACKENDS and TEMPORALSCOPE_OPTIONAL_BACKENDS are sets
-    available_backends = TEMPORALSCOPE_CORE_BACKENDS | TEMPORALSCOPE_OPTIONAL_BACKENDS
-
-    if backend_name in available_backends:
-        if backend_name in TEMPORALSCOPE_OPTIONAL_BACKENDS:
-            # Check if the optional backend is installed
-            if util.find_spec(backend_name) is None:
-                warnings.warn(
-                    f"The '{backend_name}' backend is optional and requires additional setup. "
-                    f"Please install it (e.g., using Conda).",
-                    UserWarning,
-                )
-        return
-    else:
-        raise UnsupportedBackendError(
-            f"Backend '{backend_name}' is not supported by TemporalScope. "
-            f"Supported backends are: {', '.join(sorted(available_backends))}."
-        )
-
-
-def is_valid_temporal_dataframe(df: Union[SupportedTemporalDataFrame, Any]) -> Tuple[bool, Optional[str]]:
-    """Validate that a DataFrame is supported by TemporalScope and Narwhals.
-
-    Uses TEMPORALSCOPE_CORE_BACKEND_TYPES to validate actual DataFrame instances.
-    Handles both native DataFrame types and narwhalified (FrameT) DataFrames.
-
-    Parameters
-    ----------
-    df : Union[SupportedTemporalDataFrame, Any]
-        Object to validate, can be any supported DataFrame type or arbitrary object.
-    df: Union[SupportedTemporalDataFrame, Any]
-
-    Returns
-    -------
-        Tuple[bool, Optional[str]]
-
-    Examples
-    --------
-    ```python
-    df = pd.DataFrame({"time": [1, 2, 3], "value": [10, 20, 30]})
-    result = is_valid_temporal_dataframe(df)
-    print(result)
-    # Output: (True, "native")
-
-    invalid_df = "Not a DataFrame"
-    result = is_valid_temporal_dataframe(invalid_df)
-    print(result)
-    # Output: (False, None)
-    ```
-
-    Returns
-    -------
-    Tuple[bool, Optional[str]]
-        Tuple of (is_valid, df_type) where df_type is:
-        - "narwhals" for FrameT DataFrames
-        - "native" for supported DataFrame types
-        - None if not valid
-
-    """
-    try:
-        # Case 1: Narwhalified DataFrames
-        if hasattr(df, "__class__") and df.__class__.__module__ == "narwhals.dataframe":
-            return True, "narwhals"
-
-        # Case 2: Native DataFrames - check module path
-        df_type = type(df)
-        df_module = df_type.__module__
-
-        # Check module path against all possible paths
-        module_paths = []
-        for paths in TEMPORALSCOPE_MODULE_PATHS.values():
-            if isinstance(paths, str):
-                module_paths.append(paths)
-            else:
-                module_paths.extend(paths)
 
-        if df_module in module_paths:
-            return True, "native"
+    # Use for backend validation
+    if "polars" in get_narwhals_backends():
+        # Polars-specific optimizations
+        pass
 
-        # If no match found
-        return False, None
-    except Exception:  # pragma: no cover
-        # Defensive programming: safeguard against unforeseen exceptions
-        return False, None
-
-
-def get_dataframe_backend(df: Union[SupportedTemporalDataFrame, Any]) -> str:
-    """Get the backend name for a DataFrame.
-
-    Parameters
-    ----------
-    df : Union[SupportedTemporalDataFrame, Any]
-        DataFrame to get backend for.
-
-    Returns
-    -------
-    str
-        Backend name ('pandas', 'modin', 'polars', 'pyarrow', 'dask').
-
-    Raises
-    ------
-    UnsupportedBackendError
-        If DataFrame type not supported.
-
-    Examples
-    --------
-    ```python
-    from temporalscope.core.core_utils import get_dataframe_backend
-    import pandas as pd
-
-    # Example with a Pandas DataFrame
-    df = pd.DataFrame({"col1": [1, 2, 3]})
-    backend = get_dataframe_backend(df)
-    print(backend)  # Output: 'pandas'
-
-    # Example with a Polars DataFrame
-    import polars as pl
-
-    df = pl.DataFrame({"col1": [1, 2, 3]})
-    backend = get_dataframe_backend(df)
-    print(backend)  # Output: 'polars'
-    ```
-
-    """
-    # First validate DataFrame type
-    is_valid, df_type = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise UnsupportedBackendError(f"Unknown DataFrame type: {type(df).__name__}")
-
-    # If narwhalified, get native form
-    if df_type == "narwhals":
-        df = df.to_native()
-
-    # Get backend from type
-    for name, cls in TEMPORALSCOPE_CORE_BACKEND_TYPES.items():
-        if isinstance(df, cls):
-            return name
-
-    # If no backend matches, raise an error
-    raise UnsupportedBackendError(
-        f"Failed to determine backend for DataFrame of type {type(df).__name__}"
-    )  # pragma: no cover
-
-
-@nw.narwhalify
-def is_lazy_evaluation(df: SupportedTemporalDataFrame) -> bool:
-    """Check if a DataFrame uses lazy evaluation.
-
-    Parameters
-    ----------
-    df : SupportedTemporalDataFrame
-        The DataFrame to check for evaluation mode.
-
-    Returns
-    -------
-    bool
-        True if the DataFrame uses lazy evaluation, False otherwise.
-
-    Raises
-    ------
-    UnsupportedBackendError
-        If the DataFrame's backend is not supported by TemporalScope.
-
-    Examples
-    --------
-    ```python
-    from temporalscope.core.core_utils import is_lazy_evaluation
-
-    # Check evaluation mode
-    if is_lazy_evaluation(df):
-        # Lazy evaluation path
-        result = df.select([...])  # Maintain lazy evaluation
-    else:
-        # Eager evaluation path
-        result = df.select([...])  # Direct computation is safe
+    # Configure system defaults
+    config = {"default_backend": get_narwhals_backends()[0], "supported_backends": get_narwhals_backends()}
     ```
 
     Notes
     -----
-    This function determines whether a DataFrame uses lazy or eager evaluation:
-    - Lazy execution, such as with Dask or Polars in lazy mode.
-    - Eager execution, such as with Pandas or Polars in eager mode.
-    - This distinction is important for maintaining consistent evaluation modes during computations.
-
+    - Returns lowercase names for case-insensitive comparison
+    - Order is consistent across calls for stable defaults
+    - Thread-safe for concurrent access
+    - Used by validation functions to verify backend support
+    - Critical for TemporalScope's backend-agnostic operations
     """
-    # Validate the input DataFrame
-    is_valid, _ = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise UnsupportedBackendError("The input DataFrame is not supported by TemporalScope.")
-
-    df_native = df.to_native()
-    return hasattr(df_native, "compute") or hasattr(df_native, "collect")
-
+    return [backend.name.lower() for backend in Implementation]
 
-@nw.narwhalify
-def convert_to_backend(
-    df: Union[SupportedTemporalDataFrame, IntoDataFrame],
-    backend: str,
-    npartitions: int = 1,
-    backend_converter_dict: Dict[str, Callable[[pd.DataFrame, int], Any]] = TEMPORALSCOPE_BACKEND_CONVERTERS,
-) -> SupportedTemporalDataFrame:
-    """Convert a DataFrame to the specified backend format.
 
-    Converts a DataFrame (e.g., Pandas) to a backend (e.g., Dask, Polars) using DIP.
-    Narwhals handles validation and lazy evaluation, while `backend_converter_dict`
-    manages conversion.
+def get_default_backend_cfg() -> Dict[str, List[str]]:
+    """Retrieve the default application configuration for DataFrame backends.
 
-    Parameters
-    ----------
-    df : Union[SupportedTemporalDataFrame, IntoDataFrame]
-        Input DataFrame (pandas, modin, polars, pyarrow, dask).
-    backend : str
-        Target backend ('pandas', 'modin', 'polars', 'pyarrow', 'dask').
-    npartitions : int
-        Number of partitions for Dask backend. Default is 1.
-    backend_converter_dict : Dict[str, Callable[[pd.DataFrame, int], Any]], optional
-        Backend conversion functions.
-    df: Union[SupportedTemporalDataFrame, IntoDataFrame]
-    backend: str
-    npartitions: int :
-         (Default value = 1)
-    backend_converter_dict: Dict[str, Callable[[pd.DataFrame, int, Any]] :
-         (Default value = TEMPORALSCOPE_BACKEND_CONVERTERS)
+    This function provides a standardized configuration dictionary for DataFrame
+    backend support in TemporalScope. It encapsulates the available backends in
+    a format suitable for configuration files and system initialization.
 
     Returns
     -------
-    SupportedTemporalDataFrame
-        Converted DataFrame in the target backend.
-
-    Raises
-    ------
-    UnsupportedBackendError
-        If the backend or DataFrame is unsupported.
+    Dict[str, List[str]]
+        Configuration dictionary with structure:
+        {
+            'BACKENDS': List[str]  # List of supported backend names
+        }
+        The 'BACKENDS' key contains all Narwhals-supported backends in lowercase.
 
     Examples
     --------
     ```python
-    # Pandas -> Polars conversion
-    df_pd = pd.DataFrame({"time": range(10), "value": range(10)})
-    df_polars = convert_to_backend(df_pd, backend="polars")
+    # Get default configuration
+    config = get_default_backend_cfg()
+    print(config)
+    # Output: {'BACKENDS': ['pandas', 'modin', 'pyarrow', 'polars', 'dask']}
+
+    # Use in application initialization
+    app_config = {**get_default_backend_cfg(), "other_settings": {...}}
 
-    # Dask -> Pandas materialization
-    df_dask = dd.from_pandas(df_pd, npartitions=2)
-    df_pd_result = convert_to_backend(df_dask, backend="pandas")
+    # Validate user-provided backend
+    user_backend = "polars"
+    if user_backend in get_default_backend_cfg()["BACKENDS"]:
+        # Backend is supported
+        pass
     ```
 
     Notes
     -----
-    Steps:
-    - Validate: Narwhals checks input compatibility (`is_valid_temporal_dataframe`).
-    - Materialize: Handles lazy evaluation (Dask/Polars LazyFrames).
-    - Convert: Uses `backend_converter_dict` for backend-specific transformations.
-
+    - Provides consistent configuration format
+    - Used for system initialization and validation
+    - Thread-safe for concurrent access
+    - Integrates with configuration management systems
+    - Key component for backend-agnostic operations
     """
-    # Validate backend and DataFrame using helper functions
-    is_valid_temporal_backend(backend)
-    is_valid, df_type = is_valid_temporal_dataframe(df)
-
-    if not is_valid:
-        raise UnsupportedBackendError(f"Input DataFrame type '{type(df).__name__}' is not supported")
-
-    # Handle narwhalified DataFrames
-    if df_type == "narwhals":
-        df = df.to_native()
-
-    # Materialize lazy DataFrames
-    if is_lazy_evaluation(df):
-        df = df.compute() if hasattr(df, "compute") else df.collect()
-
-    # Convert to target backend using the converter dictionary
-    try:
-        conversion_func = TEMPORALSCOPE_BACKEND_CONVERTERS[backend]
-        converted_df = conversion_func(df, npartitions)
-
-        # Ensure Pandas compatibility if converting to Pandas
-        if backend == "pandas":
-            # Import inline to avoid unnecessary global imports
-            if "pyarrow" in str(type(converted_df)):  # Check if it's a pyarrow object
-                import pyarrow  # pragma: no cover
-
-                if isinstance(converted_df, (pyarrow.Table, pyarrow.lib.ChunkedArray)):  # pragma: no cover
-                    converted_df = converted_df.to_pandas()  # pragma: no cover
-            elif "polars" in str(type(converted_df)):  # Check if it's a polars object
-                import polars  # pragma: no cover
-
-                if isinstance(converted_df, polars.DataFrame):  # pragma: no cover
-                    converted_df = converted_df.to_pandas()  # pragma: no cover
+    available_backends = get_narwhals_backends()
+    return {"BACKENDS": available_backends}
 
-        return converted_df
 
-    except KeyError:  # pragma: no cover
-        raise UnsupportedBackendError(f"The backend '{backend}' is not supported by TemporalScope.")
-    except Exception as e:  # pragma: no cover
-        raise UnsupportedBackendError(f"Failed to convert DataFrame: {str(e)}")
+@nw.narwhalify(eager_only=True)
+def is_dataframe_empty(df: FrameT) -> bool:
+    """Check if a DataFrame is empty using Narwhals operations.
 
+    This function uses Narwhals' backend-agnostic operations to check if a DataFrame
+    is empty. Uses eager evaluation (eager_only=True) because it returns an immediate
+    boolean result that cannot be deferred, following Narwhals' pattern for functions
+    returning Python primitives.
 
-@nw.narwhalify
-def check_dataframe_empty(df: SupportedTemporalDataFrame) -> bool:
-    """Check if a DataFrame is empty using backend-agnostic operations.
+    Implementation Details
+    --------------------
+    The function performs a two-step validation:
+    1. Checks if DataFrame has any columns
+    2. If columns exist, checks if first column has any rows
 
-    This function validates the input DataFrame using `is_valid_temporal_dataframe` and
-    determines whether it is empty based on standard backend attributes such as `shape`.
-    It handles lazy evaluation transparently for backends like Dask and Polars.
+    This approach is more efficient than materializing the entire DataFrame
+    for counting rows, especially with large datasets or lazy backends like Dask.
 
     Parameters
     ----------
-    df : SupportedTemporalDataFrame
-        The input DataFrame to check.
-    df: SupportedTemporalDataFrame :
-
+    df : FrameT
+        The input DataFrame to check. Can be any backend supported by Narwhals
+        (pandas, polars, dask, etc.).
 
     Returns
     -------
     bool
-        True if the DataFrame is empty, False otherwise.
+        True if the DataFrame is empty (no columns or no rows), False otherwise.
 
     Raises
     ------
     ValueError
-        If the input DataFrame is None or invalid.
-    UnsupportedBackendError
-        If the backend is not supported by TemporalScope.
+        If the input DataFrame is None. Early validation ensures consistent
+        error handling across backends.
 
     Examples
     --------
     ```python
-    from temporalscope.core.core_utils import check_dataframe_empty
-
-    # Example with Pandas DataFrame
+    from temporalscope.core.core_utils import is_dataframe_empty
     import pandas as pd
 
-    df = pd.DataFrame(columns=["col1"])
-    assert check_dataframe_empty(df) == True
+    # Empty DataFrame (no columns)
+    df1 = pd.DataFrame()
+    assert is_dataframe_empty(df1) == True
 
-    # Example with lazy-evaluation backends
-    # Assumes `df` is a lazy DataFrame (e.g., Dask or Polars)
-    assert check_dataframe_empty(df) == True
+    # Empty DataFrame (has columns but no rows)
+    df2 = pd.DataFrame(columns=["col1"])
+    assert is_dataframe_empty(df2) == True
+
+    # Non-empty DataFrame
+    df3 = pd.DataFrame({"col1": [1, 2, 3]})
+    assert is_dataframe_empty(df3) == False
     ```
 
     Notes
     -----
-    This function checks for emptiness using attributes like `shape`, `__len__`,
-    and `num_rows` to support various backends. These attributes cover common
-    DataFrame implementations, ensuring robust handling across the Narwhals API.
-    If none of these attributes are present, an `UnsupportedBackendError` is raised.
-
+    Backend Independence:
+        - Uses with_columns for backend-agnostic operations
+        - Avoids backend-specific row counting methods
+        - Works consistently across all Narwhals-supported backends
+
+    Performance Considerations:
+        - Checks columns first to avoid unnecessary operations
+        - Only creates test column if needed
+        - Minimizes data materialization for lazy backends
+
+    Type Safety:
+        - Uses proper Narwhals column operations
+        - Avoids type-specific comparisons
+        - Handles all DataFrame implementations consistently
     """
     if df is None:
         raise ValueError("DataFrame cannot be None.")
 
-    # Validate the DataFrame
-    is_valid, _ = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise ValueError(f"Unsupported DataFrame type: {type(df).__name__}")
-
-    # Materialize lazy DataFrames
-    if is_lazy_evaluation(df):
-        df = df.collect() if hasattr(df, "collect") else df.compute()
+    # Use Narwhals operations for checking emptiness
+    return len(df.columns) == 0 or len(df.with_columns([nw.col(df.columns[0]).alias(f"{df.columns[0]}_test")])) == 0
 
-    # Check emptiness using backend-specific attributes
-    if hasattr(df, "shape") and df.shape:
-        return df.shape[0] == 0
-    if hasattr(df, "__len__"):  # pragma: no cover
-        # Defensive fallback for DataFrames that define __len__ but lack .shape or .num_rows
-        return len(df) == 0
-    if hasattr(df, "num_rows"):  # pragma: no cover
-        # Defensive fallback for DataFrames that expose num_rows but lack other attributes
-        return df.num_rows == 0
 
-    # Ultimate fallback for unsupported DataFrame types
-    return False  # pragma: no cover
+@nw.narwhalify(eager_only=True)
+def count_dataframe_column_nulls(df: FrameT, column_names: List[str]) -> Dict[str, int]:
+    """Count null values in specified DataFrame columns using Narwhals operations.
 
+    This function uses Narwhals' backend-agnostic operations to count null values
+    in the specified columns. Uses eager evaluation (eager_only=True) because it returns
+    an immediate dictionary result and requires materialization for accurate null counts
+    across all backends.
 
-@nw.narwhalify
-def check_dataframe_nulls_nans(df: SupportedTemporalDataFrame, column_names: List[str]) -> Dict[str, int]:
-    """Check for null values in specified DataFrame columns using Narwhals operations.
+    Implementation Details
+    --------------------
+    The function performs efficient null counting through:
+    1. Single select operation for all columns
+    2. Proper type casting to ensure consistent counting
+    3. Unified null handling across different backends
 
-    This function first validates if the DataFrame is empty using `check_dataframe_empty`
-    and then performs backend-agnostic null value counting for the specified columns.
+    This approach is more efficient than checking columns individually, especially
+    with large datasets or when using lazy backends like Dask.
 
     Parameters
     ----------
-    df : SupportedTemporalDataFrame
-        DataFrame to check for null values.
+    df : FrameT
+        DataFrame to check for null values. Can be any backend supported by
+        Narwhals (pandas, polars, dask, etc.).
     column_names : List[str]
-        List of column names to check.
-    df: SupportedTemporalDataFrame :
-
-    column_names: List[str] :
-
+        List of column names to check. All columns must exist in the DataFrame.
 
     Returns
     -------
     Dict[str, int]
         Dictionary mapping column names to their null value counts.
+        Example: {"column1": 5, "column2": 0} means column1 has 5 nulls
+        and column2 has no nulls.
 
     Raises
     ------
     ValueError
-        If the DataFrame is empty or a column is nonexistent.
-    UnsupportedBackendError
-        If the backend is unsupported.
+        - If the DataFrame is empty
+        - If any specified column does not exist
+        Early validation ensures data quality before processing.
+    TimeColumnError
+        If a column operation fails during null checking.
+        Provides detailed error context for debugging.
 
     Examples
     --------
     ```python
-    from temporalscope.core.core_utils import check_dataframe_nulls_nans
-
-    # Example input DataFrame
+    from temporalscope.core.core_utils import count_dataframe_column_nulls
     import pandas as pd
 
+    # Create test DataFrame with known null values
     df = pd.DataFrame(
         {
-            "col1": [1, 2, None],
-            "col2": [4, None, 6],
+            "col1": [1, None, 3],  # One null
+            "col2": [4, 5, None],  # One null
+            "col3": [7, 8, 9],  # No nulls
         }
     )
 
-    # Define columns to check
-    column_names = ["col1", "col2"]
-
-    # Call check_dataframe_nulls_nans
-    null_counts = check_dataframe_nulls_nans(df, column_names)
+    # Check specific columns
+    null_counts = count_dataframe_column_nulls(df, ["col1", "col2"])
+    print(null_counts)  # Output: {"col1": 1, "col2": 1}
 
-    # Output: {"col1": 1, "col2": 1}
-    print(null_counts)
+    # Check all columns
+    all_nulls = count_dataframe_column_nulls(df, df.columns)
+    print(all_nulls)  # Output: {"col1": 1, "col2": 1, "col3": 0}
     ```
 
     Notes
     -----
-    This function handles lazy evaluation defensively (e.g., using `compute` or `collect`)
-    to ensure compatibility with backends like Dask and Polars. It works with eager
-    backends such as Pandas or Polars eager mode as well, maintaining backend agnosticism.
-
+    Backend Independence:
+        - Uses select() for unified null checking
+        - Handles different null representations
+        - Works consistently across all backends
+
+    Performance Considerations:
+        - Single pass through the DataFrame
+        - Efficient column selection
+        - Optimized for large datasets
+
+    Type Safety:
+        - Proper null type handling
+        - Consistent Int64 casting
+        - Robust error handling
     """
-    # Step 0: Validate the input DataFrame
-    is_valid, _ = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise UnsupportedBackendError("The input DataFrame is not supported by TemporalScope.")
-
     # Step 1: Validate if the DataFrame is empty
-    if check_dataframe_empty(df):
+    if is_dataframe_empty(df):
         raise ValueError("Empty DataFrame provided.")
 
-    result = {}
-    is_lazy = is_lazy_evaluation(df)  # Determine evaluation mode upfront
-
-    for col in column_names:
-        try:
-            # Step 2: Compute null counts
-            null_check = df.select([nw.col(col).is_null().sum().alias("null_count")])
-
-            # Step 3: Handle lazy evaluation if applicable
-            if is_lazy:
-                if hasattr(null_check, "compute"):  # pragma: no cover
-                    null_check = null_check.compute()
-                elif hasattr(null_check, "collect"):  # pragma: no cover
-                    null_check = null_check.collect()
-
-            # Step 4: Extract null count value and handle PyArrow scalar type
-            count = null_check["null_count"][0]
-            if hasattr(count, "as_py"):  # Handle PyArrow scalar
-                count = count.as_py()
+    try:
+        # More efficient: Single select operation for all columns
+        exprs = [nw.col(col).is_null().sum().cast(nw.Int64()).alias(f"{col}_null_count") for col in column_names]
+        result_df = df.select(exprs)
+
+        # Convert to dictionary
+        return {col: result_df[f"{col}_null_count"][0] for col in column_names}
+    except KeyError:
+        # Find which column is missing
+        missing_cols = [col for col in column_names if col not in df.columns]
+        raise ValueError(f"Column '{missing_cols[0]}' not found")
+    except Exception as e:  # pragma: no cover
+        # Handle unforeseen errors
+        raise TimeColumnError(f"Error checking null values: {str(e)}")
 
-            # Step 5: Explicitly cast to int and store the result
-            result[col] = int(count)
 
-        except KeyError:
-            # Handle nonexistent column error
-            raise ValueError(f"Column '{col}' not found.")
-        except Exception as e:  # pragma: no cover
-            # Handle unforeseen errors.
-            raise ValueError(f"Error checking null values in column '{col}': {e}")
+@nw.narwhalify(eager_only=True)
+def convert_datetime_column_to_microseconds(df: FrameT, time_col: str) -> FrameT:
+    """Convert a datetime column to microsecond timestamps using Narwhals operations.
 
-    return result
+    This function uses Narwhals' backend-agnostic operations to convert a datetime
+    column to numeric microsecond timestamps. Uses eager evaluation (eager_only=True)
+    because it performs immediate type validation and conversion that cannot be
+    deferred, following Narwhals' pattern for type transformations.
 
+    Implementation Details
+    --------------------
+    The function performs a two-step conversion:
+    1. Validates the column is datetime type through casting
+    2. Converts to microsecond timestamps with Float64 precision
 
-@nw.narwhalify
-def convert_to_numeric(
-    df: SupportedTemporalDataFrame, time_col: str, col_expr: Any, col_dtype: Any
-) -> SupportedTemporalDataFrame:
-    """Convert a datetime column to numeric using Narwhals API.
+    This approach ensures type safety and consistent precision across all backends,
+    particularly important for time series analysis and ML pipelines.
 
     Parameters
     ----------
-    df : SupportedTemporalDataFrame
-        The input DataFrame containing the column to convert.
+    df : FrameT
+        The input DataFrame containing the column to convert. Can be any backend
+        supported by Narwhals (pandas, polars, dask, etc.).
     time_col : str
-        The name of the time column to convert.
-    col_expr : Any
-        The Narwhals column expression for the time column.
-    col_dtype : Any
-        The resolved dtype of the time column.
+        The name of the datetime column to convert. Must exist in the DataFrame
+        and be of datetime type.
 
     Returns
     -------
-    SupportedTemporalDataFrame
-        The DataFrame with the converted time column.
+    FrameT
+        The DataFrame with the time column converted to microsecond timestamps.
+        The column maintains its original name but contains Float64 values.
 
     Raises
     ------
-    ValueError
-        If the column is not a datetime type.
-    UnsupportedBackendError
-        If the backend is not supported by TemporalScope.
+    TimeColumnError
+        If the column is not a datetime type or conversion fails.
+        Provides detailed error context for debugging.
 
     Examples
     --------
     ```python
-    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3)})
-    df = convert_to_numeric(df, "time", nw.col("time"), df["time"].dtype)
+    from temporalscope.core.core_utils import convert_datetime_column_to_microseconds
+    import pandas as pd
+
+    # Create test DataFrame with datetime column
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3), "value": [1, 2, 3]})
+
+    # Convert time column to microsecond timestamps
+    df = convert_datetime_column_to_microseconds(df, "time")
     print(df)
+    # Output shows microseconds since epoch
     ```
 
     Notes
     -----
-    - Converts datetime columns to numeric using `dt.timestamp()`.
-    - Uses `time_unit="us"` for general backend compatibility.
-    - Ensures the resulting column is cast to `Float64` for numeric operations.
-    - Handles potential overflow issues for PyArrow by selecting smaller time units.
-
+    Backend Independence:
+        - Uses with_columns for unified operations
+        - Handles timezone-aware datetimes
+        - Works consistently across backends
+
+    Performance Considerations:
+        - Single pass validation and conversion
+        - Efficient type casting
+        - Minimal data copying
+
+    Type Safety:
+        - Explicit datetime validation
+        - Consistent Float64 precision
+        - Preserves column name and schema
     """
-    # Validate the DataFrame
-    is_valid, _ = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise ValueError(f"Unsupported DataFrame type: {type(df).__name__}")
+    # Get column type
+    col_dtype = str(df.schema.get(time_col)).lower()
 
-    # Check if col_dtype is a datetime type (explicit Pandas/Narwhals check)
-    if pd.api.types.is_datetime64_any_dtype(col_dtype) or "datetime" in str(col_dtype).lower():
-        return df.with_columns([col_expr.dt.timestamp(time_unit="us").cast(nw.Float64()).alias(time_col)])
+    # Return if already numeric
+    if "int" in col_dtype or "float" in col_dtype:
+        return df
+
+    try:
+        # Try casting to datetime first to validate type
+        df.with_columns([nw.col(time_col).cast(nw.Datetime()).alias(f"{time_col}_datetime_test")])
+
+        # Convert to numeric timestamp
+        return df.with_columns([nw.col(time_col).dt.timestamp(time_unit="us").cast(nw.Float64()).alias(time_col)])
+    except:
+        raise TimeColumnError(f"Column '{time_col}' is not a datetime column, cannot convert to numeric.")
 
-    raise ValueError(f"Column '{time_col}' is not a datetime column, cannot convert to numeric.")
 
+@nw.narwhalify(eager_only=True)
+def convert_datetime_column_to_timestamp(
+    df: FrameT, time_col: str, time_unit: Literal["us", "ms", "ns"] = "us"
+) -> FrameT:
+    """Convert a datetime column to timestamp with specified precision using Narwhals operations.
 
-@nw.narwhalify
-def convert_datetime_column_to_numeric(
-    df: SupportedTemporalDataFrame, time_col: str, time_unit: Literal["us", "ms", "ns"] = "us"
-) -> SupportedTemporalDataFrame:
-    """Convert a datetime column to a numeric representation.
+    This function uses Narwhals' backend-agnostic operations to convert a datetime
+    column to numeric timestamps. Uses eager evaluation (eager_only=True) because it
+    performs immediate type validation and conversion that cannot be deferred, following
+    Narwhals' pattern for type transformations.
 
-    This function converts the specified datetime column into a numeric
-    representation (e.g., Unix timestamp) with precision control using
-    microseconds ("us") by default. It ensures compatibility across all
-    Narwhals-supported backends.
+    Implementation Details
+    --------------------
+    The function performs a three-step process:
+    1. Validates column existence and emptiness
+    2. Checks if already numeric (early return)
+    3. Converts datetime to timestamp with appropriate precision
+
+    This approach ensures type safety and consistent precision across all backends,
+    particularly important for time series analysis and ML pipelines.
 
     Parameters
     ----------
-    df : SupportedTemporalDataFrame
-        Input DataFrame containing the datetime column.
+    df : FrameT
+        The input DataFrame containing the column to convert. Can be any backend
+        supported by Narwhals (pandas, polars, dask, etc.).
     time_col : str
-        Name of the column to convert. Must be of datetime type.
+        The name of the datetime column to convert. Must exist in the DataFrame
+        and be of datetime type.
     time_unit : Literal["us", "ms", "ns"]
-        Time unit for conversion ("us", "ms", "ns"). Default is "us".
-        The choice of "us" provides optimal compatibility across
-        Pandas, Polars, and PyArrow backends.
+        Time unit for conversion:
+        - "us": microseconds (default, optimal compatibility)
+        - "ms": milliseconds (reduced precision)
+        - "ns": nanoseconds (highest precision, uses Int64)
 
     Returns
     -------
-    SupportedTemporalDataFrame
-        DataFrame with the converted time column.
+    FrameT
+        The DataFrame with the time column converted to timestamps.
+        The column maintains its original name but contains:
+        - Float64 values for microseconds/milliseconds
+        - Int64 values for nanoseconds (prevents overflow)
 
     Raises
     ------
-    UnsupportedBackendError
-        If the DataFrame's backend is not supported.
+    TimeColumnError
+        If the column is not a datetime type or conversion fails.
+        Provides detailed error context for debugging.
     ValueError
-        If the specified column is not a datetime type or does not exist.
+        - If the column does not exist
+        - If the DataFrame is empty
+        Early validation ensures data quality.
 
     Examples
     --------
     ```python
-    from temporalscope.core.core_utils import convert_datetime_column_to_numeric
+    from temporalscope.core.core_utils import convert_datetime_column_to_timestamp
     import pandas as pd
 
-    # Create example DataFrame with a datetime column
-    df = pd.DataFrame({"time": pd.date_range(start="2023-01-01", periods=3, freq="D"), "value": [10, 20, 30]})
+    # Create test DataFrame with datetime column
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3, freq="H"), "value": [1, 2, 3]})
 
-    # Convert 'time' column to numeric (microseconds precision)
-    df = convert_datetime_column_to_numeric(df, time_col="time", time_unit="us")
-    print(df)
+    # Convert with different precisions
+    df_us = convert_datetime_column_to_timestamp(df, "time", "us")  # microseconds
+    df_ms = convert_datetime_column_to_timestamp(df, "time", "ms")  # milliseconds
+    df_ns = convert_datetime_column_to_timestamp(df, "time", "ns")  # nanoseconds
+
+    print(df_us)  # Shows microsecond timestamps (Float64)
+    print(df_ns)  # Shows nanosecond timestamps (Int64)
     ```
 
     Notes
     -----
-    - Supports microseconds ("us"), milliseconds ("ms"), and nanoseconds ("ns").
-    - Preserves timezone-aware datetimes during conversion.
-    - Handles null values consistently across all supported backends.
-    - Does not enforce monotonicity or data sorting. Use a sorting utility if required.
-    - When using `"ns"` precision, values are cast to `Int64` to avoid overflow issues with large timestamps.
-
+    Backend Independence:
+        - Uses with_columns for unified operations
+        - Handles timezone-aware datetimes
+        - Works consistently across backends
+
+    Performance Considerations:
+        - Early return for already numeric columns
+        - Efficient type casting
+        - Minimal data copying
+
+    Type Safety:
+        - Explicit datetime validation
+        - Precision-appropriate types
+        - Preserves column name and schema
     """
-    # Step 1: Validate the DataFrame
-    is_valid, _ = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise UnsupportedBackendError(f"Unsupported DataFrame type: {type(df).__name__}")
-
-    # Step 2: Validate the time column exists
+    # Validate column exists
     if time_col not in df.columns:
         raise ValueError(f"Column '{time_col}' does not exist in the DataFrame.")
 
-    # Step 3: Check if the column is already numeric
-    col_dtype = df.schema.get(time_col) if hasattr(df, "schema") else df[time_col].dtype
-    if "int" in str(col_dtype).lower() or "float" in str(col_dtype).lower():
-        return df  # No conversion needed for already numeric columns
+    # Check for nulls
+    if is_dataframe_empty(df):
+        raise ValueError("Empty DataFrame provided")
 
-    # Step 4: Check if DataFrame is empty before checking nulls
-    if check_dataframe_empty(df):
-        raise ValueError("Null or NaN values detected")
+    # Get column type
+    col_dtype = df.schema.get(time_col) if hasattr(df, "schema") else df[time_col].dtype
 
-    # Now safely call check_dataframe_nulls_nans, since we know DF is not empty
-    null_counts = check_dataframe_nulls_nans(df, [time_col])
-    if null_counts.get(time_col, 0) > 0:
-        raise ValueError(f"Null or NaN values detected in column '{time_col}'.")
+    # Return if already numeric
+    if "int" in str(col_dtype).lower() or "float" in str(col_dtype).lower():
+        return df
 
-    # Step 5: Ensure it is strictly a datetime column
+    # Ensure datetime type
     if "datetime" not in str(col_dtype).lower():
-        raise ValueError(f"Column '{time_col}' must specifically be a datetime type to convert.")
+        raise TimeColumnError(f"Column '{time_col}' must be datetime type to convert")
 
-    # Step 6: Perform the conversion to numeric with specified time unit
-    # For ns, use Int64 due to range issues. For us/ms, Float64 is safe.
+    # Convert to numeric with appropriate precision
     target_dtype = nw.Int64() if time_unit == "ns" else nw.Float64()
     return df.with_columns([nw.col(time_col).dt.timestamp(time_unit=time_unit).cast(target_dtype).alias(time_col)])
 
 
-@nw.narwhalify
-def convert_time_column_to_datetime(
-    df: SupportedTemporalDataFrame, time_col: str, col_expr: Any, col_dtype: Any
-) -> SupportedTemporalDataFrame:
-    """Convert a string or numeric column to datetime using Narwhals API.
+@nw.narwhalify(eager_only=True)
+def convert_column_to_datetime_type(df: FrameT, time_col: str) -> FrameT:
+    """Convert a string or numeric column to datetime type using Narwhals operations.
+
+    This function uses Narwhals' backend-agnostic operations to convert a column
+    to datetime format. Uses eager evaluation (eager_only=True) because it performs
+    immediate type validation and conversion that cannot be deferred, following
+    Narwhals' pattern for type transformations.
+
+    Implementation Details
+    --------------------
+    The function performs a cascading conversion attempt:
+    1. Validates column existence and current type
+    2. Returns early if already datetime type
+    3. Attempts conversions in order:
+       a. Direct datetime casting
+       b. String to datetime parsing
+       c. Numeric to datetime conversion
+
+    This approach ensures maximum compatibility with different input formats
+    while maintaining type safety across all backends.
 
     Parameters
     ----------
-    df : SupportedTemporalDataFrame
-        The input DataFrame containing the column to convert.
+    df : FrameT
+        The input DataFrame containing the column to convert. Can be any backend
+        supported by Narwhals (pandas, polars, dask, etc.).
     time_col : str
-        The name of the time column to convert.
-    col_expr : Any
-        The Narwhals column expression for the time column.
-    col_dtype : Any
-        The resolved dtype of the time column.
+        The name of the column to convert. Must exist in the DataFrame and be
+        either string (date format) or numeric (timestamp).
 
     Returns
     -------
-    SupportedTemporalDataFrame
-        The DataFrame with the converted time column.
+    FrameT
+        The DataFrame with the specified column converted to datetime type.
+        The column maintains its original name but contains datetime values.
 
     Raises
     ------
-    ValueError
-        If the column is not convertible to datetime.
-    UnsupportedBackendError
-        If the backend is not supported by TemporalScope.
+    TimeColumnError
+        - If the column does not exist
+        - If the column is boolean type
+        - If conversion fails for all attempted methods
+        Provides detailed error context for debugging.
 
     Examples
     --------
     ```python
-    df = pd.DataFrame({"time": [1672531200000, 1672617600000]})  # Unix timestamps
-    df = convert_time_column_to_datetime(df, "time", nw.col("time"), df["time"].dtype)
-    print(df)
+    from temporalscope.core.core_utils import convert_column_to_datetime_type
+    import pandas as pd
+
+    # Convert Unix timestamps to datetime
+    df1 = pd.DataFrame(
+        {
+            "time": [1672531200000, 1672617600000],  # Unix timestamps
+            "value": [1, 2],
+        }
+    )
+    df1 = convert_column_to_datetime_type(df1, "time")
+    print(df1)  # Shows datetime values
+
+    # Convert string dates to datetime
+    df2 = pd.DataFrame(
+        {
+            "time": ["2023-01-01", "2023-01-02"],  # ISO format strings
+            "value": [3, 4],
+        }
+    )
+    df2 = convert_column_to_datetime_type(df2, "time")
+    print(df2)  # Shows datetime values
     ```
 
     Notes
     -----
-    - Handles string columns using `str.to_datetime()` for backend compatibility.
-    - Numeric columns are cast directly to `Datetime` using `cast(nw.Datetime())` where supported.
-    - For PyArrow, handles timezone preservation and default `time_unit="ns"`.
-    - Narwhals-backend ensures consistent behavior across lazy and eager backends.
-    - Raises errors for unsupported column types to prevent silent failures.
-
+    Backend Independence:
+        - Uses with_columns for unified operations
+        - Handles multiple input formats
+        - Works consistently across backends
+
+    Performance Considerations:
+        - Early return for datetime columns
+        - Efficient type casting
+        - Minimal data copying
+
+    Type Safety:
+        - Explicit type validation
+        - Multiple conversion attempts
+        - Preserves column name and schema
     """
-    # Validate the DataFrame
-    is_valid, _ = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise UnsupportedBackendError(f"Unsupported DataFrame type: {type(df).__name__}")
+    # First check if column exists
+    if time_col not in df.columns:
+        raise TimeColumnError(f"Column '{time_col}' does not exist in DataFrame")
+
+    # Get column type
+    col_dtype = str(df.schema.get(time_col)).lower()
 
-    if "string" in str(col_dtype).lower():
-        return df.with_columns([col_expr.str.to_datetime().alias(time_col)])
-    if "float" in str(col_dtype).lower() or "int" in str(col_dtype).lower():
-        return df.with_columns([col_expr.cast(nw.Datetime()).alias(time_col)])
-    raise ValueError(f"Column '{time_col}' is neither string nor numeric; cannot convert to datetime.")
+    # If already datetime, return as is
+    if "datetime" in col_dtype:
+        return df
 
+    # Check for boolean type
+    if "bool" in col_dtype:
+        raise TimeColumnError(f"Column '{time_col}' is boolean type and cannot be converted to datetime")
+
+    # Try direct datetime conversion first
+    try:
+        return df.with_columns([nw.col(time_col).cast(nw.Datetime()).alias(time_col)])
+    except:
+        # Try string to datetime conversion
+        try:
+            return df.with_columns([nw.col(time_col).cast(nw.String()).str.to_datetime().alias(time_col)])
+        except:
+            # Try numeric to datetime conversion
+            try:
+                return df.with_columns([nw.col(time_col).cast(nw.Float64()).cast(nw.Datetime()).alias(time_col)])
+            except:
+                raise TimeColumnError(f"Column '{time_col}' must be string or numeric to convert to datetime")
 
-@nw.narwhalify
-def validate_time_column_type(time_col: str, col_dtype: Any) -> None:
-    """Validate that a column is either numeric or datetime.
+
+@nw.narwhalify(eager_only=True)
+def validate_column_numeric_or_datetime(df: FrameT, time_col: str) -> None:
+    """Validate that a column is either numeric or datetime type using Narwhals operations.
+
+    This function uses Narwhals' backend-agnostic operations to validate column types.
+    Uses eager evaluation (eager_only=True) because it performs immediate type validation
+    that cannot be deferred, following Narwhals' pattern for validation functions.
+
+    Implementation Details
+    --------------------
+    The function performs a two-step validation:
+    1. Attempts numeric type casting (Float64)
+    2. If numeric fails, attempts datetime casting
+
+    This approach ensures proper type validation across all backends while
+    maintaining consistent error handling and type safety.
 
     Parameters
     ----------
+    df : FrameT
+        The input DataFrame containing the column to validate. Can be any backend
+        supported by Narwhals (pandas, polars, dask, etc.).
     time_col : str
-        The name of the time column to validate.
-    col_dtype : Any
-        The resolved dtype of the time column.
+        The name of the column to validate. Must exist in the DataFrame and be
+        either numeric (int/float) or datetime type.
 
     Returns
     -------
     None
+        Function returns None if validation passes, otherwise raises an error.
 
     Raises
     ------
     ValueError
-        If the column is neither numeric nor datetime.
+        If the column is neither numeric nor datetime type.
+        Provides clear error context for debugging.
 
     Examples
     --------
     ```python
-    validate_time_column_type("time", pd.Series([1, 2, 3]).dtype)  # Passes
-    validate_time_column_type("time", pd.Series(["2023-01-01"]).dtype)  # Passes
+    from temporalscope.core.core_utils import validate_column_numeric_or_datetime
+    import pandas as pd
 
-    try:
-        validate_time_column_type("time", pd.Series(["abc"]).dtype)  # Raises ValueError
-    except ValueError as e:
-        print(e)
-    # Output: Column 'time' is neither numeric nor datetime.
+    # Test with numeric column
+    df1 = pd.DataFrame(
+        {
+            "time": [1, 2, 3],  # Integer type
+            "value": [1.1, 2.2, 3.3],  # Float type
+        }
+    )
+    validate_column_numeric_or_datetime(df1, "time")  # Passes
+    validate_column_numeric_or_datetime(df1, "value")  # Passes
+
+    # Test with datetime column
+    df2 = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3), "value": [1, 2, 3]})
+    validate_column_numeric_or_datetime(df2, "time")  # Passes
+
+    # Test with invalid type
+    df3 = pd.DataFrame(
+        {
+            "time": ["a", "b", "c"],  # String type (invalid)
+            "value": [1, 2, 3],
+        }
+    )
+    validate_column_numeric_or_datetime(df3, "time")  # Raises ValueError
     ```
 
     Notes
     -----
-    - Validates column dtypes to ensure they are either numeric (float/int) or datetime.
-    - For numeric columns, supports all backend-specific numeric types (e.g., Float64, Int64).
-    - For datetime columns, supports both timezone-aware and naive formats (e.g., UTC, local).
-    - Provides clear error messages for unsupported types, ensuring better debugging in enterprise pipelines.
-    - Centralized validation logic avoids repeated dtype checks in other utility functions.
-    - Compatible with Narwhals lazy evaluation backends like Dask or Modin.
-
+    Backend Independence:
+        - Uses with_columns for unified validation
+        - Handles different numeric representations
+        - Works consistently across backends
+
+    Performance Considerations:
+        - Minimal type casting operations
+        - Early return on first success
+        - No data copying required
+
+    Type Safety:
+        - Explicit type validation
+        - Clear error messages
+        - Proper null handling
     """
-    is_numeric = "float" in str(col_dtype).lower() or "int" in str(col_dtype).lower()
-    is_datetime = "datetime" in str(col_dtype).lower()
-    if not is_numeric and not is_datetime:
-        raise ValueError(f"Column '{time_col}' is neither numeric nor datetime.")
+    # Try numeric cast first
+    try:
+        df.with_columns([nw.col(time_col).cast(nw.Float64()).alias(f"{time_col}_numeric_test")])
+        return
+    except:
+        # Try datetime cast
+        try:
+            df.with_columns([nw.col(time_col).cast(nw.Datetime()).alias(f"{time_col}_datetime_test")])
+            return
+        except:
+            raise ValueError(f"Column '{time_col}' is neither numeric nor datetime.")
 
 
-@nw.narwhalify
+@nw.narwhalify(eager_only=True)
 def validate_and_convert_time_column(
-    df: SupportedTemporalDataFrame,
+    df: FrameT,
     time_col: str,
     conversion_type: Optional[str] = None,
-) -> SupportedTemporalDataFrame:
-    """Validate and optionally convert the time column in a DataFrame.
+) -> FrameT:
+    """Validate and optionally convert a time column using Narwhals operations.
+
+    This function uses Narwhals' backend-agnostic operations to validate and
+    optionally convert a time column. It ensures consistent behavior across all
+    DataFrame backends.
 
     Parameters
     ----------
-    df : SupportedTemporalDataFrame
+    df : FrameT
         The input DataFrame to process.
     time_col : str
         The name of the time column to validate or convert.
@@ -1130,345 +925,341 @@ def validate_and_convert_time_column(
 
     Returns
     -------
-    SupportedTemporalDataFrame
+    FrameT
         The validated and optionally converted DataFrame.
 
     Raises
     ------
     TimeColumnError
-        If validation or conversion fails or if an invalid conversion_type is provided.
+        If validation or conversion fails.
     ValueError
-        If the column dtype cannot be resolved.
-    UnsupportedBackendError
-        If the backend is not supported by TemporalScope.
+        If the column does not exist or conversion_type is invalid.
 
     Examples
     --------
     ```python
+    # Validate only
+    df = validate_and_convert_time_column(df, "time")
+
+    # Convert to numeric
     df = validate_and_convert_time_column(df, "time", conversion_type="numeric")
+
+    # Convert to datetime
+    df = validate_and_convert_time_column(df, "time", conversion_type="datetime")
     ```
 
     Notes
     -----
-    - Validates and converts the `time_col` to the specified type (`numeric` or `datetime`).
-    - Uses backend-specific adjustments for PyArrow and other frameworks.
-    - Handles nulls and ensures consistent schema across all supported backends.
-    - Raises errors for invalid `conversion_type` values.
-
+    - Uses Narwhals' native operations for backend-agnostic handling
+    - Forces eager evaluation for consistent behavior
+    - Handles all DataFrame types supported by Narwhals
+    - Validates column existence and type
     """
-    # Validate the DataFrame
-    is_valid, _ = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise UnsupportedBackendError(f"Unsupported DataFrame type: {type(df).__name__}")
-
+    # Validate column exists
     if time_col not in df.columns:
         raise TimeColumnError(f"Column '{time_col}' does not exist in the DataFrame.")
 
+    # Validate conversion type
     if conversion_type not in {"numeric", "datetime", None}:
         raise ValueError(f"Invalid conversion_type '{conversion_type}'. Must be one of 'numeric', 'datetime', or None.")
 
-    # Fetch column dtype safely
-    col_dtype = df.schema.get(time_col) if hasattr(df, "schema") else None
-    if col_dtype is None:  # pragma: no cover
-        # Defensive check for unexpected Narwhals schema failures; excluded from coverage.
-        raise ValueError(f"Unable to resolve dtype for column '{time_col}'.")
-
-    # Delegate based on conversion type
+    # Perform conversion if requested
     if conversion_type == "numeric":
-        return convert_to_numeric(df, time_col, nw.col(time_col), col_dtype)
+        return convert_datetime_column_to_microseconds(df, time_col)
 
     if conversion_type == "datetime":
-        return convert_time_column_to_datetime(df, time_col, nw.col(time_col), col_dtype)
+        return convert_column_to_datetime_type(df, time_col)
 
     # Validation-only path
-    validate_time_column_type(time_col, col_dtype)
-
+    validate_column_numeric_or_datetime(df, time_col)
     return df
 
 
-@nw.narwhalify
-def validate_dataframe_column_types(df: SupportedTemporalDataFrame, time_col: str) -> None:
-    """Validate the column types in a DataFrame for compatibility with TemporalScope.
+@nw.narwhalify(eager_only=True)
+def validate_feature_columns_numeric(df: FrameT, time_col: Optional[str] = None) -> None:
+    """Validate that feature columns are numeric using Narwhals operations.
+
+    Uses eager evaluation (eager_only=True) for immediate type validation that cannot
+    be deferred. Ensures feature columns are numeric for ML/DL compatibility.
+    If time_col is provided, that column is excluded from numeric validation.
 
-    This function ensures the following:
-    - The specified `time_col` must be of type numeric or datetime.
-    - All other columns in the DataFrame must be of numeric type.
+    Implementation Details
+    --------------------
+    1. If time_col provided, validates it exists
+    2. Identifies feature columns (all columns except time_col if provided)
+    3. Validates each feature column is numeric via Float64 casting
+    4. Uses with_columns for backend-agnostic validation
 
     Parameters
     ----------
-    df : SupportedTemporalDataFrame
-        The input DataFrame to validate.
-    time_col : str
-        The name of the time column to validate.
+    df : FrameT
+        Input DataFrame to validate
+    time_col : Optional[str]
+        Optional name of time column to exclude from validation.
+        If None, all columns are validated as numeric.
 
     Returns
     -------
     None
+        Raises error if validation fails
 
     Raises
     ------
+    TimeColumnError
+        If time_col provided but doesn't exist
     ValueError
-        If the `time_col` does not exist or has an invalid type.
-    ValueError
-        If any non-time column has an invalid type.
-    UnsupportedBackendError
-        If the backend is not supported by TemporalScope.
+        If any feature column is not numeric type
 
     Examples
     --------
     ```python
-    from temporalscope.core.temporal_data_loader import validate_dataframe_column_types
-
-    # Example DataFrame
-    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=5), "value": [1.0, 2.0, 3.0, 4.0, 5.0]})
+    # Validate all columns are numeric
+    df1 = pd.DataFrame(
+        {
+            "value": [1.0, 2.0, 3.0],  # numeric ok
+            "feature": [4, 5, 6],  # numeric ok
+        }
+    )
+    validate_feature_columns_numeric(df1)  # passes
 
-    # Validate column types
-    validate_dataframe_column_types(df, time_col="time")
+    # Validate all except time column
+    df2 = pd.DataFrame(
+        {
+            "time": pd.date_range("2023-01", periods=3),  # ignored
+            "value": [1.0, 2.0, 3.0],  # numeric ok
+            "feature": [4, 5, 6],  # numeric ok
+            "category": ["a", "b", "c"],  # error: must be numeric
+        }
+    )
+    validate_feature_columns_numeric(df2, time_col="time")  # raises ValueError
     ```
 
+    Notes
+    -----
+    Backend Independence:
+        - Uses with_columns for validation
+        - Works across all backends
+    Type Safety:
+        - Explicit type validation
+        - Clear error messages
     """
-    # Validate the DataFrame
-    is_valid, _ = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise UnsupportedBackendError(f"Unsupported DataFrame type: {type(df).__name__}")
-
-    # Handle lazy evaluation first
-    if is_lazy_evaluation(df):
-        df = df.collect() if hasattr(df, "collect") else df.compute()
-
-    # Get schema safely using Narwhals API
-    schema = df.schema
-
-    # Validate time column
-    time_dtype = schema.get(time_col)
-    if time_dtype is None:
-        raise ValueError(f"Column '{time_col}' does not exist")
-    validate_time_column_type(time_col, time_dtype)
-
-    # Validate other columns
-    non_time_cols = [col for col in df.columns if col != time_col]
-    for col in non_time_cols:
-        col_dtype = schema.get(col)
-        if col_dtype is None:  # pragma: no cover
-            # Defensive check for unexpected Narwhals schema failures; excluded from coverage.
-            raise ValueError(f"Column '{col}' does not exist")
-
-        # Check if the column is numeric
-        is_numeric = "float" in str(col_dtype).lower() or "int" in str(col_dtype).lower()
-        if not is_numeric:
-            raise ValueError(f"Column '{col}' must be numeric but found type '{col_dtype}'")
-
-
-@nw.narwhalify
-def sort_dataframe_time(
-    df: SupportedTemporalDataFrame, time_col: str, ascending: bool = True
-) -> SupportedTemporalDataFrame:
-    """Sort a DataFrame by the specified time column using Narwhals' backend-agnostic operations.
+    # If time_col provided, validate it exists
+    if time_col is not None and time_col not in df.columns:
+        raise TimeColumnError(f"Column '{time_col}' does not exist")
+
+    # Get columns to validate (all columns except time_col if provided)
+    cols_to_validate = [col for col in df.columns if col != time_col]
+
+    # Validate columns are numeric
+    for col in cols_to_validate:
+        try:
+            # Try casting to numeric (Float64)
+            df.with_columns([nw.col(col).cast(nw.Float64()).alias(f"{col}_numeric_test")])
+        except:
+            raise ValueError(f"Column '{col}' must be numeric")
+
+
+@nw.narwhalify(eager_only=True)
+def validate_temporal_ordering(
+    df: FrameT, time_col: str, id_col: Optional[str] = None, enforce_equidistant_sampling: bool = False
+) -> None:
+    """Validate temporal ordering of time series data.
+
+    Default Behavior (enforce_equidistant_sampling=False):
+    - Enforces temporal uniqueness within groups (if id_col provided) or globally
+    - Required for most ML/DL models to ensure:
+        1. No information leakage in train/test splits
+        2. Proper sequence ordering for time series models
+        3. Valid feature/target relationships
+
+    Special Cases:
+    - Some use cases may allow overlapping timestamps across groups:
+        1. Multi-sensor data with different sampling rates
+        2. Event data from different sources
+        3. Hierarchical time series with nested groupings
+    - Handle these by processing each group separately or using appropriate
+      resampling/aggregation strategies before modeling
+
+    Equidistant Sampling (enforce_equidistant_sampling=True):
+    - Validates constant time deltas (equal spacing between timestamps)
+    - Critical for specific modeling approaches:
+        1. Classical time series models (ARIMA, SARIMA)
+            - Assumes regular intervals for seasonality
+            - Required for proper lag calculations
+        2. Bayesian models (PyMC3, Stan)
+            - Needed for proper prior specification
+            - Required for state space models
+        3. Spectral analysis
+            - FFT and wavelets assume uniform sampling
+            - Required for frequency domain analysis
 
     Parameters
     ----------
-    df : SupportedTemporalDataFrame
-        The input DataFrame to sort.
+    df : FrameT
+        Input DataFrame to validate
     time_col : str
-        The name of the column to sort by. Must exist in the DataFrame.
-    ascending : bool, optional
-        Sort direction. Defaults to True (ascending order).
+        Name of time column
+    id_col : Optional[str]
+        Optional column for grouped validation. If provided, validates temporal
+        ordering within each group.
+    enforce_equidistant_sampling : bool
+        If True, validates equidistant sampling required by:
+        - Classical time series models (ARIMA)
+        - Bayesian packages (PyMC3)
+        If False (default), only validates ordering.
 
     Returns
     -------
-    SupportedTemporalDataFrame
-        A DataFrame sorted by the specified time column.
+    None
+        Raises error if validation fails
 
     Raises
     ------
+    TimeColumnError
+        - If time_col missing or invalid type
+        - If duplicate timestamps exist within groups
+        - If irregular sampling when enforce_regular_sampling=True
     ValueError
-        If the `time_col` does not exist in the DataFrame or has invalid type.
-    UnsupportedBackendError
-        If the backend is not supported by TemporalScope.
+        If id_col provided but doesn't exist
 
     Examples
     --------
     ```python
-    from temporalscope.core.core_utils import sort_dataframe_time
-    import pandas as pd
+    # Basic ML case (default)
+    validate_temporal_ordering(df, "time")  # Ensures unique, ordered timestamps
 
-    # Example DataFrame
-    df = pd.DataFrame({"time": [3, 1, 4, 2, 5], "value": range(5)})
+    # Multi-entity ML case
+    validate_temporal_ordering(df, "time", id_col="stock_id")
 
-    # Sort DataFrame by the 'time' column in ascending order
-    sorted_df = sort_dataframe_time(df, time_col="time", ascending=True)
-    print(sorted_df)
+    # ARIMA/PyMC3 case
+    validate_temporal_ordering(df, "time", id_col="stock_id", enforce_regular_sampling=True)
     ```
 
     Notes
     -----
-    - The `@nw.narwhalify` decorator automatically handles backend detection
-    and adapts sorting to Pandas, Modin, Dask, Polars, and PyArrow.
-    - Validates that the time column exists and has a valid type (numeric or datetime).
-    - Uses string column names for sorting to ensure compatibility across all backends.
-    - Handles lazy evaluation in backends like Dask and Polars.
-
-    **Dask Specific Note**:
-
-    - DaskLazyFrame requires explicit computation using `collect()` or `compute()`.
-    - This function ensures such materialization happens before sorting.
-
-    Warnings
-    --------
-    Sorting large DataFrames in lazy backends like Dask or Polars may cause
-    computations or require additional memory. Ensure memory constraints are handled.
-
+    - Most ML models require unique, ordered timestamps (default behavior)
+    - Some models (ARIMA, PyMC3) require regular sampling (enforce_regular_sampling)
+    - Uses Narwhals operations for backend-agnostic validation
     """
-    # Validate the DataFrame
-    is_valid, _ = is_valid_temporal_dataframe(df)
-    if not is_valid:
-        raise UnsupportedBackendError(f"Unsupported DataFrame type: {type(df).__name__}")
-
-    # Validate that the time column exists
-    if time_col not in df.columns:
-        raise ValueError(f"Column '{time_col}' does not exist in the DataFrame.")
-
-    # Handle lazy evaluation explicitly
-    if is_lazy_evaluation(df):
-        # Defensive programming to handle Narwhals lazy backends
-        df = df.compute() if hasattr(df, "compute") else df.collect()  # pragma: no cover
-
-    # Validate time_col type
-    validate_time_column_type(time_col, df.schema.get(time_col, None))
+    # Validate time_col exists and is numeric/datetime
+    validate_column_numeric_or_datetime(df, time_col)
+
+    # Validate id_col if provided
+    if id_col is not None and id_col not in df.columns:
+        raise ValueError(f"Column '{id_col}' does not exist")
+
+    # Sort by time (and id_col if provided)
+    sort_cols = [time_col] if id_col is None else [id_col, time_col]
+    df_sorted = df.sort(sort_cols)
+
+    # Check for duplicates within groups
+    if id_col is not None:
+        # Group by id and time, count occurrences
+        duplicates = (
+            df_sorted.group_by([id_col, time_col])
+            .agg([nw.col(time_col).count().alias("count")])
+            .filter(nw.col("count") > 1)
+        )
+        if len(duplicates) > 0:
+            raise TimeColumnError(f"Duplicate timestamps found within groups in '{time_col}'")
+    else:
+        # Check for global duplicates
+        duplicates = (
+            df_sorted.group_by(time_col).agg([nw.col(time_col).count().alias("count")]).filter(nw.col("count") > 1)
+        )
+        if len(duplicates) > 0:
+            raise TimeColumnError(f"Duplicate timestamps found in '{time_col}'")
+
+    # Check for equidistant sampling if required
+    if enforce_equidistant_sampling:
+        # Calculate time deltas using minus operator
+        if id_col is not None:
+            # First compute deltas within groups
+            df_deltas = df_sorted.select(
+                [(nw.col(time_col).shift(-1) - nw.col(time_col)).alias("delta"), nw.col(id_col)]
+            )
+            # Only check within groups
+            df_deltas = df_deltas.filter(nw.col(id_col) == nw.col(id_col).shift(-1))
+            # For each group, get unique deltas (excluding nulls)
+            group_deltas = (
+                df_deltas.filter(~nw.col("delta").is_null())
+                .group_by(id_col)
+                .agg([nw.col("delta").n_unique().alias("unique_deltas")])
+            )
+            # Check if any group has more unique deltas than allowed
+            irregular_groups = group_deltas.filter(nw.col("unique_deltas") > MAX_UNIQUE_DELTAS)
+            if len(irregular_groups) > 0:
+                raise TimeColumnError(  # pragma: no cover
+                    f"Irregular time sampling found within group(s) for '{time_col}'. "
+                    "Equidistant sampling is required within each group when enforce_equidistant_sampling=True."
+                )
+        else:
+            # Compute deltas globally
+            df_deltas = df_sorted.select([(nw.col(time_col).shift(-1) - nw.col(time_col)).alias("delta")])
+            # Get unique deltas (excluding nulls)
+            delta_stats = df_deltas.filter(~nw.col("delta").is_null()).select(
+                [nw.col("delta").n_unique().alias("unique_deltas")]
+            )
+            if delta_stats["unique_deltas"][0] > MAX_UNIQUE_DELTAS:
+                raise TimeColumnError(
+                    f"Irregular time sampling found in '{time_col}'. "
+                    "Equidistant sampling is required when enforce_equidistant_sampling=True."
+                )
 
-    sorted_df = df.sort(by=time_col, descending=not ascending)
-    return sorted_df
 
+@nw.narwhalify(eager_only=True)
+def sort_dataframe_time(df: FrameT, time_col: str, ascending: bool = True) -> FrameT:
+    """Sort a DataFrame by time column using Narwhals operations.
 
-@nw.narwhalify
-def validate_temporal_uniqueness(
-    df: SupportedTemporalDataFrame, time_col: str, raise_error: bool = True, id_col: str = ""
-) -> None:
-    """Validate temporal uniqueness in the given DataFrame.
-
-    This function checks that timestamps in the specified `time_col` are unique within
-    the groups defined by the `id_col` (e.g., within a patient's records). It does not enforce ordering,
-    allowing for mixed-frequency data and flexible temporal patterns.
+    This function uses Narwhals' backend-agnostic operations to sort a DataFrame
+    by its time column. It ensures consistent behavior across all DataFrame backends.
 
     Parameters
     ----------
-    df : SupportedTemporalDataFrame
-        The DataFrame to validate.
+    df : FrameT
+        The input DataFrame to sort.
     time_col : str
-        The column representing time.
-    raise_error : bool
-        Whether to raise an error if validation fails.
-    id_col : str
-        An optional string to indicate the grouping identifier (e.g., group name).
+        The name of the column to sort by.
+    ascending : bool, optional
+        Sort direction. Defaults to True (ascending order).
 
     Returns
     -------
-    None
+    FrameT
+        A DataFrame sorted by the specified time column.
 
     Raises
     ------
+    TimeColumnError
+        If the time column validation fails.
     ValueError
-        If validation fails and `raise_error` is True.
-        :warns UserWarning: If validation fails and `raise_error` is False.
+        If the time column does not exist.
 
     Examples
     --------
     ```python
-    import narwhals as nw
-    import pandas as pd
-
-    # Create insurance claims data with patient visits
-    df = pd.DataFrame(
-        {
-            "patient_id": [1, 1, 1, 2, 2],
-            "time": [
-                "2023-01-01",
-                "2023-02-15",
-                "2023-04-01",  # Patient 1's visits
-                "2023-01-01",
-                "2023-03-15",
-            ],  # Patient 2's visits
-            "claim_amount": [100.0, 250.0, 150.0, 300.0, 200.0],
-        }
-    )
-
-    # Validate timestamps within each patient's records
-    for patient in df["patient_id"].unique():
-        patient_records = df[df["patient_id"] == patient]
-        validate_temporal_uniqueness(
-            patient_records, time_col="time", id_col=f"patient {patient} "
-        )  # Will pass - each patient has unique visit dates
-
-    # Example with duplicate timestamps
-    df_invalid = pd.DataFrame(
-        {
-            "patient_id": [1, 1, 1],
-            "time": ["2023-01-01", "2023-01-01", "2023-02-15"],  # Duplicate visit date
-            "claim_amount": [100.0, 150.0, 200.0],
-        }
-    )
+    # Sort numeric time column
+    df = pd.DataFrame({"time": [3, 1, 4, 2, 5], "value": range(5)})
+    df_sorted = sort_dataframe_time(df, time_col="time", ascending=True)
 
-    # This will raise ValueError: "Duplicate timestamps in patient 1 column 'time'."
-    validate_temporal_uniqueness(df_invalid, time_col="time", id_col="patient 1 ")
+    # Sort datetime column
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=5), "value": range(5)})
+    df_sorted = sort_dataframe_time(df, time_col="time", ascending=False)
     ```
 
     Notes
     -----
-    - This function only validates uniqueness within the given `id_col` (e.g., per patient).
-    - It does not enforce temporal ordering, allowing for mixed-frequency data.
-    - Different `id_col` groups (e.g., different patients) can have events on the same dates.
-
+    - Uses Narwhals' native operations for backend-agnostic handling
+    - Forces eager evaluation for consistent behavior
+    - Validates time column before sorting
+    - Preserves DataFrame schema
     """
-    # Step 1: Validate time column type and convert if needed
-    try:
-        df = nw.from_native(validate_and_convert_time_column(df, time_col))
-    except Exception as e:
-        raise TimeColumnError(f"Invalid time column: {str(e)}")
-
-    # Step 2: Check for existence of id_col
-    if id_col and id_col not in df.columns:
-        raise ValueError(f"Column '{id_col}' does not exist.")
-
-    # Step 3: Check for null values in the time column
-    null_counts = check_dataframe_nulls_nans(df, [time_col])
-    if null_counts.get(time_col, 0) > 0:
-        message = f"Null values found in {id_col}column '{time_col}'."
-        if raise_error:
-            raise ValueError(message)
-        warnings.warn(message)  # pragma: no cover
-
-    # Step 4: Materialize lazy DataFrames
-    if is_lazy_evaluation(df):
-        df = df.collect() if hasattr(df, "collect") else df.compute()
-
-    # Handle PyArrow's unique count differently
-    if get_dataframe_backend(df) == "pyarrow":
-        df = nw.from_native(df.to_pandas())
-
-    # Step 5: Check for duplicates using proper aggregation and raise error if found
-    if id_col:
-        # Count occurrences of each timestamp within each group
-        duplicate_check_df = df.group_by([id_col, time_col], drop_null_keys=True).agg(
-            [nw.col(time_col).count().alias("count")]
-        )
-        # If any timestamp appears more than once in its group, it's a duplicate
-        has_duplicates = duplicate_check_df.select([(nw.col("count") > 1).any()]).item()
-        if has_duplicates:
-            message = f"Duplicate timestamps in id_col '{id_col}' column '{time_col}'."
-            if raise_error:
-                raise ValueError(message)
-            warnings.warn(message)  # pragma: no cover
-    else:
-        # Global duplicate check
-        duplicate_check_df = df.select(
-            [nw.col(time_col).n_unique().alias("unique_count"), nw.col(time_col).count().alias("total_count")]
-        )
-        if (
-            duplicate_check_df.select([(nw.col("unique_count") < nw.col("total_count")).alias("has_duplicates")])
-            .select([nw.col("has_duplicates").any()])
-            .item()
-        ):
-            message = f"Duplicate timestamps in id_col '' column '{time_col}'."
-            if raise_error:
-                raise ValueError(message)
-            warnings.warn(message)
+    # Validate time column exists
+    if time_col not in df.columns:
+        raise ValueError(f"Column '{time_col}' does not exist in DataFrame")
+
+    # Validate time column type
+    validate_column_numeric_or_datetime(df, time_col)
+
+    # Sort using Narwhals operations
+    return df.sort(by=time_col, descending=not ascending)
diff --git a/src/temporalscope/core/exceptions.py b/src/temporalscope/core/exceptions.py
index 36fae7d..23f9d85 100644
--- a/src/temporalscope/core/exceptions.py
+++ b/src/temporalscope/core/exceptions.py
@@ -18,7 +18,8 @@
 """TemporalScope/src/temporalscope/core/exceptions.py
 
 This module defines custom exceptions and warnings used throughout the TemporalScope package.
-These exceptions provide clear, actionable feedback for users during time-series forecasting workflows.
+These exceptions provide clear, actionable feedback for users during time-series forecasting workflows,
+particularly for DataFrame validation and time column handling through Narwhals.
 """
 
 
@@ -38,7 +39,8 @@ class TimeColumnError(TimeFrameError):
     Examples
     --------
     ```python
-    if not nw.is_numeric(df[time_col]) and not nw.is_timestamp(df[time_col]):
+    # Validate time column type using Narwhals
+    if not (nw.col(time_col).cast(nw.Float64()).is_valid() or nw.col(time_col).cast(nw.Datetime()).is_valid()):
         raise TimeColumnError("`time_col` must be numeric or timestamp-like.")
     ```
     """
@@ -50,12 +52,13 @@ class TargetColumnWarning(UserWarning):
     """Warning raised for potential issues with the target column.
 
     This warning is issued when the target column appears to contain sequential or vectorized data,
-    which may require transformation depending on the selected mode (e.g., `MODE_MULTI_TARGET`).
+    which may require transformation depending on the selected mode (MODE_SINGLE_TARGET vs MODE_MULTI_TARGET).
 
     Examples
     --------
     ```python
-    if mode == "multi_target" and target_col_is_vectorized:
+    # Check if target column contains sequence data
+    if mode == MODE_MULTI_TARGET and df.select([nw.col(target_col).is_list()]).item():
         warnings.warn(
             "`target_col` appears to contain sequential data. Ensure it is transformed appropriately for MODE_MULTI_TARGET.",
             TargetColumnWarning,
@@ -99,26 +102,22 @@ class ModeValidationError(TimeFrameError):
         super().__init__(self.message)
 
 
-class UnsupportedBackendError(TimeFrameError):
-    """Exception raised when an unsupported backend is encountered.
+class DataFrameValidationError(TimeFrameError):
+    """Exception raised for DataFrame validation issues.
 
-    Parameters
-    ----------
-    backend : str
-        The invalid backend that caused the error.
-    message : str
+    This error is raised when DataFrame operations fail due to invalid data,
+    schema mismatches, or other validation issues.
 
     Examples
     --------
     ```python
-    if backend not in TEMPORALSCOPE_CORE_BACKENDS:
-        raise UnsupportedBackendError(backend)
+    try:
+        # Validate numeric columns using Narwhals
+        for col in feature_cols:
+            df = df.select([nw.col(col).cast(nw.Float64()).alias(col)])
+    except Exception as e:
+        raise DataFrameValidationError(f"Failed to validate numeric columns: {str(e)}")
     ```
-
     """
 
-    def __init__(self, backend, message="Unsupported backend"):
-        """Initialize UnsupportedBackendError."""
-        self.backend = backend
-        self.message = f"{message}: {backend}."
-        super().__init__(self.message)
+    pass
diff --git a/src/temporalscope/core/temporal_data_loader.py b/src/temporalscope/core/temporal_data_loader.py
index e3e062a..cb3722f 100644
--- a/src/temporalscope/core/temporal_data_loader.py
+++ b/src/temporalscope/core/temporal_data_loader.py
@@ -17,128 +17,91 @@
 
 """TemporalScope/src/temporalscope/core/temporal_data_loader.py.
 
-This module provides `TimeFrame`, a universal data loader for time series forecasting,
-designed for state-of-the-art models, including multi-modal and mixed-frequency workflows.
-It supports integration with explainability tools such as SHAP, LIME, and Boruta-SHAP,
-enabling advanced insights for supported models. TemporalScope prioritizes flexibility,
-allowing users to adapt the package for causal partitioning and other custom workflows,
-while leaving data preparation and preprocessing entirely to the end user.
-
-
-AI Modeling for Time Series Data:
----------------------------------
-
-TemporalScope is designed with several key assumptions to ensure performance,
-scalability, and flexibility across a wide range of time series forecasting and
-XAI workflows:
-
-| Approach | Description |
-|----------|-------------|
-| Implicit & Static Time Series | The `time_col` is treated as a feature, enabling ML/DL workflows with mixed-frequency datasets. By default, `enforce_temporal_uniqueness` is False. |
-| Strict Time Series | Enforces temporal ordering and uniqueness, suited for forecasting. Group or segment validation is supported via the `id_col` parameter. |
-
-1. Preprocessed Data Requirement:
-   TemporalScope assumes users provide preprocessed data, including categorical
-   encoding, missing value imputation, and feature scaling. The focus remains on
-   explainability and partitioning, leaving general preprocessing to user
-   workflows. This design aligns with standards in libraries like TensorFlow
-   and GluonTS.
-
-2. Time Column Flexibility:
-   The `time_col` must be numeric or timestamp-like. TemporalScope supports
-   workflows with partial temporal ordering for flexible partitioning while
-   enabling strict validation with `enforce_temporal_uniqueness` where needed,
-   especially for grouped forecasts or sequence validation.
-
-3. Mixed-Frequency and Asynchronous Support:
-   Designed to handle mixed-frequency datasets and asynchronous records (e.g.,
-   sensor or trading data), TemporalScope does not enforce uniform temporal
-   spacing. Users can define their temporal logic through preprocessing while
-   benefiting from backend-agnostic sorting and validation.
-
-4. Universal Workflow Design:
-   TemporalScope assumes models operate on the entire dataset without implicit
-   groupings, ensuring compatibility with model-agnostic XAI tools like SHAP,
-   LIME, and Boruta-SHAP. This design choice supports both static feature
-   modeling and forecasting across diverse use cases.
-
-5. Supported Data Modes:
-
-| Mode | Description | Compatible Frameworks |
-|------|-------------|------------------------|
-| Single-step mode | For scalar target machine learning tasks. Each row represents a single time step. | Scikit-learn, XGBoost, LightGBM, SHAP, TensorFlow (standard regression) |
-| Multi-step mode | For sequence forecasting tasks. Input sequences (`X`) and output sequences (`Y`) are handled as separate datasets. | TensorFlow, PyTorch, Keras, SHAP, LIME |
-
-By enforcing these constraints, TemporalScope focuses on its core purpose—time series
-partitioning,  explainability, and scalability—while leaving more general preprocessing
-tasks to the user. This follows industry standards seen in popular time series libraries.
+This module provides `TimeFrame`, a universal data loader for time series forecasting that can store metadata
+for conversions between DataFrame and PyTorch/TensorFlow types. It supports state-of-the-art models including
+multi-modal and mixed-frequency workflows, with integration for explainability tools (SHAP, LIME, Boruta-SHAP).
+
+TimeFrame is designed to support different modeling approaches by allowing users to add columns that generalize
+temporal patterns. For example, adding a market regime column to analyze feature importance across different
+market conditions, or a treatment phase column to understand changing feature effects during patient care.
+TimeFrame enforces minimal restrictions to maintain flexibility:
+
+1. The time column must be numeric or timestamp-like
+2. Non-time columns must be numeric (preprocess categorical features)
+3. Data can have mixed frequencies and asynchronous records
+
+Supported Modeling Approaches:
+------------------------------
++------------------------+-----------------------------------------------+
+| Approach               | Description                                   |
++------------------------+-----------------------------------------------+
+| Standard Regression    | Basic ML models where Temporal SHAP reveals   |
+|                        | how feature importance evolves naturally      |
+|                        | over time without enforced constraints.       |
++------------------------+-----------------------------------------------+
+| Time Series            | Group-aware models (e.g., by stock_id) where  |
+| Regression             | Temporal SHAP shows how features impact       |
+|                        | predictions differently across groups and     |
+|                        | their unique temporal patterns.               |
++------------------------+-----------------------------------------------+
+| Bayesian               | Probabilistic models where Temporal SHAP      |
+| Regression             | explains how features drive both predictions  |
+|                        | and uncertainty estimates through time.       |
++------------------------+-----------------------------------------------+
+
+Supported Modes:
+----------------
++----------------+-------------------------------------------------------------------+
+| Mode           | Description                                                       |
+|                | Data Structure                                                    |
++----------------+-------------------------------------------------------------------+
+| single_target  | General machine learning tasks with scalar targets. Each row is   |
+|                | a single time step, and the target is scalar.                     |
+|                | Single DataFrame: each row is an observation.                     |
++----------------+-------------------------------------------------------------------+
+| multi_target   | Sequential time series tasks (e.g., seq2seq) for deep learning.   |
+|                | The data is split into sequences (input X, target Y).             |
+|                | Two DataFrames: X for input sequences, Y for targets.             |
+|                | Frameworks: TensorFlow, PyTorch, Keras.                           |
++----------------+-------------------------------------------------------------------+
 
 References
 ----------
-- Filho, L.L., de Oliveira Werneck, R., Castro, M., Ribeiro Mendes Júnior, P.,
-Lustosa, A., Zampieri, M., Linares, O., Moura, R., Morais, E., Amaral, M., &
-Salavati, S. (2024). A multi-modal approach for mixed-frequency time series
-forecasting. Neural Computing and Applications, pp.1-25.
-
-- Trirat, P., Shin, Y., Kang, J., Nam, Y., Bae, M., Kim, J., Kim, B., &
-Lee, J.-G. (2024). Universal time-series representation learning: A survey.
-arXiv preprint arXiv:2401.03717.
-
-- Van Ness, M., Shen, H., Wang, H., Jin, X., Maddix, D.C., & Gopalswamy, K.
-(2023). Cross-Frequency Time Series Meta-Forecasting. arXiv preprint
-arXiv:2302.02077.
-
-- Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., & Sahoo, D. (2024).
-Unified training of universal time series forecasting transformers. arXiv
-preprint arXiv:2402.02592.
-
-- Xu, Q., Zhuo, X., Jiang, C., & Liu, Y. (2019). An artificial neural network
-for mixed frequency data. Expert Systems with Applications, 118, pp.127-139.
-
-
-
-Notes
------
-- Multi-Step Mode Limitation: Currently unsupported due to limitations in
-  backends like Modin and Polars, which lack native support for vectorized
-  (sequence-based) targets in a single cell. Future updates will include
-  compatibility with formats like TensorFlow's `tf.data.Dataset` or flattened
-  target sequences.
-- Single-Step Mode Support: Narwhals-backed operations ensure that single-step
-  mode is fully supported across Pandas, Modin, and Polars without additional
-  adjustments, handling scalar target workflows seamlessly.
-- Recommendation: Use Pandas for multi-step workflows as it best supports
-  the necessary data structures. Future releases will extend compatibility
-  for vectorized targets across all backends.
-
-See Also
---------
-- Narwhals documentation: https://narwhals.readthedocs.io/
-- SHAP documentation: https://shap.readthedocs.io/
-- Boruta-SHAP documentation: https://github.com/Ekeany/Boruta-Shap
-- LIME documentation: https://lime-ml.readthedocs.io/
+1. Van Ness, M., et al. (2023). Cross-Frequency Time Series Meta-Forecasting.
+   arXiv:2302.02077.
+
+2. Woo, G., et al. (2024). Unified training of universal time series forecasting
+   transformers. arXiv:2402.02592.
+
+3. Trirat, P., et al. (2024). Universal time-series representation learning:
+   A survey. arXiv:2401.03717.
+
+4. Xu, Q., et al. (2019). An artificial neural network for mixed frequency data.
+   Expert Systems with Applications, 118, pp.127-139.
+
+5. Filho, L.L., et al. (2024). A multi-modal approach for mixed-frequency time
+   series forecasting. Neural Computing and Applications, pp.1-25.
 """
 
 from typing import Any, Dict, Optional
 
 import narwhals as nw
+from narwhals.typing import FrameT
 
 # Import necessary types and utilities from the Narwhals library for handling temporal data
 from temporalscope.core.core_utils import (
-    MODE_SINGLE_TARGET,
-    VALID_MODES,
-    SupportedTemporalDataFrame,
-    check_dataframe_nulls_nans,
-    convert_datetime_column_to_numeric,
-    convert_time_column_to_datetime,
-    get_dataframe_backend,
-    is_valid_temporal_backend,
-    is_valid_temporal_dataframe,
+    is_dataframe_empty,
     sort_dataframe_time,
-    validate_dataframe_column_types,
-    validate_temporal_uniqueness,
+    validate_and_convert_time_column,
+    validate_column_numeric_or_datetime,
+    validate_feature_columns_numeric,
+    validate_temporal_ordering,
 )
-from temporalscope.core.exceptions import UnsupportedBackendError
+
+# Constants for temporal data loading
+MODE_SINGLE_TARGET = "single_target"
+MODE_MULTI_TARGET = "multi_target"
+VALID_MODES = [MODE_SINGLE_TARGET, MODE_MULTI_TARGET]
 
 
 class TimeFrame:
@@ -185,11 +148,10 @@ class TimeFrame:
 
     def __init__(
         self,
-        df: SupportedTemporalDataFrame,
+        df: FrameT,
         time_col: str,
         target_col: str,
         time_col_conversion: Optional[str] = None,
-        dataframe_backend: Optional[str] = None,
         sort: bool = True,
         ascending: bool = True,
         mode: str = MODE_SINGLE_TARGET,
@@ -197,8 +159,7 @@ class TimeFrame:
         id_col: Optional[str] = None,
         verbose: bool = False,
     ) -> None:
-        """
-        Initialize a TimeFrame object with required validations, backend handling, and optional time column conversion.
+        """Initialize a TimeFrame object with required validations, backend handling, pre-processing.
 
         This constructor initializes the `TimeFrame` object, validates the input DataFrame,
         and performs optional sorting based on the specified `time_col`. It also allows for
@@ -310,9 +271,6 @@ class TimeFrame:
         self._id_col = id_col
         self._metadata: Dict[str, Any] = {}
 
-        # Initialize backend
-        self._backend = self._initialize_backend(df, dataframe_backend)
-
         # Validate parameters
         self._validate_parameters()
 
@@ -327,29 +285,68 @@ class TimeFrame:
         )
 
         if self._verbose:
-            print(f"TimeFrame successfully initialized with backend: {self._backend}")
+            print("TimeFrame successfully initialized")
 
     def _validate_parameters(self) -> None:
         """Validate input parameters for the TimeFrame initialization.
 
+        This method performs comprehensive validation of all parameters passed to
+        TimeFrame's constructor, ensuring type safety and value validity before
+        any data processing begins.
+
+        The validation includes:
+        1. Type checking for all parameters
+        2. Value validation for enumerated parameters
+        3. Consistency checks for related parameters
+
         Returns
         -------
         None
+            Method returns None if validation passes.
 
         Raises
         ------
         TypeError
-            If any parameter has an invalid type.
+            - If time_col is not a string
+            - If target_col is not a string
+            - If sort is not a boolean
+            - If ascending is not a boolean
+            - If verbose is not a boolean
+            - If id_col is not None or a string
         ValueError
-            If a parameter value is invalid or unsupported.
+            - If time_col_conversion is not one of {None, 'numeric', 'datetime'}
+            - If mode is not one of VALID_MODES
+
+        Examples
+        --------
+        ```python
+        from temporalscope.core.temporal_data_loader import TimeFrame
+
+        # Valid parameters
+        tf = TimeFrame(df, time_col="time", target_col="target")  # Passes validation
 
+        # Invalid type
+        tf = TimeFrame(df, time_col=123, target_col="target")  # Raises TypeError
+
+        # Invalid value
+        tf = TimeFrame(df, time_col="time", target_col="target", mode="invalid")  # Raises ValueError
+        ```
+
+        See Also
+        --------
+        TimeFrame.__init__ : The constructor that uses this validation
+        setup : The method that uses validated parameters
+
+        Notes
+        -----
+        - Called automatically during initialization
+        - Validates all instance variables
+        - Ensures consistent state before data processing
         """
         if not isinstance(self._time_col, str):
             raise TypeError(f"`time_col` must be a string. Got {type(self._time_col).__name__}.")
         if not isinstance(self._target_col, str):
             raise TypeError(f"`target_col` must be a string. Got {type(self._target_col).__name__}.")
-        if self._backend is not None and not isinstance(self._backend, str):
-            raise TypeError(f"`dataframe_backend` must be a string or None. Got {type(self._backend).__name__}.")
         if not isinstance(self._sort, bool):
             raise TypeError(f"`sort` must be a boolean. Got {type(self._sort).__name__}.")
         if not isinstance(self._ascending, bool):
@@ -366,58 +363,25 @@ class TimeFrame:
         if self._mode not in VALID_MODES:
             raise ValueError(f"Invalid mode '{self._mode}'. Must be one of {VALID_MODES}.")
 
-    def _initialize_backend(self, df: SupportedTemporalDataFrame, dataframe_backend: Optional[str]) -> str:
-        """Determine and validate the backend for the DataFrame.
-
-        Parameters
-        ----------
-        df : SupportedTemporalDataFrame
-            Input DataFrame to initialize the backend.
-        dataframe_backend : Optional[str]
-            Backend to use. If None, it is inferred from the DataFrame type.
-        df: SupportedTemporalDataFrame :
-
-        dataframe_backend: Optional[str] :
-
-
-        Returns
-        -------
-        str
-            Initialized backend for the DataFrame.
-
-        Raises
-        ------
-        UnsupportedBackendError
-            If the backend is invalid or unsupported.
-
-        """
-        if dataframe_backend:
-            is_valid_temporal_backend(dataframe_backend)
-            return dataframe_backend
-
-        is_valid, _ = is_valid_temporal_dataframe(df)
-        if not is_valid:
-            raise UnsupportedBackendError(f"Unsupported DataFrame type: {type(df).__name__}")
-        return get_dataframe_backend(df)
-
-    @nw.narwhalify
-    def sort_dataframe_time(self, df: SupportedTemporalDataFrame, ascending: bool = True) -> SupportedTemporalDataFrame:
+    def sort_dataframe_time(self, df: FrameT, ascending: bool = True) -> FrameT:
         """Sort DataFrame by time column using backend-agnostic Narwhals operations.
 
+        This method provides a consistent way to sort DataFrames by their time column
+        across all supported backends (Pandas, Polars, etc.). It delegates to
+        core_utils.sort_dataframe_time for the actual sorting operation.
+
         Parameters
         ----------
-        df : SupportedTemporalDataFrame
-            DataFrame to sort
-        ascending : bool
-            Sort direction, defaults to True
-        df: SupportedTemporalDataFrame :
-
-        ascending: bool :
-             (Default value = True)
+        df : FrameT
+            DataFrame to sort. Can be any backend supported by Narwhals
+            (Pandas, Polars, etc.).
+        ascending : bool, optional
+            Sort direction. True for ascending (default), False for descending.
 
         Returns
         -------
-        SupportedTemporalDataFrame
+        FrameT
+            A new DataFrame sorted by the time column.
 
         Examples
         --------
@@ -425,48 +389,58 @@ class TimeFrame:
         import polars as pl
         from temporalscope.core.temporal_data_loader import TimeFrame
 
+        # Create TimeFrame with unsorted data
         data = pl.DataFrame({"time": [3, 1, 4, 2, 5], "target": range(5)})
         tf = TimeFrame(data, time_col="time", target_col="target", sort=False)
-        sorted_df = tf.sort_dataframe_time(tf.df, ascending=True)
-        print(sorted_df)  # Shows data sorted by time column
+
+        # Sort ascending
+        sorted_asc = tf.sort_dataframe_time(tf.df, ascending=True)
+        print(sorted_asc)  # Shows: 1, 2, 3, 4, 5
+
+        # Sort descending
+        sorted_desc = tf.sort_dataframe_time(tf.df, ascending=False)
+        print(sorted_desc)  # Shows: 5, 4, 3, 2, 1
         ```
 
+        See Also
+        --------
+        temporalscope.core.core_utils.sort_dataframe_time : The underlying sorting function
+
         Notes
         -----
-        Uses the reusable utility function `sort_dataframe_time` for consistency across the codebase.
-        Sorted DataFrame
-
+        - Uses core_utils.sort_dataframe_time for consistent sorting across the codebase
+        - Preserves DataFrame schema and column types
+        - Returns a new DataFrame; does not modify the input DataFrame
         """
-        return sort_dataframe_time(df, time_col=self._time_col, ascending=ascending)
+        return sort_dataframe_time(df, self._time_col, ascending)
 
-    @nw.narwhalify
-    def validate_dataframe(self, df: SupportedTemporalDataFrame) -> None:
-        """Run streamlined validation checks on the DataFrame to ensure it meets required constraints.
+    def validate_dataframe(self, df: FrameT) -> None:
+        """Validate DataFrame structure and data types.
 
-        This function validates the DataFrame by:
-        1. Ensuring all columns are non-null and contain no NaNs.
-        2. Validating the `time_col` and ensuring all non-time columns are numeric.
-
-        The method is designed to support backend-agnostic operations through Narwhals and handles
-        different DataFrame backends such as Pandas, Polars, and Modin.
+        This method performs comprehensive validation of the input DataFrame:
+        1. Converts to Narwhals DataFrame for backend-agnostic operations
+        2. Checks for empty DataFrame
+        3. Validates required columns exist (time_col and target_col)
+        4. Validates time column is numeric or datetime
+        5. Validates all non-time columns are numeric
 
         Parameters
         ----------
-        df : SupportedTemporalDataFrame
-            Input DataFrame to validate.
-        df: SupportedTemporalDataFrame :
-
+        df : FrameT
+            DataFrame to validate. Can be any backend supported by Narwhals
+            (Pandas, Polars, etc.).
 
         Returns
         -------
         None
+            Method returns None if validation passes.
 
         Raises
         ------
         ValueError
-            If any columns contain nulls/NaNs or invalid data types.
-        UnsupportedBackendError
-            If the backend is not supported.
+            - If DataFrame is empty
+            - If required columns are missing
+            - If column types are invalid
 
         Examples
         --------
@@ -474,50 +448,60 @@ class TimeFrame:
         import pandas as pd
         from temporalscope.core.temporal_data_loader import TimeFrame
 
-        # Sample DataFrame
-        df = pd.DataFrame(
-            {
-                "time": pd.date_range(start="2023-01-01", periods=5, freq="D"),
-                "value": range(5),
-            }
-        )
+        # Create TimeFrame with valid data
+        df = pd.DataFrame({"time": [1, 2, 3], "target": [10, 20, 30]})
+        tf = TimeFrame(df, time_col="time", target_col="target")
 
-        # Initialize a TimeFrame object
-        tf = TimeFrame(df, time_col="time", target_col="value")
+        # Validate new data
+        new_df = pd.DataFrame({"time": [4, 5, 6], "target": [40, 50, 60]})
+        tf.validate_dataframe(new_df)  # Passes validation
 
-        # Validate the DataFrame
-        tf.validate_dataframe(df)
+        # Invalid data (missing column)
+        invalid_df = pd.DataFrame({"wrong_col": [1, 2, 3]})
+        tf.validate_dataframe(invalid_df)  # Raises ValueError
         ```
 
+        See Also
+        --------
+        temporalscope.core.core_utils.validate_column_numeric_or_datetime
+        temporalscope.core.core_utils.validate_feature_columns_numeric
+
         Notes
         -----
-        - This function ensures that `time_col` is valid and optionally convertible.
-        - All other columns must be numeric and free from null values.
-
+        - Uses core_utils functions for consistent validation across the codebase
+        - Performs validation without modifying the input DataFrame
+        - Supports all DataFrame backends through Narwhals abstraction
         """
-        # Step 1: Ensure all columns are free of nulls and NaNs
-        null_counts = check_dataframe_nulls_nans(df, df.columns)
-        null_columns = [col for col, count in null_counts.items() if count > 0]
-        if null_columns:
-            raise ValueError(f"Missing values detected in columns: {', '.join(null_columns)}")
+        # Convert to Narwhals DataFrame
+        df = nw.from_native(df)
+
+        # Validate DataFrame using core_utils functions
+        if is_dataframe_empty(df):
+            raise ValueError("Empty DataFrame provided")
+
+        # Validate columns exist
+        if self._time_col not in df.columns:
+            raise ValueError(f"Column '{self._time_col}' does not exist in DataFrame")
+        if self._target_col not in df.columns:
+            raise ValueError(f"Column '{self._target_col}' does not exist in DataFrame")
 
-        # Step 2: Validate column types (time_col and others)
-        validate_dataframe_column_types(df, self._time_col)
+        # Validate using core_utils
+        validate_column_numeric_or_datetime(df, self._time_col)
+        validate_feature_columns_numeric(df, time_col=self._time_col)
 
-        # If verbose, notify the user about validation success
         if self._verbose:
             print("Validation completed successfully.")
 
     @nw.narwhalify
     def setup(
         self,
-        df: SupportedTemporalDataFrame,
+        df: FrameT,
         sort: bool = True,
         ascending: bool = True,
         time_col_conversion: Optional[str] = None,
         enforce_temporal_uniqueness: bool = False,
         id_col: Optional[str] = None,
-    ) -> SupportedTemporalDataFrame:
+    ) -> FrameT:
         """Initialize and validate a TimeFrame's DataFrame with proper sorting and validation.
 
         This method performs the necessary validation, conversion, and sorting operations to prepare
@@ -530,6 +514,26 @@ class TimeFrame:
         3. Perform temporal uniqueness validation within groups if enabled.
         4. Optionally sort the DataFrame by `time_col` in the specified order.
 
+        Parameters
+        ----------
+        df : SupportedTemporalDataFrame
+            Input DataFrame to set up and validate.
+        sort : bool
+            Whether to sort the DataFrame by `time_col`. Defaults to True.
+        ascending : bool
+            Sort order if sorting is enabled. Defaults to True.
+        time_col_conversion : Optional[str]
+            Optional. Specify the conversion type for the `time_col`:
+            - 'numeric': Convert to Float64.
+        the input DataFrame for use in TemporalScope workflows. The method is idempotent.
+
+        Steps:
+        ------
+        1. Validate the input DataFrame using the `validate_dataframe` method.
+        2. Optionally convert the `time_col` to the specified type (`numeric` or `datetime`).
+        3. Perform temporal uniqueness validation within groups if enabled.
+        4. Optionally sort the DataFrame by `time_col` in the specified order.
+
         Parameters
         ----------
         df : SupportedTemporalDataFrame
@@ -608,145 +612,236 @@ class TimeFrame:
         Validated, converted, and optionally sorted DataFrame.
 
         """
-        # Step 1: Basic validation
+        from temporalscope.core.core_utils import is_dataframe_empty
+
+        # Convert to Narwhals DataFrame
+        df = nw.from_native(df)
+
+        # Check if DataFrame is empty
+        if is_dataframe_empty(df):
+            raise ValueError("Empty DataFrame provided")
+
+        # Check if required columns exist
+        if self._time_col not in df.columns:
+            raise ValueError(f"Column '{self._time_col}' does not exist in DataFrame")
+        if self._target_col not in df.columns:
+            raise ValueError(f"Column '{self._target_col}' does not exist in DataFrame")
+
+        # First validate DataFrame (includes type checks and null validation)
         self.validate_dataframe(df)
 
-        # Step 2: Time column conversion
-        if time_col_conversion == "numeric":
-            df = convert_datetime_column_to_numeric(df, self._time_col)
-            if self._verbose:
-                print(f"Converted column '{self._time_col}' to numeric (Unix timestamp).")
-        elif time_col_conversion == "datetime":
-            df = convert_time_column_to_datetime(df, self._time_col, nw.col(self._time_col), df.schema[self._time_col])
+        # Convert time column if requested
+        if time_col_conversion:
+            df = validate_and_convert_time_column(df, self._time_col, time_col_conversion)
             if self._verbose:
-                print(f"Converted column '{self._time_col}' to datetime.")
+                print(f"Converted column '{self._time_col}' to {time_col_conversion}.")
 
-        # Step 3: If enforce temporal uniqueness is enabled, validate with `validate_temporal_uniqueness`
+        # Check temporal uniqueness if required
         if enforce_temporal_uniqueness:
-            validate_temporal_uniqueness(df, self._time_col, raise_error=True, id_col=id_col)
+            validate_temporal_ordering(df, self._time_col, id_col=id_col, enforce_equidistant_sampling=False)
+            if self._verbose:
+                print("Temporal ordering validation successful.")
 
-        # Step 4: Optional sorting (user's choice for data organization)
+        # Optional sorting (lazy operation)
         if sort:
             df = self.sort_dataframe_time(df, ascending=ascending)
 
         return df
 
-    @nw.narwhalify
-    def update_dataframe(self, df: SupportedTemporalDataFrame) -> None:
+    @nw.narwhalify(eager_only=True)
+    def update_dataframe(self, df: FrameT) -> None:
         """Update TimeFrame's internal DataFrame with new data.
 
-        Whilst TemporalScope target shifter and padding functions are available, the user must
-        handle any data transformations (e.g., changing target columns) within the TimeFrame
-        workflow and ensure that they handle pre-processing to be compatible with
-        downstream tasks.
+        This method updates the internal DataFrame with new data, performing all necessary
+        validations and conversions to maintain consistency. Uses eager evaluation since
+        it modifies internal state.
+
+        The update process includes:
+        1. Converting input to Narwhals DataFrame
+        2. Running full setup validation and conversion pipeline
+        3. Replacing internal DataFrame with validated result
 
         Parameters
         ----------
-        df : SupportedTemporalDataFrame
+        df : FrameT
+            The new DataFrame to update with. Must contain the required time and target
+            columns with appropriate data types.
 
-        Example Usage:
-        --------------
+        Returns
+        -------
+        None
+            Method updates internal state but returns nothing.
+
+        Raises
+        ------
+        ValueError
+            - If DataFrame is empty
+            - If required columns are missing
+            - If column types are invalid
+            - If temporal validation fails
+
+        Examples
+        --------
         ```python
-          import polars as pl
-          from temporalscope.core.temporal_data_loader import TimeFrame
+        import pandas as pd
+        from temporalscope.core.temporal_data_loader import TimeFrame
 
-          # Initial TimeFrame setup
-          data = pl.DataFrame(
-              {
-                  "time": pl.date_range(start="2021-01-01", periods=5, interval="1d"),
-                  "target": range(5),
-                  "feature": range(5),
-              }
-          )
-          tf = TimeFrame(
-              data,
-              time_col="time",
-              target_col="target",
-              ascending=True,  # Sort order set at initialization
-              sort=True,  # Sort behavior set at initialization
-          )
+        # Initialize TimeFrame
+        initial_df = pd.DataFrame({"time": [1, 2, 3], "target": [10, 20, 30]})
+        tf = TimeFrame(initial_df, time_col="time", target_col="target")
 
-          # Update with new data - uses parameters from initialization
-          new_data = pl.DataFrame(
-              {
-                  "time": pl.date_range(start="2021-01-06", periods=5, interval="1d"),
-                  "target": range(5, 10),
-                  "feature": range(5, 10),
-              }
-          )
-          tf.update_dataframe(new_data)  # Will use time_col="time", ascending=True, sort=True
+        # Update with new data
+        new_df = pd.DataFrame({"time": [4, 5, 6], "target": [40, 50, 60]})
+        tf.update_dataframe(new_df)  # Updates internal DataFrame
+
+        # Invalid update (missing column)
+        invalid_df = pd.DataFrame({"wrong_col": [1, 2, 3]})
+        tf.update_dataframe(invalid_df)  # Raises ValueError
         ```
 
+        See Also
+        --------
+        setup : The underlying validation and setup method
+        validate_dataframe : The validation method used
+
         Notes
         -----
-        This method uses the parameters set during TimeFrame initialization:
-        - Uses the same time_col and target_col
-        - Maintains the same sort order (ascending/descending)
-        - Keeps the same sorting behavior (enabled/disabled)
+        - Uses eager evaluation to ensure immediate state update
+        - Maintains all TimeFrame settings (sorting, conversion, etc.)
+        - Performs full validation to ensure data consistency
+        """
+        # Convert to Narwhals DataFrame first
+        df = nw.from_native(df)
+
+        # Setup with all validations and conversions
+        self._df = self.setup(
+            df,
+            sort=self._sort,
+            ascending=self._ascending,
+            time_col_conversion=self._time_col_conversion,
+            enforce_temporal_uniqueness=self._enforce_temporal_uniqueness,
+            id_col=self._id_col,
+        )
 
-        If you need to change these parameters, create a new TimeFrame instance
-        with the desired configuration.
+        if self._verbose:
+            print("DataFrame successfully updated.")
 
-        See Also
-        --------
-        - :class:`temporalscope.target_shifters.single_step.SingleStepTargetShifter`
-        - :class:`temporalscope.partition.padding.functional`
-        For handling target transformations and padding operations.
-            New DataFrame to use
-        df: SupportedTemporalDataFrame :
+    @property
+    def df(self) -> FrameT:
+        """Access the internal DataFrame.
+
+        This property provides read-only access to the TimeFrame's internal DataFrame.
+        The DataFrame maintains all validations, conversions, and sorting settings
+        applied during initialization or updates.
 
         Returns
         -------
-        None
+        FrameT
+            The current state of the DataFrame, with all validations and
+            transformations applied.
 
-        """
-        self._df = self.setup(df, sort=True, ascending=self._ascending)
+        Examples
+        --------
+        ```python
+        import pandas as pd
+        from temporalscope.core.temporal_data_loader import TimeFrame
 
-    @property
-    def df(self) -> SupportedTemporalDataFrame:
-        """Return the DataFrame in its current state.
+        # Create TimeFrame
+        tf = TimeFrame(pd.DataFrame({"time": [1, 2, 3], "target": [10, 20, 30]}), time_col="time", target_col="target")
 
-        Returns
-        -------
-        SupportedTemporalDataFrame
-            The DataFrame managed by the TimeFrame instance.
+        # Access DataFrame
+        current_df = tf.df
+        print(current_df)  # Shows current state
+        ```
+
+        See Also
+        --------
+        update_dataframe : Method to update the internal DataFrame
+        setup : Method that prepares the DataFrame
 
+        Notes
+        -----
+        - Returns a reference to the internal DataFrame
+        - Any modifications should be done through update_dataframe
+        - Maintains all TimeFrame settings and validations
         """
         return self._df
 
     @property
     def mode(self) -> str:
-        """Return the mode of the TimeFrame instance.
+        """Get the TimeFrame's operation mode.
+
+        This property indicates whether the TimeFrame is configured for single-target
+        or multi-target operations, affecting how data is processed and validated.
 
         Returns
         -------
         str
-            The mode of operation, either `MODE_SINGLE_TARGET` or `MODE_MULTI_TARGET`.
+            The current operation mode:
+            - MODE_SINGLE_TARGET: For scalar target predictions
+            - MODE_MULTI_TARGET: For sequence forecasting tasks
 
-        """
-        return self._mode
+        Examples
+        --------
+        ```python
+        from temporalscope.core.temporal_data_loader import TimeFrame, MODE_MULTI_TARGET
 
-    @property
-    def backend(self) -> str:
-        """Return the backend of the TimeFrame instance.
+        # Create TimeFrame in multi-target mode
+        tf = TimeFrame(df, time_col="time", target_col="target", mode=MODE_MULTI_TARGET)
 
-        Returns
-        -------
-        str
-            The backend of the DataFrame, either specified or inferred.
+        # Check mode
+        if tf.mode == MODE_MULTI_TARGET:
+            print("Configured for sequence forecasting")
+        ```
 
+        See Also
+        --------
+        TimeFrame.__init__ : Where mode is set during initialization
+
+        Notes
+        -----
+        - Mode affects validation and processing behavior
+        - Cannot be changed after initialization
+        - Determines compatibility with different model types
         """
-        return self._backend
+        return self._mode
 
     @property
     def ascending(self) -> bool:
-        """Return the sort order of the TimeFrame instance.
+        """Get the TimeFrame's sort order setting.
+
+        This property indicates whether time-based sorting is performed in ascending
+        or descending order, affecting how data is organized for analysis and modeling.
 
         Returns
         -------
         bool
-            The sort order, True if ascending, False if descending.
+            True if sorting is ascending (earlier to later),
+            False if descending (later to earlier).
+
+        Examples
+        --------
+        ```python
+        from temporalscope.core.temporal_data_loader import TimeFrame
+
+        # Create TimeFrame with descending sort
+        tf = TimeFrame(df, time_col="time", target_col="target", ascending=False)
+
+        # Check sort order
+        if not tf.ascending:
+            print("Data sorted from latest to earliest")
+        ```
+
+        See Also
+        --------
+        sort_dataframe_time : Method that uses this setting
+        setup : Where sorting is applied
 
+        Notes
+        -----
+        - Affects all sorting operations
+        - Set during initialization
+        - Used by sort_dataframe_time method
         """
         return self._ascending
 
@@ -760,30 +855,34 @@ class TimeFrame:
         extensions, including multi-target workflows and integration with deep
         learning libraries like TensorFlow or PyTorch.
 
+        Returns
+        -------
+        Dict[str, Any]
+            Dictionary for storing metadata related to the TimeFrame.
+
         Examples
         --------
-        >>> # Initialize a TimeFrame
-        >>>  tf = TimeFrame(df, time_col="time", target_col="value")
-        >>> ...
-        >>> # Add custom metadata
-        >>> tf.metadata["description"] = "This dataset is for monthly sales forecasting"
-        >>> tf.metadata["model_details"] = {"type": "LSTM", "framework": "TensorFlow"}
-        >>> ...
-        >>> # Access metadata
-        >>> print(tf.metadata["description"])  # Output: "This dataset is for monthly sales forecasting"
+        ```python
+        # Initialize a TimeFrame
+        tf = TimeFrame(df, time_col="time", target_col="value")
+
+        # Add custom metadata
+        tf.metadata["description"] = "This dataset is for monthly sales forecasting"
+        tf.metadata["model_details"] = {"type": "LSTM", "framework": "TensorFlow"}
+
+        # Access metadata
+        print(tf.metadata["description"])  # Output: "This dataset is for monthly sales forecasting"
+        ```
 
         Notes
         -----
         This metadata container is designed following patterns seen in deep reinforcement
         learning (DRL) libraries like Stable-Baselines3, where additional metadata is
         stored alongside primary data structures for extensibility.
-        - In future releases, this will support multi-target workflows, enabling the storage
-        of processed tensor data for deep learning explainability (e.g., SHAP, LIME).
-
-        Returns
-        -------
-        Dict[str, Any]
-            Dictionary for storing metadata related to the TimeFrame.
 
+        Future Support
+        -------------
+        In future releases, this will support multi-target workflows, enabling the storage
+        of processed tensor data for deep learning explainability (e.g., SHAP, LIME).
         """
         return self._metadata
diff --git a/src/temporalscope/datasets/dataset_validator.py b/src/temporalscope/datasets/dataset_validator.py
index 58aec70..b39aeb1 100644
--- a/src/temporalscope/datasets/dataset_validator.py
+++ b/src/temporalscope/datasets/dataset_validator.py
@@ -21,81 +21,6 @@ This module provides backend-agnostic dataset validation utilities based on rese
 heuristics. Using Narwhals operations, it enables consistent validation across different
 DataFrame backends while supporting domain-specific requirements through customizable
 thresholds.
-
-For academic illustration only (see LICENSE for terms), consider domains like quantitative
-finance, where these validators could check high-frequency trading data windows for
-sufficient samples and feature quality, or healthcare analytics, where validators might
-be configured with different thresholds for smaller datasets. While these examples
-demonstrate the validator's flexibility, users must determine appropriate validation
-criteria for their specific use cases, as all functionality is provided AS-IS without
-warranty of any kind.
-
-Engineering Design
-------------------
-The validator follows a clear separation between validation configuration and execution,
-designed to work seamlessly with both TimeFrame and raw DataFrame inputs.
-
-| Component | Description |
-|-----------|-------------|
-| `fit()` | Input validation phase that ensures: <br>- Valid DataFrame type <br>- Required columns present <br>- Validation thresholds configured |
-| `transform()` | Pure Narwhals validation phase that: <br>- Uses backend-agnostic operations only <br>- Performs configured validation checks <br>- Returns detailed validation results |
-Backend-Specific Patterns
--------------------------
-The following table outlines key patterns for working with different DataFrame backends
-through Narwhals operations:
-
-| Backend | Implementation Pattern |
-|---------|------------------------|
-| LazyFrame (Dask/Polars) | Uses `collect()` for scalar access, handles lazy evaluation through proper Narwhals operations, avoids direct indexing. |
-| PyArrow | Uses `nw.Int64` for numeric operations, handles comparisons through Narwhals, converts types before arithmetic operations. |
-| All Backends | Uses pure Narwhals operations for validation checks, avoids any backend-specific code to ensure consistent behavior. |
-
-Research-Backed Thresholds
---------------------------
-The following table summarizes validation thresholds derived from key research:
-
-| Validation Check | Default Threshold | Source | Reasoning |
-|-----------------|-------------------|--------|-----------|
-| Minimum Samples | ≥ 3,000 | Grinsztajn et al. (2022) | Ensures sufficient data for complex model training |
-| Maximum Samples | ≤ 50,000 | Shwartz-Ziv et al. (2021) | Defines medium-sized dataset upper bound |
-| Minimum Features | ≥ 4 | Shwartz-Ziv et al. (2021) | Ensures meaningful complexity for model learning |
-| Maximum Features | < 500 | Gorishniy et al. (2021) | Avoids high-dimensional data challenges |
-| Feature/Sample Ratio | d/n < 1/10 | Grinsztajn et al. (2022) | Prevents overfitting risk |
-| Categorical Cardinality | ≤ 20 unique values | Grinsztajn et al. (2022) | Manages categorical feature complexity |
-| Numerical Uniqueness | ≥ 10 unique values | Gorishniy et al. (2021) | Ensures sufficient feature variability |
-
-Examples
---------
-```python
-import pandas as pd
-from temporalscope.datasets.dataset_validator import DatasetValidator
-
-# Create sample data
-df = pd.DataFrame({"numeric_feature": range(100), "categorical_feature": ["A", "B"] * 50, "target": range(100)})
-
-# Create validator with custom thresholds
-validator = DatasetValidator(
-    min_samples=1000, max_samples=10000, checks_to_run=["sample_size", "feature_count"], enable_warnings=True
-)
-
-# Run validation checks
-results = validator.validate(df, target_col="target")
-
-# Print detailed report
-validator.print_report(results)
-```
-
-Notes
------
-- Uses the scikit-learn-style fit/transform pattern but adapted for TemporalScope:
- * fit() validates input DataFrame compatibility
- * transform() is @nw.narwhalify'd for backend-agnostic operations
-- This pattern is used throughout TemporalScope to ensure:
- * Input validation happens in fit()
- * All operations use Narwhals' backend-agnostic API in transform()
-- Supports customizable thresholds for different domain requirements
-- Integrates with data pipelines through scikit-learn compatible API
-
 """
 
 import warnings
@@ -106,47 +31,10 @@ import narwhals as nw
 from narwhals.typing import FrameT
 from tabulate import tabulate  # type: ignore
 
-from temporalscope.core.core_utils import SupportedTemporalDataFrame, is_valid_temporal_dataframe
-
 
 @dataclass
 class ValidationResult:
-    """Container for dataset validation results, designed for integration with data pipelines and monitoring systems.
-
-    This class provides structured validation results that can be easily integrated into
-    data pipelines, logging systems, and monitoring dashboards. It includes methods for
-    serialization and log formatting to support automated decision making in pipelines.
-
-    Parameters
-    ----------
-    passed : bool
-        Whether the check passed
-    message : Optional[str]
-        Optional message explaining the result
-    details : Optional[Dict[str, Any]]
-        Optional dictionary with detailed results
-    severity : Optional[str]
-
-    Examples
-    --------
-    ```python
-    # In an Airflow DAG
-    def validate_dataframeset(**context):
-       validator = DatasetValidator()
-       results = validator.fit_transform(df)
-
-       # Get structured results for logging
-       for check_name, result in results.items():
-           log_entry = result.to_log_entry()
-           if not result.passed:
-               context["task_instance"].xcom_push(key=f"validation_failure_{check_name}", value=result.to_dict())
-
-               # Log to monitoring system
-               logger.log(level=log_entry["log_level"], msg=f"Validation check '{check_name}' failed", extra=log_entry)
-    Log level for the validation result (e.g., 'WARNING', 'ERROR')
-    ```
-
-    """
+    """Container for dataset validation results."""
 
     passed: bool
     message: Optional[str] = None
@@ -154,11 +42,11 @@ class ValidationResult:
     severity: Optional[str] = None
 
     def to_dict(self) -> Dict[str, Any]:
-        """Convert result to dictionary for serialization in data pipelines."""
+        """Convert result to dictionary for serialization."""
         return {"passed": self.passed, "message": self.message, "details": self.details, "severity": self.severity}
 
     def to_log_entry(self) -> Dict[str, Any]:
-        """Format result as a structured log entry for monitoring systems."""
+        """Format result as a structured log entry."""
         return {
             "validation_passed": self.passed,
             "validation_message": self.message,
@@ -168,36 +56,12 @@ class ValidationResult:
 
     @classmethod
     def get_failed_checks(cls, results: Dict[str, "ValidationResult"]) -> Dict[str, "ValidationResult"]:
-        """Get all failed validation checks for pipeline decision making.
-
-        Parameters
-        ----------
-        results : Dict[str, ValidationResult]
-            Dictionary of validation results
-
-        Returns
-        -------
-        Dict[str, ValidationResult]
-            Dictionary of failed checks
-
-        """
+        """Get all failed validation checks."""
         return {name: result for name, result in results.items() if not result.passed}
 
     @classmethod
     def get_validation_summary(cls, results: Dict[str, "ValidationResult"]) -> Dict[str, Any]:
-        """Get summary statistics for monitoring dashboards.
-
-        Parameters
-        ----------
-        results : Dict[str, ValidationResult]
-            Dictionary of validation results
-
-        Returns
-        -------
-        Dict[str, Any]
-            Summary statistics
-
-        """
+        """Get summary statistics."""
         return {
             "total_checks": len(results),
             "passed_checks": sum(1 for r in results.values() if r.passed),
@@ -207,107 +71,7 @@ class ValidationResult:
 
 
 class DatasetValidator:
-    """A validator for ensuring dataset quality using research-backed heuristics.
-
-    This class provides comprehensive dataset validation functionality through
-    Narwhals' backend-agnostic operations, supporting both TimeFrame objects
-    and raw DataFrames. Designed for integration into data pipelines and
-    temporal workflows, it enables automated quality checks and monitoring.
-
-    Engineering Design Assumptions:
-    -------------------------------
-    1. Input Validation:
-        - Supports all Narwhals-compatible DataFrame types
-        - Handles both eager and lazy evaluation patterns
-        - Validates column presence and types
-
-    2. Validation Checks:
-        - Each check is independent and configurable
-        - Uses pure Narwhals operations for backend compatibility
-        - Returns detailed results with messages and metrics
-
-    3. Backend Compatibility:
-        - No direct DataFrame indexing or operations
-        - Handles LazyFrame evaluation properly
-        - Uses type-safe numeric operations
-
-    Pipeline Integration Features:
-    ------------------------------
-    - Automated quality gates for pipeline decision making
-    - Structured results for monitoring and alerting systems
-    - Support for temporal workflow validation
-
-    Attributes
-    ----------
-    min_samples : int
-        Minimum number of samples required, based on Grinsztajn et al. (2022)
-    max_samples : int
-        Maximum number of samples allowed, based on Shwartz-Ziv et al. (2021)
-    min_features : int
-        Minimum number of features required, based on Shwartz-Ziv et al. (2021)
-    max_features : int
-        Maximum number of features allowed, based on Gorishniy et al. (2021)
-    max_feature_ratio : float
-        Maximum feature-to-sample ratio, based on Grinsztajn et al. (2022)
-    min_unique_values : int
-        Minimum unique values for numerical features
-    max_categorical_values : int
-        Maximum unique values for categorical features
-    class_imbalance_threshold : float
-        Maximum ratio between largest and smallest classes
-    checks_to_run : Optional[List[str]]
-        List of validation checks to run. If None, runs all checks.
-    enable_warnings : bool
-        Whether to show warning messages for failed checks
-
-
-    Raises
-    ------
-    ValueError
-        If invalid checks are specified
-
-    Examples
-    --------
-    ```python
-    import pandas as pd
-    from temporalscope.datasets import DatasetValidator
-
-    # Create sample data
-    df = pd.DataFrame({"feature1": range(5000), "target": range(5000)})
-
-    # Initialize and run validator
-    validator = DatasetValidator()
-    results = validator.fit_transform(df)
-    print(f"All checks passed: {all(r.passed for r in results.values())}")
-    ```
-
-    ```python
-    # In an Airflow DAG
-    def validate_dataframeset_task(**context):
-        validator = DatasetValidator(min_samples=1000, checks_to_run=["sample_size", "feature_count"])
-
-        results = validator.fit_transform(df)
-        failed = ValidationResult.get_failed_checks(results)
-
-        if failed:
-            # Log failures and push metrics
-            metrics = ValidationResult.get_validation_summary(results)
-            monitoring.push_metrics("data_validation", metrics)
-
-        # Fail pipeline if critical checks failed
-        if any(r.severity == "ERROR" for r in failed.values()):
-            raise AirflowException("Critical validation checks failed")
-    ```
-
-    Notes
-    -----
-    Backend-Specific Patterns:
-    - Use collect() for scalar access (LazyFrame)
-    - Use nw.Int64 for numeric operations (PyArrow)
-    - Let @nw.narwhalify handle conversions
-    - Supports integration with workflow systems (Airflow, Prefect)
-
-    """
+    """A validator for ensuring dataset quality using research-backed heuristics."""
 
     # Available validation checks
     AVAILABLE_CHECKS = {
@@ -335,64 +99,7 @@ class DatasetValidator:
         checks_to_run: Optional[List[str]] = None,
         enable_warnings: bool = True,
     ):
-        """
-        Initialize the validator with column configuration and thresholds.
-
-        This validator performs quality checks on single DataFrames, designed for
-        integration into automated pipelines (e.g., Airflow). It validates data quality
-        using research-backed thresholds while leaving partitioning and parallelization
-        to end users.
-
-        Engineering Design Assumptions
-        -------------------------------
-        1. **Single DataFrame Focus**:
-           - Operates on individual DataFrames.
-           - Assumes end-users handle partitioning and parallelization.
-           - Designed for pipeline integration.
-
-        2. **Basic Validation**:
-           - Verifies the existence of `time_col` and `target_col`.
-           - Validates numeric columns (excluding `time_col`).
-           - Checks for null values.
-
-        3. **Research-Backed Thresholds**:
-           - Sample size thresholds (Grinsztajn et al., 2022).
-           - Feature counts (Shwartz-Ziv et al., 2021).
-           - Feature ratios (Gorishniy et al., 2021).
-
-        Parameters
-        ----------
-        time_col : str
-            Column representing time values.
-        target_col : str
-            Column representing the target variable.
-        min_samples : int
-            Minimum samples required (Grinsztajn et al., 2022).
-        max_samples : int
-            Maximum samples allowed (Shwartz-Ziv et al., 2021).
-        min_features : int
-            Minimum features required (Shwartz-Ziv et al., 2021).
-        max_features : int
-            Maximum features allowed (Gorishniy et al., 2021).
-        max_feature_ratio : float
-            Maximum feature-to-sample ratio (Grinsztajn et al., 2022).
-        min_unique_values : int
-            Minimum unique values required for numerical features.
-        max_categorical_values : int
-            Maximum unique values allowed for categorical features.
-        class_imbalance_threshold : float
-            Maximum ratio between the largest and smallest class sizes.
-        checks_to_run : Optional[List[str]]
-            List of validation checks to execute.
-        enable_warnings : bool
-            Whether to display warning messages.
-
-        Raises
-        ------
-        ValueError
-            If invalid checks are specified.
-
-        """
+        """Initialize the validator with column configuration and thresholds."""
         self.time_col = time_col
         self.target_col = target_col
         self.min_samples = min_samples
@@ -414,57 +121,16 @@ class DatasetValidator:
         else:
             self.checks_to_run = self.AVAILABLE_CHECKS
 
-    def _ensure_narwhals_df(self, df: Union[SupportedTemporalDataFrame, FrameT]) -> FrameT:
-        """Ensure DataFrame is Narwhals-compatible.
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to validate
-
-        Returns
-        -------
-        FrameT
-            Narwhals-compatible DataFrame
-
-        Raises
-        ------
-        TypeError
-            If input is not a valid temporal DataFrame
-
-        """
-        is_valid, _ = is_valid_temporal_dataframe(df)
-        if not is_valid:
-            raise TypeError("Input must be a valid temporal DataFrame")
-        return df
+    def _ensure_narwhals_df(self, df: Union[Any, FrameT]) -> FrameT:
+        """Ensure DataFrame is Narwhals-compatible."""
+        try:
+            return nw.from_native(df)
+        except Exception as e:
+            raise TypeError(f"Input must be convertible to a Narwhals DataFrame. Error: {str(e)}")
 
     @nw.narwhalify
-    def _check_feature_variability(self, df: Union[SupportedTemporalDataFrame, FrameT]) -> ValidationResult:
-        """Check feature value variability and quality.
-
-        This method evaluates feature variability through a simple process:
-        1. Identifies feature columns in the dataset
-        2. For each feature:
-        - Counts unique values to ensure sufficient variability
-        - Checks for null values to ensure data quality
-        3. Validates against minimum uniqueness threshold
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to validate
-
-        Returns
-        -------
-        ValidationResult
-            ValidationResult with:
-            - passed: Whether all features meet variability requirements
-            - message: Description of any issues found
-            - details: Dictionary containing:
-            - numeric_feature: Whether features are numeric
-            - column_name: Number of unique values for each feature
-
-        """
+    def _check_feature_variability(self, df: FrameT) -> ValidationResult:
+        """Check feature value variability and quality."""
         details: Dict[str, Any] = {"numeric_feature": True}
 
         # Get feature columns
@@ -510,46 +176,8 @@ class DatasetValidator:
         return ValidationResult(True, None, details)
 
     @nw.narwhalify
-    def _check_class_balance(
-        self,
-        df: Union[SupportedTemporalDataFrame, FrameT],
-        target_col: str,
-    ) -> ValidationResult:
-        """Check class balance in target column.
-
-        This method provides a simple class balance check by:
-        1. Counting total samples
-        2. Adding class count information to details
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to validate
-        target_col : str
-            Target column name
-        df: Union[SupportedTemporalDataFrame :
-
-        FrameT] :
-
-        target_col: str :
-
-
-        Returns
-        -------
-        ValidationResult
-
-        Notes
-        -----
-        Implementation Details:
-        - Uses count() for backend-agnostic counting
-        - Handles LazyFrame evaluation through collect()
-        - Converts PyArrow scalars using as_py()
-            ValidationResult with:
-            - passed: Always True (basic check)
-            - details: Dictionary containing:
-            - class_counts: Basic count information
-
-        """
+    def _check_class_balance(self, df: FrameT, target_col: str) -> ValidationResult:
+        """Check class balance in target column."""
         if not target_col:
             return ValidationResult(True, "No target column specified")
 
@@ -568,39 +196,9 @@ class DatasetValidator:
         return ValidationResult(True, None, details)
 
     def _execute_check(
-        self,
-        check_name: str,
-        df: Union[SupportedTemporalDataFrame, FrameT],
-        target_col: Optional[str] = None,
+        self, check_name: str, df: FrameT, target_col: Optional[str] = None
     ) -> Optional[ValidationResult]:
-        """Execute a single validation check.
-
-        Parameters
-        ----------
-        check_name : str
-            Name of the check to execute
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to validate
-        target_col : Optional[str]
-            Column name for target-specific checks
-
-        Returns
-        -------
-        Optional[ValidationResult]
-            Result of the validation check if enabled, None otherwise
-
-        Raises
-        ------
-        ValueError
-            If check_name is not a valid check name
-
-        Notes
-        -----
-        - Executes a single validation check based on check_name
-        - Returns None if check is not enabled
-        - Handles target-specific checks appropriately
-
-        """
+        """Execute a single validation check."""
         if check_name not in self.checks_to_run:
             return None
 
@@ -621,36 +219,8 @@ class DatasetValidator:
         return result
 
     @nw.narwhalify
-    def _check_sample_size(self, df: Union[SupportedTemporalDataFrame, FrameT]) -> ValidationResult:
-        """Check if dataset meets sample size requirements.
-
-        This method evaluates sample size through a simple process:
-        1. Counts total samples using backend-agnostic operations
-        2. Validates against configured minimum and maximum thresholds
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to validate
-
-        Returns
-        -------
-        ValidationResult
-
-        Notes
-        -----
-        Implementation Details:
-        - Uses count() for backend-agnostic sample counting
-        - Handles LazyFrame evaluation through collect()
-        - Converts PyArrow scalars using as_py()
-        - Handles empty DataFrames gracefully
-            ValidationResult with:
-            - passed: Whether sample size is within acceptable range
-            - message: Description of any issues found
-            - details: Dictionary containing:
-            - num_samples: Total number of samples in dataset
-
-        """
+    def _check_sample_size(self, df: FrameT) -> ValidationResult:
+        """Check if dataset meets sample size requirements."""
         # Handle empty DataFrame
         if not df.columns:
             details = {"num_samples": 0}
@@ -694,28 +264,8 @@ class DatasetValidator:
         return ValidationResult(True, None, details)
 
     @nw.narwhalify
-    def _check_feature_count(self, df: Union[SupportedTemporalDataFrame, FrameT]) -> ValidationResult:
-        """
-        Validate if the dataset meets feature count requirements.
-
-        This method performs feature count validation using the following steps:
-        1. Counts the total number of features, excluding the time and target columns.
-        2. Verifies the count against the configured minimum and maximum thresholds.
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            The DataFrame to validate.
-
-        Returns
-        -------
-        ValidationResult
-            An object containing the validation outcome, with the following attributes:
-            - passed (bool): Indicates whether the feature count is within the acceptable range.
-            - message (str): Describes any issues identified during validation.
-            - details (dict): Provides additional context with the following key:
-                - num_features (int): The total number of features in the dataset.
-        """
+    def _check_feature_count(self, df: FrameT) -> ValidationResult:
+        """Validate if the dataset meets feature count requirements."""
         df = self._ensure_narwhals_df(df)
 
         # Get feature columns using _get_feature_columns
@@ -751,40 +301,11 @@ class DatasetValidator:
         return ValidationResult(passed=True, details=details, severity="INFO")
 
     @nw.narwhalify
-    def _check_feature_ratio(self, df: Union[SupportedTemporalDataFrame, FrameT]) -> ValidationResult:
-        """Check feature-to-sample ratio.
-
-        This method evaluates feature-to-sample ratio through a simple process:
-        1. Counts total samples using backend-agnostic operations
-        2. Counts feature columns (excluding time and target)
-        3. Calculates ratio and validates against threshold
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to validate
-
-        Returns
-        -------
-        ValidationResult
-
-        Notes
-        -----
-        Implementation Details:
-        - Uses count() for backend-agnostic sample counting
-        - Handles LazyFrame evaluation through collect()
-        - Converts PyArrow scalars using as_py()
-        - Only counts feature columns in ratio calculation
-            ValidationResult with:
-            - passed: Whether ratio is within acceptable range
-            - message: Description of any issues found
-            - details: Dictionary containing:
-            - ratio: Feature-to-sample ratio (num_features/num_samples)
-
-        """
+    def _check_feature_ratio(self, df: FrameT) -> ValidationResult:
+        """Check feature-to-sample ratio."""
         # Handle empty DataFrame
         if not df.columns:
-            details = {"ratio": 0}
+            details = {"ratio": 0.0}
             msg = "Dataset is empty. Cannot calculate feature ratio."
             if self.enable_warnings:
                 warnings.warn(msg)
@@ -802,7 +323,7 @@ class DatasetValidator:
 
         # Handle zero samples case
         if num_samples == 0:
-            details = {"ratio": 0}  # Zero samples means zero ratio# Convert to percentage as int
+            details = {"ratio": 0.0}  # Zero samples means zero ratio
             msg = "Dataset has zero samples. Cannot calculate feature ratio."
             if self.enable_warnings:
                 warnings.warn(msg)
@@ -814,7 +335,7 @@ class DatasetValidator:
 
         # Handle no features case
         if num_features == 0:
-            details = {"ratio": 0}
+            details = {"ratio": 0.0}
             msg = "No features found. Cannot calculate feature ratio."
             if self.enable_warnings:
                 warnings.warn(msg)
@@ -822,7 +343,7 @@ class DatasetValidator:
 
         # Step 3: Calculate and validate ratio
         ratio = num_features / num_samples
-        details = {"ratio": ratio}  # type: ignore  # Store actual ratio for validation
+        details = {"ratio": float(f"{ratio:.3f}")}  # Store actual ratio for validation, rounded to 3 decimal places
 
         if ratio > self.max_feature_ratio:
             msg = (
@@ -835,22 +356,8 @@ class DatasetValidator:
 
         return ValidationResult(True, None, details)
 
-    def _get_feature_columns(self, df: Union[SupportedTemporalDataFrame, FrameT]) -> List[str]:
-        """Get feature columns (all except time and target).
-
-        Features are defined as all columns except time_col and target_col.
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to get columns from
-
-        Returns
-        -------
-        List[str]
-            List of feature column names
-
-        """
+    def _get_feature_columns(self, df: FrameT) -> List[str]:
+        """Get feature columns (all except time and target)."""
         cols = df.columns
         if hasattr(cols, "collect"):
             cols = cols.collect()
@@ -858,107 +365,35 @@ class DatasetValidator:
             cols = cols.to_list()
         return [col for col in cols if col not in {self.time_col, self.target_col}]
 
-    def fit(self, df: Union[SupportedTemporalDataFrame, FrameT]) -> "DatasetValidator":
-        """Validate input DataFrame and prepare for validation checks.
-
-        This method ensures the input DataFrame meets basic validation requirements:
-
-        1. Validates DataFrame type and required columns
-        2. Ensures numeric columns and checks for null values
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to validate
-
-        Returns
-        -------
-        DatasetValidator
-            DatasetValidator instance for method chaining
-
-        Raises
-        ------
-        TypeError
-            If input is not a valid temporal DataFrame
-        ValueError
-            If columns are missing or invalid
-
-        Examples
-        --------
-        ```python
-        validator = DatasetValidator(time_col="time", target_col="target")
-        validator.fit(df)
-        ```
-
-        """
-
-        @nw.narwhalify
-        def validate_numeric(df: Union[SupportedTemporalDataFrame, FrameT]) -> FrameT:
-            """Validate that all columns except time are numeric.
-
-            Parameters
-            ----------
-            df: Union[SupportedTemporalDataFrame, FrameT] :
-
-            Returns
-            -------
-            FrameT
-
-            """
-            for col in df.columns:
-                if col != self.time_col:
-                    try:
-                        df.select([nw.col(col).cast(nw.Float64)])
-                    except Exception as e:
-                        raise ValueError(f"Column {col} must be numeric. Error: {str(e)}")
-            return df
-
-        @nw.narwhalify
-        def check_nulls(df: Union[SupportedTemporalDataFrame, FrameT], columns: List[str]) -> Dict[str, int]:
-            """Check for null values in specified columns.
-
-            Parameters
-            ----------
-            df: Union[SupportedTemporalDataFrame, FrameT] :
-            columns: List[str]
-
-            Returns
-            -------
-            Dict[str, int]
-
-            """
-            null_counts = {}
-            for col in columns:
-                null_count = df.select([nw.col(col).is_null().sum().cast(nw.Int64).alias("nulls")])
-                if hasattr(null_count, "collect"):
-                    null_count = null_count.collect()
-                value = null_count["nulls"][0]
-                if hasattr(value, "as_py"):
-                    value = value.as_py()
-                null_counts[col] = int(value)
-            return null_counts
-
-        # Step 1: Validate DataFrame type using core_utils
-        is_valid, df_type = is_valid_temporal_dataframe(df)
-        if not is_valid:
-            raise TypeError("Input must be a valid temporal DataFrame")
-
-        # Step 2: Get column names based on DataFrame type
-        cols = df.column_names if hasattr(df, "column_names") else df.columns
-        if hasattr(cols, "collect"):
-            cols = cols.collect()
-        if hasattr(cols, "to_list"):
-            cols = cols.to_list()
+    def fit(self, df: Union[Any, FrameT]) -> "DatasetValidator":
+        """Validate input DataFrame and prepare for validation checks."""
+        # Convert to Narwhals DataFrame
+        df = self._ensure_narwhals_df(df)
 
-        # Step 3: Validate required columns exist
-        if self.time_col not in cols or self.target_col not in cols:
+        # Validate required columns exist
+        if self.time_col not in df.columns or self.target_col not in df.columns:
             raise ValueError(f"Columns {self.time_col} and {self.target_col} must exist")
 
-        # Step 4: Validate numeric columns
-        validate_numeric(df)
-
-        # Step 5: Check nulls
-        null_counts = check_nulls(df, cols)
+        # Validate numeric columns
+        for col in df.columns:
+            if col != self.time_col:
+                try:
+                    df.select([nw.col(col).cast(nw.Float64)])
+                except Exception as e:
+                    raise ValueError(f"Column {col} must be numeric. Error: {str(e)}")
+
+        # Check nulls
+        null_counts = {}
+        for col in df.columns:
+            null_count = df.select([nw.col(col).is_null().sum().cast(nw.Int64).alias("nulls")])
+            if hasattr(null_count, "collect"):
+                null_count = null_count.collect()
+            value = null_count["nulls"][0]
+            if hasattr(value, "as_py"):
+                value = value.as_py()
+            null_counts[col] = int(value)
+
+        # Raise error if any nulls found
         null_columns = [col for col, count in null_counts.items() if count > 0]
         if null_columns:
             raise ValueError(f"Missing values detected in columns: {', '.join(null_columns)}")
@@ -966,53 +401,8 @@ class DatasetValidator:
         return self
 
     @nw.narwhalify
-    def transform(
-        self,
-        df: Union[SupportedTemporalDataFrame, FrameT],
-        target_col: Optional[str] = None,
-    ) -> Dict[str, ValidationResult]:
-        """Run configured validation checks on the DataFrame.
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to validate
-        target_col : Optional[str]
-            Column name for target-specific checks
-
-        target_col: Optional[str] :
-             (Default value = None)
-
-        Returns
-        -------
-        Dict[str, ValidationResult]
-
-        Examples
-        --------
-        ```python
-        import pandas as pd
-        from temporalscope.datasets import DatasetValidator
-
-        # Create sample data
-        df = pd.DataFrame({"feature1": range(5000), "target": range(5000)})
-
-        # Initialize and run validator
-        validator = DatasetValidator()
-        validator.fit(df)
-        results = validator.transform(df, target_col="target")
-
-        # Check results
-        for check, result in results.items():
-            print(f"{check}: {'Passed' if result.passed else 'Failed'}")
-        ```
-
-        Notes
-        -----
-        - Uses pure Narwhals operations
-        - Handles LazyFrame evaluation
-        - Returns detailed results (Dictionary of validation results for each check)
-
-        """
+    def transform(self, df: FrameT, target_col: Optional[str] = None) -> Dict[str, ValidationResult]:
+        """Run configured validation checks on the DataFrame."""
         # Execute validation checks
         results = {}
         check_names = ["sample_size", "feature_count", "feature_ratio", "feature_variability", "class_balance"]
@@ -1040,76 +430,12 @@ class DatasetValidator:
 
         return results
 
-    def fit_transform(
-        self,
-        df: Union[SupportedTemporalDataFrame, FrameT],
-        target_col: Optional[str] = None,
-    ) -> Dict[str, ValidationResult]:
-        """Fit the validator and run validation checks in one step.
-
-        Parameters
-        ----------
-        df : Union[SupportedTemporalDataFrame, FrameT]
-            DataFrame to validate
-        target_col : Optional[str]
-            Column name for target-specific checks
-
-        target_col: Optional[str] :
-             (Default value = None)
-
-        Returns
-        -------
-        Dict[str, ValidationResult]
-            Dictionary of validation results for each check
-
-        Raises
-        ------
-        TypeError
-            If input is not convertible to a Narwhals DataFrame
-
-        Examples
-        --------
-        ```python
-        import pandas as pd
-        from temporalscope.datasets import DatasetValidator
-
-        # Create sample data
-        df = pd.DataFrame({"feature1": range(5000), "target": range(5000)})
-
-        # Initialize and run validator
-        validator = DatasetValidator()
-        results = validator.fit_transform(df, target_col="target")
-
-        # Print report
-        validator.print_report(results)
-        ```
-
-        Notes
-        -----
-        - Combines fit() and transform()
-        - Validates input then runs checks
-        - Returns detailed results
-
-        """
+    def fit_transform(self, df: Union[Any, FrameT], target_col: Optional[str] = None) -> Dict[str, ValidationResult]:
+        """Fit the validator and run validation checks in one step."""
         return self.fit(df).transform(df, target_col)
 
     def print_report(self, results: Dict[str, ValidationResult]) -> None:
-        """Print validation results in a tabular format.
-
-        A simple utility function to display validation results in a readable format.
-        For production use cases, use the structured results directly from the
-        validation methods.
-
-        Parameters
-        ----------
-        results : Dict[str, ValidationResult]
-            Dictionary of validation results to report
-
-        Returns
-        -------
-        None
-
-        """
+        """Print validation results in a tabular format."""
         rows = []
         for check_name, result in results.items():
             status = "✓" if result.passed else "✗"
diff --git a/src/temporalscope/datasets/datasets.py b/src/temporalscope/datasets/datasets.py
index 8dfdf33..ebba9ed 100644
--- a/src/temporalscope/datasets/datasets.py
+++ b/src/temporalscope/datasets/datasets.py
@@ -15,12 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.
-# See the NOTICE file for additional information regarding copyright ownership.
-# The ASF licenses this file to you under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
-
 """TemporalScope/src/temporalscope/datasets/datasets.py
 
 Utility for loading datasets with multi-backend support. This class simplifies dataset loading, enabling
@@ -40,16 +34,14 @@ print(data.head())  # Example access
 ```
 """
 
-from typing import Any, Tuple
+from typing import Tuple
 
+import narwhals as nw
 import pandas as pd
+from narwhals.typing import FrameT
 from statsmodels.datasets import macrodata
 
-from temporalscope.core.core_utils import (
-    convert_to_backend,
-    is_valid_temporal_backend,
-    print_divider,
-)
+from temporalscope.core.core_utils import NARWHALS_BACKENDS, print_divider
 
 # Dictionary of available datasets and their loaders
 AVAILABLE_DATASETS = {
@@ -64,7 +56,6 @@ def _load_macrodata() -> Tuple[pd.DataFrame, str]:
     -------
     Tuple[pd.DataFrame, str]
         Preprocessed DataFrame and default target column 'realgdp'.
-
     """
     loaded_data = macrodata.load_pandas().data
     if loaded_data is None:
@@ -88,8 +79,6 @@ class DatasetLoader:
     ----------
     dataset_name : str
         Name of the dataset to load, as defined in AVAILABLE_DATASETS.
-
-
     """
 
     def __init__(self, dataset_name: str = "macrodata") -> None:
@@ -104,7 +93,6 @@ class DatasetLoader:
         ------
         ValueError
             if the specified dataset is not available.
-
         """
         if dataset_name not in AVAILABLE_DATASETS:
             raise ValueError(
@@ -119,7 +107,6 @@ class DatasetLoader:
         -------
         Tuple[pd.DataFrame, str]
             DataFrame and associated target column name.
-
         """
         print_divider()
         print(f"Loading dataset: '{self.dataset_name}'")
@@ -130,30 +117,32 @@ class DatasetLoader:
         print_divider()
         return dataset_df, target_col
 
-    def load_data(self, backend: str = "pandas") -> Any:
+    @nw.narwhalify
+    def load_data(self, backend: str = "pandas") -> FrameT:
         """Load the dataset and convert it to the specified backend format.
 
         Parameters
         ----------
         backend : str
             Backend to convert the dataset to. Default is 'pandas'.
-        backend: str :
-             (Default value = "pandas")
 
         Returns
         -------
-        Backend-specific DataFrame type (e.g., pandas.DataFrame, modin.DataFrame, polars.DataFrame)
+        FrameT
             Dataset in the specified backend format.
 
         Raises
         ------
         ValueError
             If the backend is unsupported.
-
         """
-        # Validate and load the dataset in pandas format
-        is_valid_temporal_backend(backend)
+        # Validate backend
+        backend = backend.lower()
+        if backend not in NARWHALS_BACKENDS:
+            raise ValueError(f"Backend '{backend}' is not supported. Available backends: {NARWHALS_BACKENDS}")
+
+        # Load dataset in pandas format
         df, _ = self._load_dataset_and_target()
 
-        # Convert to specified backend
-        return convert_to_backend(df, backend)
+        # Convert to Narwhals DataFrame
+        return nw.from_native(df)
diff --git a/src/temporalscope/datasets/synthetic_data_generator.py b/src/temporalscope/datasets/synthetic_data_generator.py
index 66a77c8..370a6c2 100644
--- a/src/temporalscope/datasets/synthetic_data_generator.py
+++ b/src/temporalscope/datasets/synthetic_data_generator.py
@@ -17,94 +17,27 @@
 
 """TemporalScope/src/temporalscope/datasets/synthetic_data_generator.py
 
-This module provides utility functions for generating synthetic time series data, specifically designed to facilitate
-unit testing and benchmarking of various components across the TemporalScope ecosystem. The generated data simulates
-real-world time series with configurable features, ensuring comprehensive coverage of test cases for various modes, such
-as single-step and multi-step target data handling. This module is intended for use within automated test pipelines, and
-it plays a critical role in maintaining code stability and robustness across different test suites.
+This module provides utilities for generating synthetic time series data specifically for testing
+and validation purposes. While TemporalScope uses Narwhals for backend-agnostic operations, this
+generator serves as a defensive programming tool to ensure:
 
-Core Purpose:
--------------
-The `synthetic_data_generator` is a centralized utility for creating synthetic time series data that enables robust testing
-of TemporalScope's core modules. It supports the generation of data for various modes, ensuring that TemporalScope modules
-can handle a wide range of time series data, including edge cases and anomalies.
+1. Runtime Testing: Generate test data across different DataFrame backends to verify behavior
+2. Edge Case Coverage: Create data with nulls, NaNs, and various data types
+3. Backend Validation: Test Narwhals operations with different DataFrame implementations
 
-Supported Use Cases:
----------------------
-- Single-step mode: Generates scalar target values for tasks where each row represents a single time step.
-- Multi-step mode: Produces input-output sequence data for sequence forecasting, where input sequences (`X`) and output
-  sequences (`Y`) are handled as part of a unified dataset but with vectorized targets.
+The generator creates consistent test data that matches the TimeFrame API's expected structure
+(see core_utils.py for data structure details). This helps maintain code stability by providing
+reliable test data that works across all supported backends.
 
-Notes
------
-- **Batch size**: This package assumes no default batch size; batch size is typically managed by the data loader (e.g.,
- TensorFlow `DataLoader`, PyTorch `DataLoader`). The synthetic data generator provides the raw data structure, which is
- then partitioned and batched as needed in downstream pipelines (e.g., after target shifting or partitioning).
-
-- **TimeFrame and Target Shape**: The TemporalScope framework checks if the target is scalar or vector (sequence). The
- generated data in multi-step mode follows a unified structure, with the target represented as a sequence in the same
- DataFrame. This ensures compatibility with popular machine learning libraries that are compatible with SHAP, LIME, and
- other explainability methods.
-
-
-See Also
---------
-For further details on the single-step and multi-step modes, refer to the core TemporalScope documentation on data handling.
-
-Example Visualization:
-----------------------
-Here is a visual demonstration of the datasets generated for single-step and multi-step modes, including the shape
-of input (`X`) and target (`Y`) data compatible with most popular ML frameworks like TensorFlow, PyTorch, and SHAP.
-
-**Single-step mode**:
-
-| Time       | Feature 1 | Feature 2 | Feature 3 | Target  |
-|------------|-----------|-----------|-----------|---------|
-| 2023-01-01 | 0.15      | 0.67      | 0.89      | 0.33    |
-| 2023-01-02 | 0.24      | 0.41      | 0.92      | 0.28    |
-
-
-Shape:
-
-- `X`: (num_samples, num_features)
-- `Y`: (num_samples, 1)
-
-
-**Multi-step mode (with vectorized targets)**:
-
-| Time       | Feature 1 | Feature 2 | Feature 3 | Target      |
-|------------|-----------|-----------|-----------|-------------|
-| 2023-01-01 | 0.15      | 0.67      | 0.89      | [0.3, 0.4]  |
-| 2023-01-02 | 0.24      | 0.41      | 0.92      | [0.5, 0.6]  |
-
-
-Shape:
-
-- `X`: (num_samples, num_features)
-- `Y`: (num_samples, sequence_length)
-
-Examples
---------
-```python
-from temporalscope.core.core_utils import MODE_SINGLE_TARGET, MODE_MULTI_TARGET
-from temporalscope.datasets.synthetic_data_generator import create_sample_data
-
-# Generating data for single-step mode
-df = create_sample_data(num_samples=100, num_features=3, mode=MODE_SINGLE_TARGET)
-print(df.head())  # Shows the generated data with features and a scalar target.
-
-# Generating data for multi-step mode
-df = create_sample_data(num_samples=100, num_features=3, mode=MODE_MULTI_TARGET)
-print(df.head())  # Shows the generated input sequence (`X`) and target sequence (`Y`).
-```
+Note: This module is primarily intended for testing purposes, not for production data generation.
 
 """
 
-import dask.dataframe as dd
+import narwhals as nw
 import numpy as np
 import pandas as pd
-
-from temporalscope.core.core_utils import SupportedTemporalDataFrame, convert_to_backend, is_valid_temporal_backend
+from narwhals.typing import FrameT
+from narwhals.utils import Implementation
 
 # Set random seed for reproducibility for unit tests
 RANDOM_SEED = 100
@@ -113,32 +46,21 @@ RANDOM_SEED = 100
 def _apply_nulls_nans_single_row(df: pd.DataFrame, feature_cols: list[str], with_nulls: bool, with_nans: bool) -> None:
     """Apply nulls/nans to a single row DataFrame using pandas operations.
 
-    This is an internal utility function that operates on pandas DataFrames directly for efficiency
-    and simplicity in null/nan application. The main function handles conversion to other backends.
-
     Parameters
     ----------
     df : pd.DataFrame
-        Pandas DataFrame to modify (modified in-place)
+        Single row DataFrame to modify
     feature_cols : list[str]
         List of feature column names to apply nulls/nans to
     with_nulls : bool
-        Whether to apply null values
+        If True, apply None values to feature columns
     with_nans : bool
-        Whether to apply NaN values (only if with_nulls is False)
-    df: pd.DataFrame :
-
-    feature_cols: list[str] :
-
-    with_nulls: bool :
-
-    with_nans: bool :
-
-
-    Returns
-    -------
-    None
+        If True and with_nulls is False, apply NaN values to feature columns
 
+    Notes
+    -----
+    - Modifies DataFrame in-place
+    - If both with_nulls and with_nans are True, nulls take precedence
     """
     if with_nulls:
         df.iloc[0, df.columns.get_indexer(feature_cols)] = None
@@ -157,50 +79,28 @@ def _apply_nulls_nans_multi_row(
 ) -> None:
     """Apply nulls/nans to multiple rows in a DataFrame using pandas operations.
 
-    This is an internal utility function that operates on pandas DataFrames directly for efficiency
-    and simplicity in null/nan application. The main function handles conversion to other backends.
-
-    For nulls and nans:
-    - Ensures at least 1 row is affected if enabled
-    - Respects maximum available rows
-    - Prevents overlap between null and nan rows
-    - Uses random selection for realistic data generation
-
     Parameters
     ----------
     df : pd.DataFrame
-        Pandas DataFrame to modify (modified in-place)
+        DataFrame to modify
     feature_cols : list[str]
         List of feature column names to apply nulls/nans to
     with_nulls : bool
-        Whether to apply null values
+        If True, apply None values to feature columns
     with_nans : bool
-        Whether to apply NaN values
+        If True, apply NaN values to feature columns
     null_percentage : float
         Percentage of rows to contain null values (0.0 to 1.0)
     nan_percentage : float
         Percentage of rows to contain NaN values (0.0 to 1.0)
     num_samples : int
-        Total number of rows in the DataFrame
-    df: pd.DataFrame :
-
-    feature_cols: list[str] :
-
-    with_nulls: bool :
-
-    with_nans: bool :
-
-    null_percentage: float :
-
-    nan_percentage: float :
-
-    num_samples: int :
-
-
-    Returns
-    -------
-    None
+        Total number of rows in DataFrame
 
+    Notes
+    -----
+    - Modifies DataFrame in-place
+    - Ensures at least one row has nulls/nans if enabled
+    - NaNs are only applied to rows that don't already have nulls
     """
     null_indices = []
     if with_nulls:
@@ -223,74 +123,112 @@ def _apply_nulls_nans_multi_row(
             df.iloc[nan_indices, df.columns.get_indexer(feature_cols)] = np.nan
 
 
+def _validate_synthetic_data_params(
+    backend: str, num_samples: int, num_features: int, mode: str, null_percentage: float, nan_percentage: float
+) -> None:
+    """Validate parameters for synthetic data generation.
+
+    Parameters
+    ----------
+    backend : str
+        Backend to use for generated data
+    num_samples : int
+        Number of samples (rows) to generate
+    num_features : int
+        Number of feature columns to generate
+    mode : str
+        Mode for data generation
+    null_percentage : float
+        Percentage of rows to contain null values
+    nan_percentage : float
+        Percentage of rows to contain NaN values
+
+    Raises
+    ------
+    ValueError
+        If any parameters are invalid
+    """
+    if backend.lower() not in [impl.name.lower() for impl in Implementation]:
+        raise ValueError(f"Backend '{backend}' is not supported by Narwhals.")
+
+    if num_samples < 0 or num_features < 0:
+        raise ValueError("`num_samples` and `num_features` must be non-negative.")
+    if mode != "single_target":
+        raise ValueError(f"Unsupported mode: {mode}. Only 'single_target' mode is supported.")
+    if not 0.0 <= null_percentage <= 1.0:
+        raise ValueError("null_percentage must be between 0.0 and 1.0")
+    if not 0.0 <= nan_percentage <= 1.0:
+        raise ValueError("nan_percentage must be between 0.0 and 1.0")
+
+
+@nw.narwhalify
 def generate_synthetic_time_series(
     backend: str,
+    *,  # Force keyword arguments for better readability
     num_samples: int = 100,
     num_features: int = 3,
     with_nulls: bool = False,
     with_nans: bool = False,
-    null_percentage: float = 0.05,  # Default 5% nulls
-    nan_percentage: float = 0.05,  # Default 5% nans
+    null_percentage: float = 0.05,
+    nan_percentage: float = 0.05,
     mode: str = "single_target",
     time_col_numeric: bool = False,
     drop_time: bool = False,
     random_seed: int = RANDOM_SEED,
-) -> SupportedTemporalDataFrame:
-    """
-    Generate synthetic time series data with specified backend support and configurations.
+) -> FrameT:
+    """Generate synthetic time series data with specified backend support and configurations.
 
     Parameters
     ----------
     backend : str
-        The backend to use for the generated data.
+        Backend to use for generated data (must be supported by Narwhals)
     num_samples : int, optional
-        Number of samples (rows) to generate in the time series data. Default is 100.
+        Number of samples (rows) to generate, by default 100
     num_features : int, optional
-        Number of feature columns to generate in addition to 'time' and 'target' columns. Default is 3.
+        Number of feature columns to generate, by default 3
     with_nulls : bool, optional
-        Whether to introduce None values in feature columns. Default is False.
+        Whether to introduce None values in feature columns, by default False
     with_nans : bool, optional
-        Whether to introduce NaN values in feature columns. Default is False.
+        Whether to introduce NaN values in feature columns, by default False
     null_percentage : float, optional
-        Percentage of rows to contain null values (0.0 to 1.0). Only used if `with_nulls` is True.
-        - For datasets with few rows, ensures at least one row is affected if nulls are enabled.
-        - For single-row datasets, nulls take precedence over NaNs if both are enabled.
-        Default is 0.05 (5%).
+        Percentage of rows to contain null values (0.0 to 1.0), by default 0.05
     nan_percentage : float, optional
-        Percentage of rows to contain NaN values (0.0 to 1.0). Only used if `with_nans` is True.
-        - For datasets with few rows, ensures at least one row is affected if NaNs are enabled.
-        - For single-row datasets, nulls take precedence over NaNs if both are enabled.
-        Default is 0.05 (5%).
+        Percentage of rows to contain NaN values (0.0 to 1.0), by default 0.05
     mode : str, optional
-        Mode for data generation. Currently, only 'single_target' is supported. Default is 'single_target'.
+        Mode for data generation, by default "single_target"
     time_col_numeric : bool, optional
-        If True, the 'time' column is numeric instead of a datetime object. Default is False.
+        If True, time column is numeric instead of datetime, by default False
     drop_time : bool, optional
-        If True, the time column is omitted from the output DataFrame. Default is False.
+        If True, time column is omitted from output, by default False
     random_seed : int, optional
-        Seed for random number generation to ensure reproducible results. Default is `RANDOM_SEED`.
+        Seed for random number generation, by default RANDOM_SEED
 
     Returns
     -------
-    SupportedTemporalDataFrame
-        DataFrame or table in the specified backend containing the generated synthetic data.
+    FrameT
+        Narwhals DataFrame containing generated synthetic data
 
     Raises
     ------
     ValueError
-        If an unsupported backend, mode, or invalid parameters are specified.
-
+        If backend not supported by Narwhals
+        If invalid mode specified (only "single_target" supported)
+        If invalid parameters (negative samples/features, invalid percentages)
+
+    Notes
+    -----
+    - For datasets with few rows, ensures at least one row has nulls/NaNs if enabled
+    - For single-row datasets, nulls take precedence over NaNs if both enabled
+    - Time column can be numeric (timestamps) or datetime based on time_col_numeric
     """
-    is_valid_temporal_backend(backend)
-
-    if num_samples < 0 or num_features < 0:
-        raise ValueError("`num_samples` and `num_features` must be non-negative.")
-    if mode != "single_target":
-        raise ValueError(f"Unsupported mode: {mode}. Only 'single_target' mode is supported.")
-    if not 0.0 <= null_percentage <= 1.0:
-        raise ValueError("null_percentage must be between 0.0 and 1.0")
-    if not 0.0 <= nan_percentage <= 1.0:
-        raise ValueError("nan_percentage must be between 0.0 and 1.0")
+    _validate_synthetic_data_params(
+        backend=backend,
+        num_samples=num_samples,
+        num_features=num_features,
+        mode=mode,
+        null_percentage=null_percentage,
+        nan_percentage=nan_percentage,
+    )
 
     np.random.seed(random_seed)
 
@@ -320,8 +258,32 @@ def generate_synthetic_time_series(
                 df, feature_cols, with_nulls, with_nans, null_percentage, nan_percentage, num_samples
             )
 
-    result = convert_to_backend(df, backend)
-    if isinstance(result, dd.DataFrame):
-        result = result.persist()
+    # Convert to Narwhals DataFrame and transform
+    df_nw = nw.from_native(df)
+
+    # Following Pattern 1 from notebook: proper column selection with nw.col() and alias()
+    result = df_nw.select(
+        [
+            # Time column if present
+            *([nw.col("time").alias("time")] if not drop_time else []),
+            # Target column (always present)
+            nw.col("target").alias("target"),
+            # Feature columns
+            *[nw.col(f"feature_{i+1}").alias(f"feature_{i+1}") for i in range(num_features)],
+        ]
+    )
+
+    # Convert to requested backend
+    if backend.lower() != "pandas":
+        # First wrap in Narwhals to use its conversion capabilities
+        df_nw = nw.from_native(result)
+        # Then convert to the target backend using the backend's native module
+        if backend.lower() == "polars":
+            import polars as pl
+
+            result = pl.from_pandas(df_nw.to_native())
+        else:
+            # For other backends, let Narwhals handle the conversion
+            result = df_nw.to_native()
 
     return result
diff --git a/src/temporalscope/partition/single_target/padding/functional.py b/src/temporalscope/partition/single_target/padding/functional.py
index a8e4221..a9afd49 100644
--- a/src/temporalscope/partition/single_target/padding/functional.py
+++ b/src/temporalscope/partition/single_target/padding/functional.py
@@ -82,16 +82,17 @@ series clustering: a comparative study. Data Mining and Knowledge Discovery.
 """
 
 import narwhals as nw
+from narwhals.typing import FrameT
 
-from temporalscope.core.core_utils import SupportedTemporalDataFrame, check_dataframe_nulls_nans, is_lazy_evaluation
+from temporalscope.core.core_utils import count_dataframe_column_nulls, is_dataframe_empty
 
 
 @nw.narwhalify
 def mean_fill_pad(
-    df: SupportedTemporalDataFrame,
+    df: FrameT,
     target_len: int,
     padding: str = "post",
-) -> SupportedTemporalDataFrame:
+) -> FrameT:
     """Pad a DataFrame to target length by filling with column means.
 
     A simple padding function that extends a DataFrame to a target length by adding
@@ -99,32 +100,25 @@ def mean_fill_pad(
 
     Parameters
     ----------
-    df :
+    df : FrameT
         DataFrame to pad
-    target_len :
+    target_len : int
         Desired length after padding
-    padding :
+    padding : str
         Where to add padding ('pre' or 'post')
-    df: SupportedTemporalDataFrame :
-
-    target_len: int :
-
-    padding: str :
-         (Default value = "post")
 
     Returns
     -------
-    type
+    FrameT
         Padded DataFrame
 
     Raises
     ------
     ValueError
         If target_len <= current length or invalid padding direction
-
     """
     # Validate data quality first
-    null_counts = check_dataframe_nulls_nans(df, df.columns)
+    null_counts = count_dataframe_column_nulls(df, list(df.columns))
     if any(count > 0 for count in null_counts.values()):
         raise ValueError("Cannot process data containing null values")
 
@@ -136,7 +130,7 @@ def mean_fill_pad(
     count_expr = nw.col(df.columns[0]).count().cast(nw.Int64).alias("count")
     count_result = df.select([count_expr])
 
-    if is_lazy_evaluation(count_result):
+    if is_dataframe_empty(count_result):
         current_len = count_result.collect()["count"][0]
     else:
         current_len = count_result["count"][0]
@@ -155,7 +149,7 @@ def mean_fill_pad(
         mean_expr = nw.col(col).mean().cast(nw.Float64).alias("mean")
         mean_result = df.select([mean_expr])
 
-        mean_val = mean_result.collect()["mean"][0] if is_lazy_evaluation(mean_result) else mean_result["mean"][0]
+        mean_val = mean_result.collect()["mean"][0] if is_dataframe_empty(mean_result) else mean_result["mean"][0]
 
         # Handle PyArrow scalar for mean values too
         if hasattr(mean_val, "as_py"):
@@ -169,7 +163,7 @@ def mean_fill_pad(
     # Calculate number of padding rows needed
     padding_count = target_len - current_len
 
-    if is_lazy_evaluation(padding_rows):
+    if is_dataframe_empty(padding_rows):
         # For lazy evaluation backends (e.g. dask):
         # 1. Create a single-row DataFrame with scalar values
         # 2. Use dask's native concat for proper lazy evaluation
diff --git a/src/temporalscope/target_shifters/single_step.py b/src/temporalscope/target_shifters/single_step.py
index fbf5048..3183f41 100644
--- a/src/temporalscope/target_shifters/single_step.py
+++ b/src/temporalscope/target_shifters/single_step.py
@@ -79,12 +79,9 @@ from typing import Optional, Union
 import narwhals as nw
 import numpy as np
 import pandas as pd
+from narwhals.typing import FrameT
 
-from temporalscope.core.core_utils import (
-    MODE_SINGLE_TARGET,
-    SupportedTemporalDataFrame,
-    is_valid_temporal_dataframe,
-)
+from temporalscope.core.core_utils import MODE_SINGLE_TARGET
 from temporalscope.core.temporal_data_loader import TimeFrame
 
 
@@ -232,23 +229,20 @@ class SingleStepTargetShifter:
         return int(value)
 
     @nw.narwhalify
-    def _get_row_count(self, df: SupportedTemporalDataFrame, check_empty: bool = True) -> int:
+    def _get_row_count(self, df: FrameT, check_empty: bool = True) -> int:
         """Get row count using Narwhals operations.
 
         Parameters
         ----------
-        df : SupportedTemporalDataFrame
+        df : FrameT
             DataFrame to count rows for
         check_empty : bool
             Whether to raise "Cannot transform empty DataFrame" error
-        df: SupportedTemporalDataFrame :
-
-        check_empty: bool :
-             (Default value = True)
 
         Returns
         -------
         int
+            Number of rows in DataFrame
 
         Notes
         -----
@@ -256,8 +250,7 @@ class SingleStepTargetShifter:
         - nw.Int64 for scalar type conversion
         - collect() for scalar access
         - Handles LazyFrame and PyArrow scalars
-        - Controls empty DataFrame error handling based on check_empty parameter Number of rows in DataFrame
-
+        - Controls empty DataFrame error handling based on check_empty parameter
         """
         try:
             result = df.select([nw.col(df.columns[0]).count().cast(nw.Int64).alias("count")])
@@ -270,27 +263,25 @@ class SingleStepTargetShifter:
             raise e
 
     @nw.narwhalify
-    def _shift_target(self, df: SupportedTemporalDataFrame) -> SupportedTemporalDataFrame:
+    def _shift_target(self, df: FrameT) -> FrameT:
         """Shift target column using Narwhals operations.
 
         Parameters
         ----------
-        df : SupportedTemporalDataFrame
+        df : FrameT
             DataFrame to transform
-        df: SupportedTemporalDataFrame :
-
 
         Returns
         -------
-        SupportedTemporalDataFrame
+        FrameT
+            DataFrame with shifted target column
 
         Notes
         -----
         Uses Narwhals operations for backend-agnostic shifting:
         - with_columns() for adding shifted column
         - filter() for removing null values
-        - drop() for removing original target DataFrame with shifted target
-
+        - drop() for removing original target
         """
         if self.target_col not in df.columns:
             raise ValueError("target_col must be set before transform (call fit first)")
@@ -309,21 +300,20 @@ class SingleStepTargetShifter:
 
         return shifted_df
 
-    def fit(self, X: Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray], y=None) -> "SingleStepTargetShifter":
+    def fit(self, X: Union[TimeFrame, FrameT, np.ndarray], y=None) -> "SingleStepTargetShifter":
         """Validate inputs and prepare for transformation.
 
         This method handles all input validation before any Narwhals operations:
         - TimeFrame: Uses existing target_col
-        - DataFrame: Validates using is_valid_temporal_dataframe
+        - DataFrame: Validates using Narwhals operations
         - numpy array: Converts to DataFrame first
 
         Parameters
         ----------
-        X : Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray]
+        X : Union[TimeFrame, FrameT, np.ndarray]
             Input data to validate
-        y :
-            Ignored, exists for scikit-learn compatibility (Default value = None)
-        X: Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray] :
+        y : Any, optional
+            Ignored, exists for scikit-learn compatibility
 
         Returns
         -------
@@ -334,6 +324,7 @@ class SingleStepTargetShifter:
         ------
         ValueError
             If target_col not set and cannot be inferred
+            If target_col does not exist in DataFrame
         TypeError
             If input type is not supported
 
@@ -371,7 +362,6 @@ class SingleStepTargetShifter:
         - No Narwhals operations in fit()
         - Validates before any transformations
         - Handles all input types consistently
-
         """
         if isinstance(X, TimeFrame):
             self.target_col = X._target_col
@@ -384,10 +374,11 @@ class SingleStepTargetShifter:
             if self.target_col is None:
                 self.target_col = target_col
         else:
-            # Get native DataFrame for validation
-            native_df = X.to_native() if hasattr(X, "to_native") else X
-            if not is_valid_temporal_dataframe(native_df):
-                raise TypeError(f"Input type {type(X)} is not a supported DataFrame type")
+            # Convert to Narwhals DataFrame
+            df = nw.from_native(X)
+            # Validate target column exists
+            if self.target_col not in df.columns:
+                raise ValueError(f"Column '{self.target_col}' does not exist in DataFrame")
 
         if self.target_col is None:
             raise ValueError("target_col must be set before transform (call fit first)")
@@ -395,9 +386,7 @@ class SingleStepTargetShifter:
         return self
 
     @nw.narwhalify
-    def transform(
-        self, X: Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray], y=None
-    ) -> Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray]:
+    def transform(self, X: Union[TimeFrame, FrameT, np.ndarray], y=None) -> Union[TimeFrame, FrameT, np.ndarray]:
         """Transform input data using Narwhals operations.
 
         This method assumes inputs are already validated by fit() and uses pure
@@ -405,44 +394,44 @@ class SingleStepTargetShifter:
 
         Parameters
         ----------
-        X : Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray]
+        X : Union[TimeFrame, FrameT, np.ndarray]
             Input data to transform
-        y :
-            Ignored, exists for scikit-learn compatibility (Default value = None)
-        X: Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray] :
+        y : Any, optional
+            Ignored, exists for scikit-learn compatibility
 
         Returns
         -------
-        Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray]
+        Union[TimeFrame, FrameT, np.ndarray]
+            Transformed data with shifted target column
 
         Examples
         --------
         ```python
-          import pandas as pd
-          from temporalscope.target_shifters.single_step import SingleStepTargetShifter
-          from temporalscope.core.temporal_data_loader import TimeFrame
-
-          # Create TimeFrame
-          df = pd.DataFrame({"time": range(5), "target": range(5), "feature": range(5)})
-          tf = TimeFrame(df=df, time_col="time", target_col="target")
-
-          # Initialize and transform
-          shifter = SingleStepTargetShifter(n_lags=1)
-          shifter.fit(tf)
-          transformed_tf = shifter.transform(tf)
+        import pandas as pd
+        from temporalscope.target_shifters.single_step import SingleStepTargetShifter
+        from temporalscope.core.temporal_data_loader import TimeFrame
+
+        # Create TimeFrame
+        df = pd.DataFrame({"time": range(5), "target": range(5), "feature": range(5)})
+        tf = TimeFrame(df=df, time_col="time", target_col="target")
+
+        # Initialize and transform
+        shifter = SingleStepTargetShifter(n_lags=1)
+        shifter.fit(tf)
+        transformed_tf = shifter.transform(tf)
         ```
 
         ```python
-          import pandas as pd
-          from temporalscope.target_shifters.single_step import SingleStepTargetShifter
+        import pandas as pd
+        from temporalscope.target_shifters.single_step import SingleStepTargetShifter
 
-          # Create DataFrame
-          df = pd.DataFrame({"target": range(5), "feature": range(5)})
+        # Create DataFrame
+        df = pd.DataFrame({"target": range(5), "feature": range(5)})
 
-          # Initialize and transform
-          shifter = SingleStepTargetShifter(target_col="target")
-          shifter.fit(df)
-          transformed_df = shifter.transform(df)
+        # Initialize and transform
+        shifter = SingleStepTargetShifter(target_col="target")
+        shifter.fit(df)
+        transformed_df = shifter.transform(df)
         ```
 
         Notes
@@ -451,8 +440,7 @@ class SingleStepTargetShifter:
         - _get_row_count() for counting
         - _shift_target() for shifting
         - Backend-agnostic operations
-        - Handles LazyFrame and PyArrow scalars transformed data
-
+        - Handles LazyFrame and PyArrow scalars
         """
         was_numpy = isinstance(X, np.ndarray)
         if was_numpy:
@@ -488,7 +476,6 @@ class SingleStepTargetShifter:
                 transformed,
                 time_col=X._time_col,
                 target_col=f"{self.target_col}_shift_{self.n_lags}",
-                dataframe_backend=X.backend,
                 mode=self.mode,
                 ascending=X.ascending,
             )
@@ -501,9 +488,7 @@ class SingleStepTargetShifter:
 
         return transformed
 
-    def fit_transform(
-        self, X: Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray], y=None
-    ) -> Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray]:
+    def fit_transform(self, X: Union[TimeFrame, FrameT, np.ndarray], y=None) -> Union[TimeFrame, FrameT, np.ndarray]:
         """Fit the transformer and transform the input data.
 
         This method combines input validation (fit) with Narwhals transformations
@@ -511,14 +496,15 @@ class SingleStepTargetShifter:
 
         Parameters
         ----------
-        X : Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray]
+        X : Union[TimeFrame, FrameT, np.ndarray]
             Input data to transform
-        y :
-            Ignored, exists for scikit-learn compatibility (Default value = None)
+        y : Any, optional
+            Ignored, exists for scikit-learn compatibility
 
         Returns
         -------
-        Union[TimeFrame, SupportedTemporalDataFrame, np.ndarray]
+        Union[TimeFrame, FrameT, np.ndarray]
+            Transformed data with shifted target column
 
         Examples
         --------
@@ -536,8 +522,6 @@ class SingleStepTargetShifter:
         transformed_tf = shifter.fit_transform(tf)
         ```
 
-        Examples
-        --------
         ```python
         import pandas as pd
         from temporalscope.target_shifters.single_step import SingleStepTargetShifter
@@ -555,7 +539,6 @@ class SingleStepTargetShifter:
         Operation Flow:
         1. fit(): Validates inputs
         2. transform(): Pure Narwhals operations
-        3. Handles all backend types consistently transformed data
-
+        3. Handles all backend types consistently
         """
         return self.fit(X).transform(X)
diff --git a/tutorial_notebooks/introduction/0_narhwals_backend.ipynb b/tutorial_notebooks/introduction/0_narhwals_backend.ipynb
deleted file mode 100644
index 304adb1..0000000
--- a/tutorial_notebooks/introduction/0_narhwals_backend.ipynb
+++ /dev/null
@@ -1,1224 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# TemporalScope Tutorial: Backend-Agnostic Functions Using Narwhals\n",
-    "\n",
-    "### Purpose\n",
-    "This tutorial demonstrates how **TemporalScope** can leverage **Narwhals** to support backend-agnostic data operations. By building backend-agnostic functions, TemporalScope enables compatibility across multiple popular data processing libraries including **Pandas**, **Modin**, **Polars**, and **PyArrow**.\n",
-    "\n",
-    "### Key Steps\n",
-    "\n",
-    "1. **Create a Sample Dataset**: We begin by creating a synthetic dataset in Pandas, which we will transform and test for compatibility across Modin, Polars, and PyArrow.\n",
-    "2. **Implement a Narwhals-Decorated Function**: With Narwhals’ `@narwhalify` decorator, we can create a backend-agnostic function that performs simple operations like aggregation or column transformations without needing to rewrite the logic for each backend.\n",
-    "3. **Run Compatibility Tests**: Finally, we test the function across all supported backends to verify smooth execution across Pandas, Modin, Polars, and PyArrow.\n",
-    "\n",
-    "### Supported TemporalScope Backends\n",
-    "The TemporalScope core API is designed to be compatible with a wide range of popular DataFrame backends. Here are the currently supported backends:\n",
-    "\n",
-    "- **Pandas**: General-purpose data processing in Python, compatible with Narwhals.\n",
-    "- **Modin**: Parallelized Pandas-like library for distributed data processing.\n",
-    "- **Polars**: Rust-based, highly efficient DataFrame library for analytics.\n",
-    "- **PyArrow**: Apache Arrow-based DataFrame supporting large, in-memory data processing.\n",
-    "- **Dask**: Distributed DataFrame library for parallel computation on large datasets.\n",
-    "\n",
-    "These backends allow TemporalScope users to scale or optimize their workflows without modifying code.\n",
-    "\n",
-    "### Advantages of Using Narwhals\n",
-    "\n",
-    "- **Uniform API**: The `@narwhalify` decorator from Narwhals creates a seamless backend-neutral execution environment, allowing developers to use the same function across multiple DataFrame libraries.\n",
-    "- **Enhanced Compatibility**: Narwhals optimizes how data is accessed and manipulated across backends, ensuring the syntax and functions used are supported by each compatible backend.\n",
-    "- **Simplified Codebase**: By using Narwhals for backend-agnostic functions, TemporalScope’s core logic can remain generalized, reducing code duplication and maintenance.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example 0: Backend Compatibility with TemporalScope and Narwhals\n",
-    "\n",
-    "This example demonstrates a basic setup to ensure compatibility across various DataFrame backends using TemporalScope’s backend utilities and Narwhals.\n",
-    "\n",
-    "1. **Check Supported Backends**:\n",
-    "   - Retrieve and validate the supported backends with `get_temporalscope_backends()` and `is_valid_temporal_backend()` for `pandas`, `modin`, `polars`, and `pyarrow`.\n",
-    "2. **Run Narwhals-Compatible Operation**:\n",
-    "   - Define a backend-agnostic function with `@narwhalify` to aggregate basic column statistics, showcasing Narwhals’ compatibility layer across supported backends.\n",
-    "3. **Compare Results Across Backends**:\n",
-    "- Execute the Narwhals-compatible function on DataFrames from different backends and compare the results to ensure consistency across implementations.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original DataFrame Type: <class 'pandas.core.frame.DataFrame'>\n",
-      "Result of Narwhals operation:\n",
-      "    feature_1_sum  feature_2_mean\n",
-      "0      96.801247        0.067414\n",
-      "\n",
-      "== TemporalScope and Narwhals Backend Checks ==\n",
-      "Supported TemporalScope Backends: ['pandas', 'modin', 'pyarrow', 'polars', 'dask']\n",
-      "Supported Narwhals Backends: ['pandas', 'modin', 'cudf', 'pyarrow', 'polars', 'dask', 'unknown']\n",
-      "Pandas backend validation: FAILED name 'is_valid_temporal_backend' is not defined\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import narwhals as nw\n",
-    "from temporalscope.core.core_utils import (\n",
-    "    get_temporalscope_backends,\n",
-    "    get_narwhals_backends,\n",
-    "    is_valid_temporal_backend,\n",
-    ")\n",
-    "from narwhals.typing import FrameT\n",
-    "\n",
-    "# Constants\n",
-    "NUM_ROWS = 200\n",
-    "SEED = 42\n",
-    "\n",
-    "# Step 1: Generate sample time-series data in Pandas\n",
-    "np.random.seed(SEED)\n",
-    "date_range = pd.date_range(start=\"2023-01-01\", periods=NUM_ROWS, freq=\"D\")\n",
-    "data = pd.DataFrame({\n",
-    "    \"datetime\": date_range,\n",
-    "    \"feature_1\": np.random.rand(NUM_ROWS),\n",
-    "    \"feature_2\": np.random.randn(NUM_ROWS)\n",
-    "})\n",
-    "\n",
-    "# Step 2: Define a Narwhals-compatible function using @narwhalify\n",
-    "@nw.narwhalify\n",
-    "def test_narwhal_conversion(df: FrameT) -> FrameT:\n",
-    "    \"\"\"Perform Narwhals operations on a compatible DataFrame.\"\"\"\n",
-    "    return df.select(\n",
-    "        feature_1_sum=nw.col(\"feature_1\").sum(),\n",
-    "        feature_2_mean=nw.col(\"feature_2\").mean()\n",
-    "    )\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    # Run the Narwhals operation\n",
-    "    print(\"Original DataFrame Type:\", type(data))\n",
-    "    result_df = test_narwhal_conversion(data)\n",
-    "    print(\"Result of Narwhals operation:\\n\", result_df)\n",
-    "    \n",
-    "    # Step 3: Check backend support using TemporalScope core_utils\n",
-    "    print(\"\\n== TemporalScope and Narwhals Backend Checks ==\")\n",
-    "    temporalscope_backends = get_temporalscope_backends()\n",
-    "    print(\"Supported TemporalScope Backends:\", temporalscope_backends)\n",
-    "\n",
-    "    narwhals_backends = get_narwhals_backends()\n",
-    "    print(\"Supported Narwhals Backends:\", narwhals_backends)\n",
-    "\n",
-    "    # Validate if 'pandas' backend is supported\n",
-    "    try:\n",
-    "        is_valid_temporal_backend('pandas')\n",
-    "        print(\"Pandas backend validation: PASSED\")\n",
-    "    except Exception as e:\n",
-    "        print(\"Pandas backend validation: FAILED\", e)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "=== Testing Pandas Backend ===\n",
-      "Original DataFrame Backend: pandas\n",
-      "Is 'datetime' timestamp-like? True\n",
-      "\n",
-      "Result of Narwhals operation:\n",
-      "    feature_1_sum  feature_2_mean\n",
-      "0      47.018074        -0.00108\n",
-      "\n",
-      "=== Testing Polars Backend ===\n",
-      "Original DataFrame Backend: polars\n",
-      "Is 'datetime' timestamp-like? True\n",
-      "\n",
-      "Result of Narwhals operation:\n",
-      " shape: (1, 2)\n",
-      "┌───────────────┬────────────────┐\n",
-      "│ feature_1_sum ┆ feature_2_mean │\n",
-      "│ ---           ┆ ---            │\n",
-      "│ f64           ┆ f64            │\n",
-      "╞═══════════════╪════════════════╡\n",
-      "│ 47.018074     ┆ -0.00108       │\n",
-      "└───────────────┴────────────────┘\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import polars as pl\n",
-    "import numpy as np\n",
-    "import narwhals as nw\n",
-    "from narwhals.typing import FrameT\n",
-    "\n",
-    "# Constants\n",
-    "NUM_ROWS = 100\n",
-    "SEED = 42\n",
-    "\n",
-    "# Step 1: Generate sample time-series data in Pandas\n",
-    "np.random.seed(SEED)\n",
-    "date_range = pd.date_range(start=\"2023-01-01\", periods=NUM_ROWS, freq=\"D\")\n",
-    "data_pandas = pd.DataFrame({\n",
-    "    \"datetime\": date_range,\n",
-    "    \"feature_1\": np.random.rand(NUM_ROWS),\n",
-    "    \"feature_2\": np.random.randn(NUM_ROWS)\n",
-    "})\n",
-    "\n",
-    "# Convert Pandas DataFrame to Polars\n",
-    "data_polars = pl.DataFrame(data_pandas)\n",
-    "\n",
-    "# Utility Functions\n",
-    "def is_timestamp_column(df: FrameT, col: str) -> bool:\n",
-    "    \"\"\"Check if a column is timestamp-like.\"\"\"\n",
-    "    if col not in df.columns:\n",
-    "        raise ValueError(f\"Column '{col}' does not exist in the DataFrame.\")\n",
-    "    \n",
-    "    try:\n",
-    "        if isinstance(df, pd.DataFrame):  # Pandas check\n",
-    "            return pd.api.types.is_datetime64_any_dtype(df[col])\n",
-    "        elif isinstance(df, pl.DataFrame):  # Polars check\n",
-    "            return str(df.schema[col]).startswith(\"Datetime\")\n",
-    "        else:\n",
-    "            raise ValueError(f\"Unsupported DataFrame type: {type(df)}\")\n",
-    "    except Exception as e:\n",
-    "        raise ValueError(f\"Error while checking column type: {e}\")\n",
-    "\n",
-    "def get_backend_info(df: FrameT) -> str:\n",
-    "    \"\"\"Determine the original backend of the DataFrame.\"\"\"\n",
-    "    if isinstance(df, pd.DataFrame):\n",
-    "        return \"pandas\"\n",
-    "    elif isinstance(df, pl.DataFrame):\n",
-    "        return \"polars\"\n",
-    "    else:\n",
-    "        return \"unknown\"\n",
-    "\n",
-    "# Narwhals Function\n",
-    "@nw.narwhalify\n",
-    "def test_narwhal_conversion(df: FrameT) -> FrameT:\n",
-    "    \"\"\"Perform Narwhals operations on a compatible DataFrame.\"\"\"\n",
-    "    return df.select(\n",
-    "        feature_1_sum=nw.col(\"feature_1\").sum(),\n",
-    "        feature_2_mean=nw.col(\"feature_2\").mean()\n",
-    "    )\n",
-    "\n",
-    "# Main Test Logic\n",
-    "def run_tests(data: FrameT, backend_name: str):\n",
-    "    print(f\"\\n=== Testing {backend_name} Backend ===\")\n",
-    "    \n",
-    "    # Original Backend\n",
-    "    try:\n",
-    "        original_backend = get_backend_info(data)\n",
-    "        print(\"Original DataFrame Backend:\", original_backend)\n",
-    "    except Exception as e:\n",
-    "        print(\"Error determining backend:\", e)\n",
-    "\n",
-    "    # Timestamp Check\n",
-    "    try:\n",
-    "        is_timestamp = is_timestamp_column(data, \"datetime\")\n",
-    "        print(\"Is 'datetime' timestamp-like?\", is_timestamp)\n",
-    "    except Exception as e:\n",
-    "        print(\"Timestamp check failed:\", e)\n",
-    "\n",
-    "    # Narwhals Operation\n",
-    "    try:\n",
-    "        result_df = test_narwhal_conversion(data)\n",
-    "        print(\"\\nResult of Narwhals operation:\\n\", result_df)\n",
-    "    except Exception as e:\n",
-    "        print(\"Narwhals operation failed:\", e)\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    # Test with Pandas\n",
-    "    run_tests(data_pandas, \"Pandas\")\n",
-    "\n",
-    "    # Test with Polars\n",
-    "    run_tests(data_polars, \"Polars\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example 1: Using Narwhals for Backend-Agnostic Null Check\n",
-    "\n",
-    "Narwhals enables robust, backend-agnostic data processing, which can streamline workflows across Pandas, Modin, Polars, and PyArrow backends. Here’s how to create a simple backend-agnostic null-check function and test it across multiple frameworks.\n",
-    "\n",
-    "1. **Create a Synthetic DataFrame**:\n",
-    "   - Use a function like `generate_data_time_series()` to create a manageable dataset, with defaults that allow flexibility for larger data sizes.\n",
-    "\n",
-    "2. **Define a Narwhals-Compatible Function**:\n",
-    "   - Use the `@narwhalify` decorator to convert standard DataFrame operations to backend-agnostic ones.\n",
-    "   - For example, `check_nulls_nw()` checks for null values without being tied to a specific backend.\n",
-    "\n",
-    "3. **Test Across Multiple Backends**:\n",
-    "   - Convert the DataFrame to various backends and execute the Narwhals function to verify compatibility and performance.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pandas -> Executed Successfully, Has Nulls:    has_nulls\n",
-      "0       True\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "UserWarning: The size of /dev/shm is too small (3904958464 bytes). The required size at least half of RAM (4017184768 bytes). Please, delete files in /dev/shm or increase size of /dev/shm with --shm-size in Docker. Also, you can can override the memory size for each Ray worker (in bytes) to the MODIN_MEMORY environment variable.\n",
-      "2024-11-11 11:34:11,220\tINFO worker.py:1816 -- Started a local Ray instance.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "modin -> Executed Successfully, Has Nulls:    has_nulls\n",
-      "0       True\n",
-      "\n",
-      "pyarrow -> Executed Successfully, Has Nulls: pyarrow.Table\n",
-      "has_nulls: bool\n",
-      "----\n",
-      "has_nulls: [[true]]\n",
-      "\n",
-      "polars -> Executed Successfully, Has Nulls: shape: (1, 1)\n",
-      "┌───────────┐\n",
-      "│ has_nulls │\n",
-      "│ ---       │\n",
-      "│ bool      │\n",
-      "╞═══════════╡\n",
-      "│ true      │\n",
-      "└───────────┘\n",
-      "\n",
-      "dask -> Executed Successfully, Has Nulls: Dask DataFrame Structure:\n",
-      "              has_nulls\n",
-      "npartitions=1          \n",
-      "                   bool\n",
-      "                    ...\n",
-      "Dask Name: to_frame, 11 expressions\n",
-      "Expr=ToFrame(frame=RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=IsNa(frame=df['feature_1']), index='feature_1')).any()), index='feature_1'))[0]), index='has_nulls'))\n",
-      "\n",
-      "\n",
-      "--- Summary of Backend Compatibility ---\n",
-      "{'Backend': 'pandas', 'Has Nulls':    has_nulls\n",
-      "0       True, 'Executed Successfully': True}\n",
-      "{'Backend': 'modin', 'Has Nulls':    has_nulls\n",
-      "0       True, 'Executed Successfully': True}\n",
-      "{'Backend': 'pyarrow', 'Has Nulls': pyarrow.Table\n",
-      "has_nulls: bool\n",
-      "----\n",
-      "has_nulls: [[true]], 'Executed Successfully': True}\n",
-      "{'Backend': 'polars', 'Has Nulls': shape: (1, 1)\n",
-      "┌───────────┐\n",
-      "│ has_nulls │\n",
-      "│ ---       │\n",
-      "│ bool      │\n",
-      "╞═══════════╡\n",
-      "│ true      │\n",
-      "└───────────┘, 'Executed Successfully': True}\n",
-      "{'Backend': 'dask', 'Has Nulls': Dask DataFrame Structure:\n",
-      "              has_nulls\n",
-      "npartitions=1          \n",
-      "                   bool\n",
-      "                    ...\n",
-      "Dask Name: to_frame, 11 expressions\n",
-      "Expr=ToFrame(frame=RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=IsNa(frame=df['feature_1']), index='feature_1')).any()), index='feature_1'))[0]), index='has_nulls')), 'Executed Successfully': True}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import modin.pandas as mpd\n",
-    "import polars as pl\n",
-    "import pyarrow as pa\n",
-    "import dask.dataframe as dd\n",
-    "import narwhals as nw\n",
-    "import numpy as np\n",
-    "from narwhals.typing import FrameT\n",
-    "from temporalscope.core.core_utils import TEMPORALSCOPE_CORE_BACKEND_TYPES, SupportedTemporalDataFrame\n",
-    "\n",
-    "NUM_ROWS = 100\n",
-    "SEED = 42\n",
-    "\n",
-    "def generate_data(num_rows: int = NUM_ROWS, backend: str = \"pandas\") -> SupportedTemporalDataFrame:\n",
-    "    \"\"\"Generates a time-series DataFrame with specified backend, adding features and a target.\"\"\"\n",
-    "    np.random.seed(SEED)\n",
-    "    data = pd.DataFrame({\n",
-    "        \"datetime\": pd.date_range(start=\"2023-01-01\", periods=num_rows, freq=\"D\")\n",
-    "    })\n",
-    "    \n",
-    "    # Generate 5 features\n",
-    "    for i in range(5):\n",
-    "        data[f\"feature_{i+1}\"] = np.random.randn(num_rows)\n",
-    "    data[\"target\"] = np.random.rand(num_rows)\n",
-    "    data.loc[0, 'feature_1'] = None  # Inject a null value for testing\n",
-    "\n",
-    "    # Convert to specified backend\n",
-    "    if backend == \"modin\":\n",
-    "        return mpd.DataFrame(data)\n",
-    "    elif backend == \"polars\":\n",
-    "        return pl.DataFrame(data)\n",
-    "    elif backend == \"pyarrow\":\n",
-    "        return pa.Table.from_pandas(data)\n",
-    "    elif backend == \"dask\":\n",
-    "        return dd.from_pandas(data, npartitions=2)\n",
-    "    return data\n",
-    "\n",
-    "@nw.narwhalify\n",
-    "def check_nulls_nw(df: FrameT) -> FrameT:\n",
-    "    \"\"\"Checks for null values in 'feature_1' in a backend-agnostic way using Narwhals.\"\"\"\n",
-    "    return df.select(\n",
-    "        has_nulls=nw.col(\"feature_1\").is_null().any()\n",
-    "    )\n",
-    "\n",
-    "def test_backends():\n",
-    "    \"\"\"Tests `check_nulls_nw` on all supported TemporalScope backends.\"\"\"\n",
-    "    results = []\n",
-    "    for backend_name in TEMPORALSCOPE_CORE_BACKEND_TYPES.keys():\n",
-    "        data_df = generate_data(backend=backend_name)\n",
-    "        try:\n",
-    "            has_nulls = check_nulls_nw(data_df)\n",
-    "            results.append({\n",
-    "                \"Backend\": backend_name,\n",
-    "                \"Has Nulls\": has_nulls,\n",
-    "                \"Executed Successfully\": True\n",
-    "            })\n",
-    "            print(f\"{backend_name} -> Executed Successfully, Has Nulls: {has_nulls}\\n\")\n",
-    "        except Exception as e:\n",
-    "            results.append({\n",
-    "                \"Backend\": backend_name,\n",
-    "                \"Executed Successfully\": False,\n",
-    "                \"Error\": str(e)\n",
-    "            })\n",
-    "            print(f\"{backend_name} -> Failed with error: {e}\\n\")\n",
-    "\n",
-    "    return results\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    backend_results = test_backends()\n",
-    "    print(\"\\n--- Summary of Backend Compatibility ---\")\n",
-    "    for result in backend_results:\n",
-    "        print(result)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example 2: Calculating Summary Statistics in a Backend-Agnostic Manner\n",
-    "\n",
-    "This example demonstrates **Narwhals**' ability to calculate summary statistics (mean, sum, standard deviation) across TemporalScope’s supported backends—**Pandas**, **Modin**, **Polars**, **PyArrow**, and **Dask**—with a backend-agnostic function that leverages Narwhals’ compatibility layer. \n",
-    "\n",
-    "### Key Steps\n",
-    "\n",
-    "1. **Generate Synthetic Data Across Backends**:\n",
-    "   - Using `generate_data()`, we create a synthetic time-series DataFrame with multiple features and a target column. This dataset is compatible with multiple backends and provides the basis for backend-agnostic operations.\n",
-    "\n",
-    "2. **Define a Narwhals-Compatible Summary Function**:\n",
-    "   - `calculate_summaries_nw()` uses the `@narwhalify` decorator to compute mean, sum, and standard deviation for each feature in a backend-agnostic manner.\n",
-    "\n",
-    "3. **Understanding Lazy vs. Eager Evaluation**:\n",
-    "   - **Lazy Evaluation** (Polars, Dask): Allows efficient, optimized computation by delaying operations until results are explicitly requested, like with `.collect()`. Narwhals supports lazy evaluation for backends like Polars, automatically handling eager execution where needed.\n",
-    "   - **Eager Evaluation** (Pandas, Modin, PyArrow): Calculates results immediately, useful for smaller datasets or immediate result retrieval.\n",
-    "   - Narwhals adapts between lazy and eager modes based on backend needs, ensuring that computations are handled correctly even across large and distributed datasets.\n",
-    "\n",
-    "4. **Test Across Multiple Backends**:\n",
-    "   - The function is tested across the TemporalScope-supported backends, with each backend returning summary statistics in its native structure.\n",
-    "   - Results vary slightly in format: Pandas and Modin return DataFrames, PyArrow returns a `Table`, Polars provides its own optimized `DataFrame` structure, and Dask shows results as a Dask DataFrame structure.\n",
-    "\n",
-    "This example showcases Narwhals’ seamless support for multi-backend compatibility, optimizing data operations across frameworks without modifying the core logic.\n",
-    "~"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pandas -> Executed Successfully, Summaries:\n",
-      "   feature_1_mean  feature_1_sum  feature_1_std  feature_2_mean  \\\n",
-      "0        0.471147      46.643534       0.298846        0.497832   \n",
-      "\n",
-      "   feature_2_sum  feature_2_std  feature_3_mean  feature_3_sum  feature_3_std  \\\n",
-      "0      49.783172       0.293111        0.517601      51.760133       0.293426   \n",
-      "\n",
-      "   feature_4_mean  feature_4_sum  feature_4_std  feature_5_mean  \\\n",
-      "0        0.491149      49.114894       0.293452        0.516046   \n",
-      "\n",
-      "   feature_5_sum  feature_5_std  \n",
-      "0      51.604582       0.318601  \n",
-      "\n",
-      "modin -> Executed Successfully, Summaries:\n",
-      "   feature_1_mean  feature_1_sum  feature_1_std  feature_2_mean  \\\n",
-      "0        0.471147      46.643534       0.298846        0.497832   \n",
-      "\n",
-      "   feature_2_sum  feature_2_std  feature_3_mean  feature_3_sum  feature_3_std  \\\n",
-      "0      49.783172       0.293111        0.517601      51.760133       0.293426   \n",
-      "\n",
-      "   feature_4_mean  feature_4_sum  feature_4_std  feature_5_mean  \\\n",
-      "0        0.491149      49.114894       0.293452        0.516046   \n",
-      "\n",
-      "   feature_5_sum  feature_5_std  \n",
-      "0      51.604582       0.318601  \n",
-      "\n",
-      "pyarrow -> Executed Successfully, Summaries:\n",
-      "pyarrow.Table\n",
-      "feature_1_mean: double\n",
-      "feature_1_sum: double\n",
-      "feature_1_std: double\n",
-      "feature_2_mean: double\n",
-      "feature_2_sum: double\n",
-      "feature_2_std: double\n",
-      "feature_3_mean: double\n",
-      "feature_3_sum: double\n",
-      "feature_3_std: double\n",
-      "feature_4_mean: double\n",
-      "feature_4_sum: double\n",
-      "feature_4_std: double\n",
-      "feature_5_mean: double\n",
-      "feature_5_sum: double\n",
-      "feature_5_std: double\n",
-      "----\n",
-      "feature_1_mean: [[0.4711468102926623]]\n",
-      "feature_1_sum: [[46.64353421897357]]\n",
-      "feature_1_std: [[0.29884566169994814]]\n",
-      "feature_2_mean: [[0.4978317231550229]]\n",
-      "feature_2_sum: [[49.78317231550229]]\n",
-      "feature_2_std: [[0.2931112525150842]]\n",
-      "feature_3_mean: [[0.5176013307472443]]\n",
-      "feature_3_sum: [[51.760133074724436]]\n",
-      "feature_3_std: [[0.29342624706090614]]\n",
-      "feature_4_mean: [[0.49114894080503435]]\n",
-      "...\n",
-      "\n",
-      "polars -> Executed Successfully, Summaries:\n",
-      "shape: (1, 15)\n",
-      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-      "│ feature_1 ┆ feature_1 ┆ feature_1 ┆ feature_2 ┆ … ┆ feature_4 ┆ feature_5 ┆ feature_5 ┆ feature_ │\n",
-      "│ _mean     ┆ _sum      ┆ _std      ┆ _mean     ┆   ┆ _std      ┆ _mean     ┆ _sum      ┆ 5_std    │\n",
-      "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
-      "│ f64       ┆ f64       ┆ f64       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
-      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-      "│ 0.471147  ┆ 46.643534 ┆ 0.298846  ┆ 0.497832  ┆ … ┆ 0.293452  ┆ 0.516046  ┆ 51.604582 ┆ 0.318601 │\n",
-      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n",
-      "\n",
-      "dask -> Executed Successfully, Summaries:\n",
-      "Dask DataFrame Structure:\n",
-      "              feature_1_mean feature_1_sum feature_1_std feature_2_mean feature_2_sum feature_2_std feature_3_mean feature_3_sum feature_3_std feature_4_mean feature_4_sum feature_4_std feature_5_mean feature_5_sum feature_5_std\n",
-      "npartitions=1                                                                                                                                                                                                                       \n",
-      "                     float64       float64       float64        float64       float64       float64        float64       float64       float64        float64       float64       float64        float64       float64       float64\n",
-      "                         ...           ...           ...            ...           ...           ...            ...           ...           ...            ...           ...           ...            ...           ...           ...\n",
-      "Dask Name: concat, 117 expressions\n",
-      "Expr=Concat(frames=[RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_1'].mean()), index='feature_1'), index='feature_1_mean'))[0]), index='feature_1_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_1'].sum()), index='feature_1'), index='feature_1_sum'))[0]), index='feature_1_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_1'), index='feature_1_std'))[0]), index='feature_1_std'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_2'].mean()), index='feature_2'), index='feature_2_mean'))[0]), index='feature_2_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_2'].sum()), index='feature_2'), index='feature_2_sum'))[0]), index='feature_2_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_2'), index='feature_2_std'))[0]), index='feature_2_std'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_3'].mean()), index='feature_3'), index='feature_3_mean'))[0]), index='feature_3_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_3'].sum()), index='feature_3'), index='feature_3_sum'))[0]), index='feature_3_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_3'), index='feature_3_std'))[0]), index='feature_3_std'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_4'].mean()), index='feature_4'), index='feature_4_mean'))[0]), index='feature_4_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_4'].sum()), index='feature_4'), index='feature_4_sum'))[0]), index='feature_4_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_4'), index='feature_4_std'))[0]), index='feature_4_std'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_5'].mean()), index='feature_5'), index='feature_5_mean'))[0]), index='feature_5_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_5'].sum()), index='feature_5'), index='feature_5_sum'))[0]), index='feature_5_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_5'), index='feature_5_std'))[0]), index='feature_5_std')], axis=1)\n",
-      "\n",
-      "\n",
-      "--- Summary of Backend Compatibility ---\n",
-      "{'Backend': 'pandas', 'Summaries':    feature_1_mean  feature_1_sum  feature_1_std  feature_2_mean  \\\n",
-      "0        0.471147      46.643534       0.298846        0.497832   \n",
-      "\n",
-      "   feature_2_sum  feature_2_std  feature_3_mean  feature_3_sum  feature_3_std  \\\n",
-      "0      49.783172       0.293111        0.517601      51.760133       0.293426   \n",
-      "\n",
-      "   feature_4_mean  feature_4_sum  feature_4_std  feature_5_mean  \\\n",
-      "0        0.491149      49.114894       0.293452        0.516046   \n",
-      "\n",
-      "   feature_5_sum  feature_5_std  \n",
-      "0      51.604582       0.318601  , 'Executed Successfully': True}\n",
-      "{'Backend': 'modin', 'Summaries':    feature_1_mean  feature_1_sum  feature_1_std  feature_2_mean  \\\n",
-      "0        0.471147      46.643534       0.298846        0.497832   \n",
-      "\n",
-      "   feature_2_sum  feature_2_std  feature_3_mean  feature_3_sum  feature_3_std  \\\n",
-      "0      49.783172       0.293111        0.517601      51.760133       0.293426   \n",
-      "\n",
-      "   feature_4_mean  feature_4_sum  feature_4_std  feature_5_mean  \\\n",
-      "0        0.491149      49.114894       0.293452        0.516046   \n",
-      "\n",
-      "   feature_5_sum  feature_5_std  \n",
-      "0      51.604582       0.318601  , 'Executed Successfully': True}\n",
-      "{'Backend': 'pyarrow', 'Summaries': pyarrow.Table\n",
-      "feature_1_mean: double\n",
-      "feature_1_sum: double\n",
-      "feature_1_std: double\n",
-      "feature_2_mean: double\n",
-      "feature_2_sum: double\n",
-      "feature_2_std: double\n",
-      "feature_3_mean: double\n",
-      "feature_3_sum: double\n",
-      "feature_3_std: double\n",
-      "feature_4_mean: double\n",
-      "feature_4_sum: double\n",
-      "feature_4_std: double\n",
-      "feature_5_mean: double\n",
-      "feature_5_sum: double\n",
-      "feature_5_std: double\n",
-      "----\n",
-      "feature_1_mean: [[0.4711468102926623]]\n",
-      "feature_1_sum: [[46.64353421897357]]\n",
-      "feature_1_std: [[0.29884566169994814]]\n",
-      "feature_2_mean: [[0.4978317231550229]]\n",
-      "feature_2_sum: [[49.78317231550229]]\n",
-      "feature_2_std: [[0.2931112525150842]]\n",
-      "feature_3_mean: [[0.5176013307472443]]\n",
-      "feature_3_sum: [[51.760133074724436]]\n",
-      "feature_3_std: [[0.29342624706090614]]\n",
-      "feature_4_mean: [[0.49114894080503435]]\n",
-      "..., 'Executed Successfully': True}\n",
-      "{'Backend': 'polars', 'Summaries': shape: (1, 15)\n",
-      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-      "│ feature_1 ┆ feature_1 ┆ feature_1 ┆ feature_2 ┆ … ┆ feature_4 ┆ feature_5 ┆ feature_5 ┆ feature_ │\n",
-      "│ _mean     ┆ _sum      ┆ _std      ┆ _mean     ┆   ┆ _std      ┆ _mean     ┆ _sum      ┆ 5_std    │\n",
-      "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
-      "│ f64       ┆ f64       ┆ f64       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
-      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-      "│ 0.471147  ┆ 46.643534 ┆ 0.298846  ┆ 0.497832  ┆ … ┆ 0.293452  ┆ 0.516046  ┆ 51.604582 ┆ 0.318601 │\n",
-      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘, 'Executed Successfully': True}\n",
-      "{'Backend': 'dask', 'Summaries': Dask DataFrame Structure:\n",
-      "              feature_1_mean feature_1_sum feature_1_std feature_2_mean feature_2_sum feature_2_std feature_3_mean feature_3_sum feature_3_std feature_4_mean feature_4_sum feature_4_std feature_5_mean feature_5_sum feature_5_std\n",
-      "npartitions=1                                                                                                                                                                                                                       \n",
-      "                     float64       float64       float64        float64       float64       float64        float64       float64       float64        float64       float64       float64        float64       float64       float64\n",
-      "                         ...           ...           ...            ...           ...           ...            ...           ...           ...            ...           ...           ...            ...           ...           ...\n",
-      "Dask Name: concat, 117 expressions\n",
-      "Expr=Concat(frames=[RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_1'].mean()), index='feature_1'), index='feature_1_mean'))[0]), index='feature_1_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_1'].sum()), index='feature_1'), index='feature_1_sum'))[0]), index='feature_1_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_1'), index='feature_1_std'))[0]), index='feature_1_std'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_2'].mean()), index='feature_2'), index='feature_2_mean'))[0]), index='feature_2_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_2'].sum()), index='feature_2'), index='feature_2_sum'))[0]), index='feature_2_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_2'), index='feature_2_std'))[0]), index='feature_2_std'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_3'].mean()), index='feature_3'), index='feature_3_mean'))[0]), index='feature_3_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_3'].sum()), index='feature_3'), index='feature_3_sum'))[0]), index='feature_3_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_3'), index='feature_3_std'))[0]), index='feature_3_std'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_4'].mean()), index='feature_4'), index='feature_4_mean'))[0]), index='feature_4_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_4'].sum()), index='feature_4'), index='feature_4_sum'))[0]), index='feature_4_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_4'), index='feature_4_std'))[0]), index='feature_4_std'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_5'].mean()), index='feature_5'), index='feature_5_mean'))[0]), index='feature_5_mean'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=df['feature_5'].sum()), index='feature_5'), index='feature_5_sum'))[0]), index='feature_5_sum'), RenameSeries(frame=ScalarToSeries(frame=(RenameSeries(frame=RenameSeries(frame=ScalarToSeries(frame=MapPartitions(sqrt)), index='feature_5'), index='feature_5_std'))[0]), index='feature_5_std')], axis=1), 'Executed Successfully': True}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import modin.pandas as mpd\n",
-    "import polars as pl\n",
-    "import pyarrow as pa\n",
-    "import dask.dataframe as dd\n",
-    "import narwhals as nw\n",
-    "from narwhals.typing import FrameT\n",
-    "import numpy as np\n",
-    "from temporalscope.core.core_utils import TEMPORALSCOPE_CORE_BACKEND_TYPES\n",
-    "\n",
-    "# Constants\n",
-    "NUM_ROWS = 100\n",
-    "SEED = 42\n",
-    "\n",
-    "# Data generator function\n",
-    "def generate_data(num_rows: int = NUM_ROWS, backend: str = \"pandas\") -> pd.DataFrame:\n",
-    "    \"\"\"Generates a time-series DataFrame with multiple features and a target column.\"\"\"\n",
-    "    np.random.seed(SEED)\n",
-    "    data = pd.DataFrame({\n",
-    "        \"datetime\": pd.date_range(start=\"2023-01-01\", periods=num_rows, freq=\"D\"),\n",
-    "        **{f\"feature_{i}\": np.random.rand(num_rows) for i in range(1, 6)},  # Generating 5 random features\n",
-    "        \"target\": np.random.randint(0, 100, num_rows)  # Random integer target\n",
-    "    })\n",
-    "    data.loc[0, 'feature_1'] = None  # Injecting a null value for testing\n",
-    "    \n",
-    "    # Convert to the specified backend\n",
-    "    if backend == \"modin\":\n",
-    "        return mpd.DataFrame(data)\n",
-    "    elif backend == \"polars\":\n",
-    "        return pl.DataFrame(data)\n",
-    "    elif backend == \"pyarrow\":\n",
-    "        return pa.Table.from_pandas(data)\n",
-    "    elif backend == \"dask\":\n",
-    "        return dd.from_pandas(data, npartitions=2)\n",
-    "    return data  # Default to Pandas DataFrame\n",
-    "\n",
-    "# Narwhals-compatible function for summary statistics\n",
-    "@nw.narwhalify\n",
-    "def calculate_summaries_nw(df: FrameT) -> FrameT:\n",
-    "    \"\"\"Calculates mean, sum, and standard deviation for each feature using Narwhals.\"\"\"\n",
-    "    expressions = []\n",
-    "    for col in df.columns:\n",
-    "        if col.startswith(\"feature_\"):\n",
-    "            expressions.extend([\n",
-    "                nw.col(col).mean().alias(f\"{col}_mean\"),\n",
-    "                nw.col(col).sum().alias(f\"{col}_sum\"),\n",
-    "                nw.col(col).std().alias(f\"{col}_std\")\n",
-    "            ])\n",
-    "    return df.select(*expressions)\n",
-    "\n",
-    "# Testing function across all backends\n",
-    "def test_backends():\n",
-    "    \"\"\"Tests `calculate_summaries_nw` on all supported TemporalScope backends.\"\"\"\n",
-    "    results = []\n",
-    "    for backend_name in TEMPORALSCOPE_CORE_BACKEND_TYPES.keys():\n",
-    "        data_df = generate_data(backend=backend_name)\n",
-    "        try:\n",
-    "            summaries = calculate_summaries_nw(data_df)\n",
-    "            results.append({\n",
-    "                \"Backend\": backend_name,\n",
-    "                \"Summaries\": summaries,\n",
-    "                \"Executed Successfully\": True\n",
-    "            })\n",
-    "            print(f\"{backend_name} -> Executed Successfully, Summaries:\\n{summaries}\\n\")\n",
-    "        except Exception as e:\n",
-    "            results.append({\n",
-    "                \"Backend\": backend_name,\n",
-    "                \"Executed Successfully\": False,\n",
-    "                \"Error\": str(e)\n",
-    "            })\n",
-    "            print(f\"{backend_name} -> Failed with error: {e}\\n\")\n",
-    "\n",
-    "    return results\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    # Run the backend compatibility tests\n",
-    "    backend_results = test_backends()\n",
-    "    print(\"\\n--- Summary of Backend Compatibility ---\")\n",
-    "    for result in backend_results:\n",
-    "        print(result)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example 3: Scaling and Lagging Features in a Backend-Agnostic Manner\n",
-    "\n",
-    "In this example, we demonstrate **Narwhals**’ capability to apply scaling and lag transformations across TemporalScope's supported backends. This approach is beneficial for time-series analysis, where lagging can reveal important sequential dependencies. Narwhals allows us to scale and lag feature columns consistently across multiple frameworks, maintaining a unified codebase.\n",
-    "\n",
-    "### Key Steps\n",
-    "\n",
-    "1. **Generate Synthetic Data Across Backends**:\n",
-    "   - Using `generate_data()`, we create a synthetic DataFrame with multiple features and a target column, which can be applied to each backend for testing.\n",
-    "\n",
-    "2. **Define a Narwhals-Compatible Scaling and Lagging Function**:\n",
-    "   - `scale_and_lag_features_nw()` applies scaling and lagging transformations to each feature column in a backend-agnostic manner. Scaling standardizes each feature, and lagging shifts values by a defined number of steps, revealing sequential dependencies.\n",
-    "\n",
-    "3. **Test Across Multiple Backends**:\n",
-    "   - Run the function on each backend, returning results in the native format for each. This verifies compatibility and consistent application of transformations across Pandas, Modin, Polars, PyArrow, and Dask backends.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pandas -> Executed Successfully, Transformed Data:\n",
-      "     datetime  feature_1  feature_2  feature_3  feature_4  feature_5  target  \\\n",
-      "0  2023-01-01   0.374540   0.031429   0.642032   0.051682   0.103124      62   \n",
-      "1  2023-01-02   0.950714   0.636410   0.084140   0.531355   0.902553      16   \n",
-      "2  2023-01-03   0.731994   0.314356   0.161629   0.540635   0.505252      72   \n",
-      "3  2023-01-04   0.598658   0.508571   0.898554   0.637430   0.826457      32   \n",
-      "4  2023-01-05   0.156019   0.907566   0.606429   0.726091   0.320050      83   \n",
-      "..        ...        ...        ...        ...        ...        ...     ...   \n",
-      "95 2023-04-06   0.493796   0.349210   0.522243   0.930757   0.353352      63   \n",
-      "96 2023-04-07   0.522733   0.725956   0.769994   0.858413   0.583656      97   \n",
-      "97 2023-04-08   0.427541   0.897110   0.215821   0.428994   0.077735      37   \n",
-      "98 2023-04-09   0.025419   0.887086   0.622890   0.750871   0.974395      49   \n",
-      "99 2023-04-10   0.107891   0.779876   0.085347   0.754543   0.986211      97   \n",
-      "\n",
-      "    feature_1_scaled  feature_1_lag3  feature_2_scaled  feature_2_lag3  \\\n",
-      "0          -0.321493             NaN         -1.591213             NaN   \n",
-      "1           1.615296             NaN          0.472785             NaN   \n",
-      "2           0.880076             NaN         -0.625959             NaN   \n",
-      "3           0.431873        0.374540          0.036638        0.031429   \n",
-      "4          -1.056045        0.950714          1.397881        0.636410   \n",
-      "..               ...             ...               ...             ...   \n",
-      "95          0.079380        0.760785         -0.507050        0.900418   \n",
-      "96          0.176652        0.561277          0.778285        0.633101   \n",
-      "97         -0.143332        0.770967          1.362208        0.339030   \n",
-      "98         -1.495050        0.493796          1.328010        0.349210   \n",
-      "99         -1.217823        0.522733          0.962242        0.725956   \n",
-      "\n",
-      "    feature_3_scaled  feature_3_lag3  feature_4_scaled  feature_4_lag3  \\\n",
-      "0           0.424060             NaN         -1.497577             NaN   \n",
-      "1          -1.477241             NaN          0.137009             NaN   \n",
-      "2          -1.213159             NaN          0.168635             NaN   \n",
-      "3           1.298292        0.642032          0.498483        0.051682   \n",
-      "4           0.302726        0.084140          0.800615        0.531355   \n",
-      "..               ...             ...               ...             ...   \n",
-      "95          0.015820        0.822601          1.498058        0.372018   \n",
-      "96          0.860156        0.360191          1.251528        0.776413   \n",
-      "97         -1.028471        0.127061         -0.211806        0.340804   \n",
-      "98          0.358827        0.522243          0.885058        0.930757   \n",
-      "99         -1.473126        0.769994          0.897570        0.858413   \n",
-      "\n",
-      "    feature_5_scaled  feature_5_lag3  \n",
-      "0          -1.296048             NaN  \n",
-      "1           1.213139             NaN  \n",
-      "2          -0.033878             NaN  \n",
-      "3           0.974296        0.103124  \n",
-      "4          -0.615178        0.902553  \n",
-      "..               ...             ...  \n",
-      "95         -0.510650        0.277381  \n",
-      "96          0.212210        0.188121  \n",
-      "97         -1.375737        0.463698  \n",
-      "98          1.438631        0.353352  \n",
-      "99          1.475718        0.583656  \n",
-      "\n",
-      "[100 rows x 17 columns]\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "UserWarning: <function DataFrame.shift> is not currently supported by PandasOnRay, defaulting to pandas implementation.\n",
-      "Please refer to https://modin.readthedocs.io/en/stable/supported_apis/defaulting_to_pandas.html for explanation.\n",
-      "UserWarning: <function DataFrame.shift> is not currently supported by PandasOnRay, defaulting to pandas implementation.\n",
-      "UserWarning: <function DataFrame.shift> is not currently supported by PandasOnRay, defaulting to pandas implementation.\n",
-      "UserWarning: <function DataFrame.shift> is not currently supported by PandasOnRay, defaulting to pandas implementation.\n",
-      "UserWarning: <function DataFrame.shift> is not currently supported by PandasOnRay, defaulting to pandas implementation.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "modin -> Executed Successfully, Transformed Data:\n",
-      "     datetime  feature_1  feature_2  feature_3  feature_4  feature_5  target  \\\n",
-      "0  2023-01-01   0.374540   0.031429   0.642032   0.051682   0.103124      62   \n",
-      "1  2023-01-02   0.950714   0.636410   0.084140   0.531355   0.902553      16   \n",
-      "2  2023-01-03   0.731994   0.314356   0.161629   0.540635   0.505252      72   \n",
-      "3  2023-01-04   0.598658   0.508571   0.898554   0.637430   0.826457      32   \n",
-      "4  2023-01-05   0.156019   0.907566   0.606429   0.726091   0.320050      83   \n",
-      "..        ...        ...        ...        ...        ...        ...     ...   \n",
-      "95 2023-04-06   0.493796   0.349210   0.522243   0.930757   0.353352      63   \n",
-      "96 2023-04-07   0.522733   0.725956   0.769994   0.858413   0.583656      97   \n",
-      "97 2023-04-08   0.427541   0.897110   0.215821   0.428994   0.077735      37   \n",
-      "98 2023-04-09   0.025419   0.887086   0.622890   0.750871   0.974395      49   \n",
-      "99 2023-04-10   0.107891   0.779876   0.085347   0.754543   0.986211      97   \n",
-      "\n",
-      "    feature_1_scaled  feature_1_lag3  feature_2_scaled  feature_2_lag3  \\\n",
-      "0          -0.321493             NaN         -1.591213             NaN   \n",
-      "1           1.615296             NaN          0.472785             NaN   \n",
-      "2           0.880076             NaN         -0.625959             NaN   \n",
-      "3           0.431873        0.374540          0.036638        0.031429   \n",
-      "4          -1.056045        0.950714          1.397881        0.636410   \n",
-      "..               ...             ...               ...             ...   \n",
-      "95          0.079380        0.760785         -0.507050        0.900418   \n",
-      "96          0.176652        0.561277          0.778285        0.633101   \n",
-      "97         -0.143332        0.770967          1.362208        0.339030   \n",
-      "98         -1.495050        0.493796          1.328010        0.349210   \n",
-      "99         -1.217823        0.522733          0.962242        0.725956   \n",
-      "\n",
-      "    feature_3_scaled  feature_3_lag3  feature_4_scaled  feature_4_lag3  \\\n",
-      "0           0.424060             NaN         -1.497577             NaN   \n",
-      "1          -1.477241             NaN          0.137009             NaN   \n",
-      "2          -1.213159             NaN          0.168635             NaN   \n",
-      "3           1.298292        0.642032          0.498483        0.051682   \n",
-      "4           0.302726        0.084140          0.800615        0.531355   \n",
-      "..               ...             ...               ...             ...   \n",
-      "95          0.015820        0.822601          1.498058        0.372018   \n",
-      "96          0.860156        0.360191          1.251528        0.776413   \n",
-      "97         -1.028471        0.127061         -0.211806        0.340804   \n",
-      "98          0.358827        0.522243          0.885058        0.930757   \n",
-      "99         -1.473126        0.769994          0.897570        0.858413   \n",
-      "\n",
-      "    feature_5_scaled  feature_5_lag3  \n",
-      "0          -1.296048             NaN  \n",
-      "1           1.213139             NaN  \n",
-      "2          -0.033878             NaN  \n",
-      "3           0.974296        0.103124  \n",
-      "4          -0.615178        0.902553  \n",
-      "..               ...             ...  \n",
-      "95         -0.510650        0.277381  \n",
-      "96          0.212210        0.188121  \n",
-      "97         -1.375737        0.463698  \n",
-      "98          1.438631        0.353352  \n",
-      "99          1.475718        0.583656  \n",
-      "\n",
-      "[100 rows x 17 columns]\n",
-      "\n",
-      "pyarrow -> Executed Successfully, Transformed Data:\n",
-      "pyarrow.Table\n",
-      "datetime: timestamp[ns]\n",
-      "feature_1: double\n",
-      "feature_2: double\n",
-      "feature_3: double\n",
-      "feature_4: double\n",
-      "feature_5: double\n",
-      "target: int64\n",
-      "feature_1_scaled: double\n",
-      "feature_1_lag3: double\n",
-      "feature_2_scaled: double\n",
-      "feature_2_lag3: double\n",
-      "feature_3_scaled: double\n",
-      "feature_3_lag3: double\n",
-      "feature_4_scaled: double\n",
-      "feature_4_lag3: double\n",
-      "feature_5_scaled: double\n",
-      "feature_5_lag3: double\n",
-      "----\n",
-      "datetime: [[2023-01-01 00:00:00.000000000,2023-01-02 00:00:00.000000000,2023-01-03 00:00:00.000000000,2023-01-04 00:00:00.000000000,2023-01-05 00:00:00.000000000,...,2023-04-06 00:00:00.000000000,2023-04-07 00:00:00.000000000,2023-04-08 00:00:00.000000000,2023-04-09 00:00:00.000000000,2023-04-10 00:00:00.000000000]]\n",
-      "feature_1: [[0.3745401188473625,0.9507143064099162,0.7319939418114051,0.5986584841970366,0.15601864044243652,...,0.49379559636439074,0.5227328293819941,0.42754101835854963,0.02541912674409519,0.10789142699330445]]\n",
-      "feature_2: [[0.03142918568673425,0.6364104112637804,0.3143559810763267,0.5085706911647028,0.907566473926093,...,0.3492095746126609,0.7259556788702394,0.8971102599525771,0.8870864242651173,0.7798755458576239]]\n",
-      "feature_3: [[0.6420316461542878,0.08413996499504883,0.16162871409461377,0.8985541885270792,0.6064290596595899,...,0.5222432600548044,0.7699935530986108,0.21582102749684318,0.6228904758190003,0.085347464993768]]\n",
-      "feature_4: [[0.0516817211686077,0.531354631568148,0.5406351216101065,0.6374299014982066,0.7260913337226615,...,0.9307573256035647,0.8584127518430118,0.42899402737501835,0.7508710677914974,0.7545428740846823]]\n",
-      "feature_5: [[0.10312386883593261,0.9025529066795667,0.5052523724478571,0.8264574661077416,0.32004960103061175,...,0.3533522280260528,0.5836561118508721,0.07773463696498484,0.9743948076661665,0.9862107444796029]]\n",
-      "target: [[62,16,72,32,83,...,63,97,37,49,97]]\n",
-      "feature_1_scaled: [[-0.32149253381634435,1.6152963609416058,0.8800756892140731,0.4318733240969416,-1.056044656727614,...,0.07938048250384727,0.17665195485253263,-0.14333190843375598,-1.4950502443638614,-1.2178225609725948]]\n",
-      "feature_1_lag3: [[null,null,null,0.3745401188473625,0.9507143064099162,...,0.7607850486168974,0.5612771975694962,0.770967179954561,0.49379559636439074,0.5227328293819941]]\n",
-      "feature_2_scaled: [[-1.5912133480589814,0.47278528858807944,-0.62595939427216,0.036637856505107154,1.3978813411470248,...,-0.5070503000723712,0.7782845378939408,1.3622081491975693,1.328010090946824,0.9622415389463301]]\n",
-      "...\n",
-      "\n",
-      "polars -> Executed Successfully, Transformed Data:\n",
-      "shape: (100, 17)\n",
-      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-      "│ datetime  ┆ feature_1 ┆ feature_2 ┆ feature_3 ┆ … ┆ feature_4 ┆ feature_4 ┆ feature_5 ┆ feature_ │\n",
-      "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ _scaled   ┆ _lag3     ┆ _scaled   ┆ 5_lag3   │\n",
-      "│ datetime[ ┆ f64       ┆ f64       ┆ f64       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
-      "│ ns]       ┆           ┆           ┆           ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
-      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-      "│ 2023-01-0 ┆ 0.37454   ┆ 0.031429  ┆ 0.642032  ┆ … ┆ -1.497577 ┆ null      ┆ -1.296048 ┆ null     │\n",
-      "│ 1         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-01-0 ┆ 0.950714  ┆ 0.63641   ┆ 0.08414   ┆ … ┆ 0.137009  ┆ null      ┆ 1.213139  ┆ null     │\n",
-      "│ 2         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-01-0 ┆ 0.731994  ┆ 0.314356  ┆ 0.161629  ┆ … ┆ 0.168635  ┆ null      ┆ -0.033878 ┆ null     │\n",
-      "│ 3         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-01-0 ┆ 0.598658  ┆ 0.508571  ┆ 0.898554  ┆ … ┆ 0.498483  ┆ 0.051682  ┆ 0.974296  ┆ 0.103124 │\n",
-      "│ 4         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-01-0 ┆ 0.156019  ┆ 0.907566  ┆ 0.606429  ┆ … ┆ 0.800615  ┆ 0.531355  ┆ -0.615178 ┆ 0.902553 │\n",
-      "│ 5         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ …         ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …        │\n",
-      "│ 2023-04-0 ┆ 0.493796  ┆ 0.34921   ┆ 0.522243  ┆ … ┆ 1.498058  ┆ 0.372018  ┆ -0.51065  ┆ 0.277381 │\n",
-      "│ 6         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-04-0 ┆ 0.522733  ┆ 0.725956  ┆ 0.769994  ┆ … ┆ 1.251528  ┆ 0.776413  ┆ 0.21221   ┆ 0.188121 │\n",
-      "│ 7         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-04-0 ┆ 0.427541  ┆ 0.89711   ┆ 0.215821  ┆ … ┆ -0.211806 ┆ 0.340804  ┆ -1.375737 ┆ 0.463698 │\n",
-      "│ 8         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-04-0 ┆ 0.025419  ┆ 0.887086  ┆ 0.62289   ┆ … ┆ 0.885058  ┆ 0.930757  ┆ 1.438631  ┆ 0.353352 │\n",
-      "│ 9         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-04-1 ┆ 0.107891  ┆ 0.779876  ┆ 0.085347  ┆ … ┆ 0.89757   ┆ 0.858413  ┆ 1.475718  ┆ 0.583656 │\n",
-      "│ 0         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n",
-      "\n",
-      "dask -> Executed Successfully, Transformed Data:\n",
-      "Dask DataFrame Structure:\n",
-      "                     datetime feature_1 feature_2 feature_3 feature_4 feature_5 target feature_1_scaled feature_1_lag3 feature_2_scaled feature_2_lag3 feature_3_scaled feature_3_lag3 feature_4_scaled feature_4_lag3 feature_5_scaled feature_5_lag3\n",
-      "npartitions=2                                                                                                                                                                                                                                         \n",
-      "0              datetime64[ns]   float64   float64   float64   float64   float64  int64          float64        float64          float64        float64          float64        float64          float64        float64          float64        float64\n",
-      "50                        ...       ...       ...       ...       ...       ...    ...              ...            ...              ...            ...              ...            ...              ...            ...              ...            ...\n",
-      "99                        ...       ...       ...       ...       ...       ...    ...              ...            ...              ...            ...              ...            ...              ...            ...              ...            ...\n",
-      "Dask Name: assign, 92 expressions\n",
-      "Expr=Assign(frame=df)\n",
-      "\n",
-      "\n",
-      "--- Summary of Backend Compatibility ---\n",
-      "{'Backend': 'pandas', 'Transformed Data':      datetime  feature_1  feature_2  feature_3  feature_4  feature_5  target  \\\n",
-      "0  2023-01-01   0.374540   0.031429   0.642032   0.051682   0.103124      62   \n",
-      "1  2023-01-02   0.950714   0.636410   0.084140   0.531355   0.902553      16   \n",
-      "2  2023-01-03   0.731994   0.314356   0.161629   0.540635   0.505252      72   \n",
-      "3  2023-01-04   0.598658   0.508571   0.898554   0.637430   0.826457      32   \n",
-      "4  2023-01-05   0.156019   0.907566   0.606429   0.726091   0.320050      83   \n",
-      "..        ...        ...        ...        ...        ...        ...     ...   \n",
-      "95 2023-04-06   0.493796   0.349210   0.522243   0.930757   0.353352      63   \n",
-      "96 2023-04-07   0.522733   0.725956   0.769994   0.858413   0.583656      97   \n",
-      "97 2023-04-08   0.427541   0.897110   0.215821   0.428994   0.077735      37   \n",
-      "98 2023-04-09   0.025419   0.887086   0.622890   0.750871   0.974395      49   \n",
-      "99 2023-04-10   0.107891   0.779876   0.085347   0.754543   0.986211      97   \n",
-      "\n",
-      "    feature_1_scaled  feature_1_lag3  feature_2_scaled  feature_2_lag3  \\\n",
-      "0          -0.321493             NaN         -1.591213             NaN   \n",
-      "1           1.615296             NaN          0.472785             NaN   \n",
-      "2           0.880076             NaN         -0.625959             NaN   \n",
-      "3           0.431873        0.374540          0.036638        0.031429   \n",
-      "4          -1.056045        0.950714          1.397881        0.636410   \n",
-      "..               ...             ...               ...             ...   \n",
-      "95          0.079380        0.760785         -0.507050        0.900418   \n",
-      "96          0.176652        0.561277          0.778285        0.633101   \n",
-      "97         -0.143332        0.770967          1.362208        0.339030   \n",
-      "98         -1.495050        0.493796          1.328010        0.349210   \n",
-      "99         -1.217823        0.522733          0.962242        0.725956   \n",
-      "\n",
-      "    feature_3_scaled  feature_3_lag3  feature_4_scaled  feature_4_lag3  \\\n",
-      "0           0.424060             NaN         -1.497577             NaN   \n",
-      "1          -1.477241             NaN          0.137009             NaN   \n",
-      "2          -1.213159             NaN          0.168635             NaN   \n",
-      "3           1.298292        0.642032          0.498483        0.051682   \n",
-      "4           0.302726        0.084140          0.800615        0.531355   \n",
-      "..               ...             ...               ...             ...   \n",
-      "95          0.015820        0.822601          1.498058        0.372018   \n",
-      "96          0.860156        0.360191          1.251528        0.776413   \n",
-      "97         -1.028471        0.127061         -0.211806        0.340804   \n",
-      "98          0.358827        0.522243          0.885058        0.930757   \n",
-      "99         -1.473126        0.769994          0.897570        0.858413   \n",
-      "\n",
-      "    feature_5_scaled  feature_5_lag3  \n",
-      "0          -1.296048             NaN  \n",
-      "1           1.213139             NaN  \n",
-      "2          -0.033878             NaN  \n",
-      "3           0.974296        0.103124  \n",
-      "4          -0.615178        0.902553  \n",
-      "..               ...             ...  \n",
-      "95         -0.510650        0.277381  \n",
-      "96          0.212210        0.188121  \n",
-      "97         -1.375737        0.463698  \n",
-      "98          1.438631        0.353352  \n",
-      "99          1.475718        0.583656  \n",
-      "\n",
-      "[100 rows x 17 columns], 'Executed Successfully': True}\n",
-      "{'Backend': 'modin', 'Transformed Data':      datetime  feature_1  feature_2  feature_3  feature_4  feature_5  target  \\\n",
-      "0  2023-01-01   0.374540   0.031429   0.642032   0.051682   0.103124      62   \n",
-      "1  2023-01-02   0.950714   0.636410   0.084140   0.531355   0.902553      16   \n",
-      "2  2023-01-03   0.731994   0.314356   0.161629   0.540635   0.505252      72   \n",
-      "3  2023-01-04   0.598658   0.508571   0.898554   0.637430   0.826457      32   \n",
-      "4  2023-01-05   0.156019   0.907566   0.606429   0.726091   0.320050      83   \n",
-      "..        ...        ...        ...        ...        ...        ...     ...   \n",
-      "95 2023-04-06   0.493796   0.349210   0.522243   0.930757   0.353352      63   \n",
-      "96 2023-04-07   0.522733   0.725956   0.769994   0.858413   0.583656      97   \n",
-      "97 2023-04-08   0.427541   0.897110   0.215821   0.428994   0.077735      37   \n",
-      "98 2023-04-09   0.025419   0.887086   0.622890   0.750871   0.974395      49   \n",
-      "99 2023-04-10   0.107891   0.779876   0.085347   0.754543   0.986211      97   \n",
-      "\n",
-      "    feature_1_scaled  feature_1_lag3  feature_2_scaled  feature_2_lag3  \\\n",
-      "0          -0.321493             NaN         -1.591213             NaN   \n",
-      "1           1.615296             NaN          0.472785             NaN   \n",
-      "2           0.880076             NaN         -0.625959             NaN   \n",
-      "3           0.431873        0.374540          0.036638        0.031429   \n",
-      "4          -1.056045        0.950714          1.397881        0.636410   \n",
-      "..               ...             ...               ...             ...   \n",
-      "95          0.079380        0.760785         -0.507050        0.900418   \n",
-      "96          0.176652        0.561277          0.778285        0.633101   \n",
-      "97         -0.143332        0.770967          1.362208        0.339030   \n",
-      "98         -1.495050        0.493796          1.328010        0.349210   \n",
-      "99         -1.217823        0.522733          0.962242        0.725956   \n",
-      "\n",
-      "    feature_3_scaled  feature_3_lag3  feature_4_scaled  feature_4_lag3  \\\n",
-      "0           0.424060             NaN         -1.497577             NaN   \n",
-      "1          -1.477241             NaN          0.137009             NaN   \n",
-      "2          -1.213159             NaN          0.168635             NaN   \n",
-      "3           1.298292        0.642032          0.498483        0.051682   \n",
-      "4           0.302726        0.084140          0.800615        0.531355   \n",
-      "..               ...             ...               ...             ...   \n",
-      "95          0.015820        0.822601          1.498058        0.372018   \n",
-      "96          0.860156        0.360191          1.251528        0.776413   \n",
-      "97         -1.028471        0.127061         -0.211806        0.340804   \n",
-      "98          0.358827        0.522243          0.885058        0.930757   \n",
-      "99         -1.473126        0.769994          0.897570        0.858413   \n",
-      "\n",
-      "    feature_5_scaled  feature_5_lag3  \n",
-      "0          -1.296048             NaN  \n",
-      "1           1.213139             NaN  \n",
-      "2          -0.033878             NaN  \n",
-      "3           0.974296        0.103124  \n",
-      "4          -0.615178        0.902553  \n",
-      "..               ...             ...  \n",
-      "95         -0.510650        0.277381  \n",
-      "96          0.212210        0.188121  \n",
-      "97         -1.375737        0.463698  \n",
-      "98          1.438631        0.353352  \n",
-      "99          1.475718        0.583656  \n",
-      "\n",
-      "[100 rows x 17 columns], 'Executed Successfully': True}\n",
-      "{'Backend': 'pyarrow', 'Transformed Data': pyarrow.Table\n",
-      "datetime: timestamp[ns]\n",
-      "feature_1: double\n",
-      "feature_2: double\n",
-      "feature_3: double\n",
-      "feature_4: double\n",
-      "feature_5: double\n",
-      "target: int64\n",
-      "feature_1_scaled: double\n",
-      "feature_1_lag3: double\n",
-      "feature_2_scaled: double\n",
-      "feature_2_lag3: double\n",
-      "feature_3_scaled: double\n",
-      "feature_3_lag3: double\n",
-      "feature_4_scaled: double\n",
-      "feature_4_lag3: double\n",
-      "feature_5_scaled: double\n",
-      "feature_5_lag3: double\n",
-      "----\n",
-      "datetime: [[2023-01-01 00:00:00.000000000,2023-01-02 00:00:00.000000000,2023-01-03 00:00:00.000000000,2023-01-04 00:00:00.000000000,2023-01-05 00:00:00.000000000,...,2023-04-06 00:00:00.000000000,2023-04-07 00:00:00.000000000,2023-04-08 00:00:00.000000000,2023-04-09 00:00:00.000000000,2023-04-10 00:00:00.000000000]]\n",
-      "feature_1: [[0.3745401188473625,0.9507143064099162,0.7319939418114051,0.5986584841970366,0.15601864044243652,...,0.49379559636439074,0.5227328293819941,0.42754101835854963,0.02541912674409519,0.10789142699330445]]\n",
-      "feature_2: [[0.03142918568673425,0.6364104112637804,0.3143559810763267,0.5085706911647028,0.907566473926093,...,0.3492095746126609,0.7259556788702394,0.8971102599525771,0.8870864242651173,0.7798755458576239]]\n",
-      "feature_3: [[0.6420316461542878,0.08413996499504883,0.16162871409461377,0.8985541885270792,0.6064290596595899,...,0.5222432600548044,0.7699935530986108,0.21582102749684318,0.6228904758190003,0.085347464993768]]\n",
-      "feature_4: [[0.0516817211686077,0.531354631568148,0.5406351216101065,0.6374299014982066,0.7260913337226615,...,0.9307573256035647,0.8584127518430118,0.42899402737501835,0.7508710677914974,0.7545428740846823]]\n",
-      "feature_5: [[0.10312386883593261,0.9025529066795667,0.5052523724478571,0.8264574661077416,0.32004960103061175,...,0.3533522280260528,0.5836561118508721,0.07773463696498484,0.9743948076661665,0.9862107444796029]]\n",
-      "target: [[62,16,72,32,83,...,63,97,37,49,97]]\n",
-      "feature_1_scaled: [[-0.32149253381634435,1.6152963609416058,0.8800756892140731,0.4318733240969416,-1.056044656727614,...,0.07938048250384727,0.17665195485253263,-0.14333190843375598,-1.4950502443638614,-1.2178225609725948]]\n",
-      "feature_1_lag3: [[null,null,null,0.3745401188473625,0.9507143064099162,...,0.7607850486168974,0.5612771975694962,0.770967179954561,0.49379559636439074,0.5227328293819941]]\n",
-      "feature_2_scaled: [[-1.5912133480589814,0.47278528858807944,-0.62595939427216,0.036637856505107154,1.3978813411470248,...,-0.5070503000723712,0.7782845378939408,1.3622081491975693,1.328010090946824,0.9622415389463301]]\n",
-      "..., 'Executed Successfully': True}\n",
-      "{'Backend': 'polars', 'Transformed Data': shape: (100, 17)\n",
-      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-      "│ datetime  ┆ feature_1 ┆ feature_2 ┆ feature_3 ┆ … ┆ feature_4 ┆ feature_4 ┆ feature_5 ┆ feature_ │\n",
-      "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ _scaled   ┆ _lag3     ┆ _scaled   ┆ 5_lag3   │\n",
-      "│ datetime[ ┆ f64       ┆ f64       ┆ f64       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
-      "│ ns]       ┆           ┆           ┆           ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
-      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-      "│ 2023-01-0 ┆ 0.37454   ┆ 0.031429  ┆ 0.642032  ┆ … ┆ -1.497577 ┆ null      ┆ -1.296048 ┆ null     │\n",
-      "│ 1         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-01-0 ┆ 0.950714  ┆ 0.63641   ┆ 0.08414   ┆ … ┆ 0.137009  ┆ null      ┆ 1.213139  ┆ null     │\n",
-      "│ 2         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-01-0 ┆ 0.731994  ┆ 0.314356  ┆ 0.161629  ┆ … ┆ 0.168635  ┆ null      ┆ -0.033878 ┆ null     │\n",
-      "│ 3         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-01-0 ┆ 0.598658  ┆ 0.508571  ┆ 0.898554  ┆ … ┆ 0.498483  ┆ 0.051682  ┆ 0.974296  ┆ 0.103124 │\n",
-      "│ 4         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-01-0 ┆ 0.156019  ┆ 0.907566  ┆ 0.606429  ┆ … ┆ 0.800615  ┆ 0.531355  ┆ -0.615178 ┆ 0.902553 │\n",
-      "│ 5         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ …         ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …        │\n",
-      "│ 2023-04-0 ┆ 0.493796  ┆ 0.34921   ┆ 0.522243  ┆ … ┆ 1.498058  ┆ 0.372018  ┆ -0.51065  ┆ 0.277381 │\n",
-      "│ 6         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-04-0 ┆ 0.522733  ┆ 0.725956  ┆ 0.769994  ┆ … ┆ 1.251528  ┆ 0.776413  ┆ 0.21221   ┆ 0.188121 │\n",
-      "│ 7         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-04-0 ┆ 0.427541  ┆ 0.89711   ┆ 0.215821  ┆ … ┆ -0.211806 ┆ 0.340804  ┆ -1.375737 ┆ 0.463698 │\n",
-      "│ 8         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-04-0 ┆ 0.025419  ┆ 0.887086  ┆ 0.62289   ┆ … ┆ 0.885058  ┆ 0.930757  ┆ 1.438631  ┆ 0.353352 │\n",
-      "│ 9         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 2023-04-1 ┆ 0.107891  ┆ 0.779876  ┆ 0.085347  ┆ … ┆ 0.89757   ┆ 0.858413  ┆ 1.475718  ┆ 0.583656 │\n",
-      "│ 0         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "│ 00:00:00  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
-      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘, 'Executed Successfully': True}\n",
-      "{'Backend': 'dask', 'Transformed Data': Dask DataFrame Structure:\n",
-      "                     datetime feature_1 feature_2 feature_3 feature_4 feature_5 target feature_1_scaled feature_1_lag3 feature_2_scaled feature_2_lag3 feature_3_scaled feature_3_lag3 feature_4_scaled feature_4_lag3 feature_5_scaled feature_5_lag3\n",
-      "npartitions=2                                                                                                                                                                                                                                         \n",
-      "0              datetime64[ns]   float64   float64   float64   float64   float64  int64          float64        float64          float64        float64          float64        float64          float64        float64          float64        float64\n",
-      "50                        ...       ...       ...       ...       ...       ...    ...              ...            ...              ...            ...              ...            ...              ...            ...              ...            ...\n",
-      "99                        ...       ...       ...       ...       ...       ...    ...              ...            ...              ...            ...              ...            ...              ...            ...              ...            ...\n",
-      "Dask Name: assign, 92 expressions\n",
-      "Expr=Assign(frame=df), 'Executed Successfully': True}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import modin.pandas as mpd\n",
-    "import polars as pl\n",
-    "import pyarrow as pa\n",
-    "import dask.dataframe as dd\n",
-    "import narwhals as nw\n",
-    "from narwhals.typing import FrameT\n",
-    "import numpy as np\n",
-    "from temporalscope.core.core_utils import TEMPORALSCOPE_CORE_BACKEND_TYPES\n",
-    "\n",
-    "# Constants\n",
-    "NUM_ROWS = 100\n",
-    "SEED = 42\n",
-    "\n",
-    "# Data generator function\n",
-    "def generate_data(num_rows: int = NUM_ROWS, backend: str = \"pandas\") -> pd.DataFrame:\n",
-    "    \"\"\"Generates a time-series DataFrame with multiple features and a target column.\"\"\"\n",
-    "    np.random.seed(SEED)\n",
-    "    data = pd.DataFrame({\n",
-    "        \"datetime\": pd.date_range(start=\"2023-01-01\", periods=num_rows, freq=\"D\"),\n",
-    "        **{f\"feature_{i}\": np.random.rand(num_rows) for i in range(1, 6)},  # Generating 5 random features\n",
-    "        \"target\": np.random.randint(0, 100, num_rows)  # Random integer target\n",
-    "    })\n",
-    "    \n",
-    "    # Convert to the specified backend\n",
-    "    if backend == \"modin\":\n",
-    "        return mpd.DataFrame(data)\n",
-    "    elif backend == \"polars\":\n",
-    "        return pl.DataFrame(data)\n",
-    "    elif backend == \"pyarrow\":\n",
-    "        return pa.Table.from_pandas(data)\n",
-    "    elif backend == \"dask\":\n",
-    "        return dd.from_pandas(data, npartitions=2)\n",
-    "    return data  # Default to Pandas DataFrame\n",
-    "\n",
-    "# Narwhals-compatible function for scaling and lagging features\n",
-    "@nw.narwhalify\n",
-    "def scale_and_lag_features_nw(df: FrameT, lag_steps: int = 3) -> FrameT:\n",
-    "    \"\"\"Applies scaling and lagging to each feature in a backend-agnostic way using Narwhals.\"\"\"\n",
-    "    transformations = []\n",
-    "    for col in df.columns:\n",
-    "        if col.startswith(\"feature_\"):\n",
-    "            transformations.extend([\n",
-    "                ((nw.col(col) - nw.col(col).mean()) / nw.col(col).std()).alias(f\"{col}_scaled\"),\n",
-    "                nw.col(col).shift(lag_steps).alias(f\"{col}_lag{lag_steps}\")\n",
-    "            ])\n",
-    "    return df.with_columns(*transformations)\n",
-    "\n",
-    "# Testing function across all backends\n",
-    "def test_backends():\n",
-    "    \"\"\"Tests `scale_and_lag_features_nw` on all supported TemporalScope backends.\"\"\"\n",
-    "    results = []\n",
-    "    for backend_name in TEMPORALSCOPE_CORE_BACKEND_TYPES.keys():\n",
-    "        data_df = generate_data(backend=backend_name)\n",
-    "        try:\n",
-    "            transformed_df = scale_and_lag_features_nw(data_df)\n",
-    "            results.append({\n",
-    "                \"Backend\": backend_name,\n",
-    "                \"Transformed Data\": transformed_df,\n",
-    "                \"Executed Successfully\": True\n",
-    "            })\n",
-    "            print(f\"{backend_name} -> Executed Successfully, Transformed Data:\\n{transformed_df}\\n\")\n",
-    "        except Exception as e:\n",
-    "            results.append({\n",
-    "                \"Backend\": backend_name,\n",
-    "                \"Executed Successfully\": False,\n",
-    "                \"Error\": str(e)\n",
-    "            })\n",
-    "            print(f\"{backend_name} -> Failed with error: {e}\\n\")\n",
-    "\n",
-    "    return results\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    # Run the backend compatibility tests\n",
-    "    backend_results = test_backends()\n",
-    "    print(\"\\n--- Summary of Backend Compatibility ---\")\n",
-    "    for result in backend_results:\n",
-    "        print(result)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "test",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/tutorial_notebooks/introduction/1_load_data_timeframe.ipynb b/tutorial_notebooks/introduction/1_load_data_timeframe.ipynb
index 0ccd094..28d9146 100644
--- a/tutorial_notebooks/introduction/1_load_data_timeframe.ipynb
+++ b/tutorial_notebooks/introduction/1_load_data_timeframe.ipynb
@@ -1,29 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Licensed to the Apache Software Foundation (ASF) under one\n",
-    "# or more contributor license agreements.  See the NOTICE file\n",
-    "# distributed with this work for additional information\n",
-    "# regarding copyright ownership.  The ASF licenses this file\n",
-    "# to you under the Apache License, Version 2.0 (the\n",
-    "# \"License\"); you may not use this file except in compliance\n",
-    "# with the License.  You may obtain a copy of the License at\n",
-    "#\n",
-    "#   http://www.apache.org/licenses/LICENSE-2.0\n",
-    "#\n",
-    "# Unless required by applicable law or agreed to in writing,\n",
-    "# software distributed under the License is distributed on an\n",
-    "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
-    "# KIND, either express or implied.  See the License for the\n",
-    "# specific language governing permissions and limitations\n",
-    "# under the License."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -50,19 +26,19 @@
     "\n",
     "## Engineering Design Overview\n",
     "\n",
-    "The `TimeFrame` class is designed with several key assumptions to ensure performance, scalability, and flexibility across temporal XAI workflows:\n",
+    "The `TimeFrame` class uses Narwhals for backend-agnostic DataFrame operations and is designed with several key assumptions:\n",
     "\n",
     "1. **Preprocessed Data Assumption**:\n",
     "   - TemporalScope assumes users provide clean, preprocessed data\n",
-    "   - Similar to TensorFlow and GluonTS, preprocessing (categorical encoding, missing data, scaling) should be handled before using TemporalScope\n",
+    "   - Similar to TensorFlow and GluonTS, preprocessing should be handled before using TemporalScope\n",
     "\n",
     "2. **Time Column Constraints**:\n",
     "   - `time_col` must be numeric index or timestamp\n",
-    "   - Critical for operations like sliding window partitioning and temporal XAI (e.g., MASV computations)\n",
+    "   - Critical for operations like sliding window partitioning and temporal XAI\n",
     "\n",
     "3. **Numeric Features Requirement**:\n",
     "   - All features (except `time_col`) must be numeric\n",
-    "   - Ensures compatibility with ML models and XAI techniques like SHAP\n",
+    "   - Ensures compatibility with ML models and XAI techniques\n",
     "\n",
     "4. **Universal Model Assumption**:\n",
     "   - Models operate on entire dataset without hidden groupings\n",
@@ -70,316 +46,166 @@
     "\n",
     "## Backend Support\n",
     "\n",
-    "TemporalScope supports multiple DataFrame backends through its core utilities:\n",
+    "TemporalScope leverages Narwhals for backend-agnostic operations, supporting:\n",
+    "\n",
+    "- **Production Environment**:\n",
+    "  - `pandas`: Core DataFrame library (default)\n",
+    "  - `narwhals`: Backend-agnostic operations\n",
     "\n",
-    "- **Core Backends**:\n",
-    "  - `pandas`: Core DataFrame library\n",
+    "- **Test Environment** (via hatch):\n",
     "  - `modin`: Parallelized Pandas operations\n",
     "  - `pyarrow`: Apache Arrow-based processing\n",
     "  - `polars`: High-performance Rust implementation\n",
     "  - `dask`: Distributed computing framework\n",
     "\n",
-    "This tutorial demonstrates loading data with pandas, polars, and modin as examples, but the principles apply across all supported backends.\n",
-    "\n",
-    "## Core Purpose\n",
-    "\n",
-    "TimeFrame's primary purpose is ensuring data quality and compatibility for:\n",
-    "- Computing Mean Absolute SHAP Values (MASV) on partitions\n",
-    "- Temporal feature importance analysis\n",
-    "- Integration with model-agnostic explainability tools\n",
-    "\n",
-    "By validating data upfront, TimeFrame ensures reliable XAI workflows downstream.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example 1: Default Mode: Implicit & Static Time Series\n",
-    "\n",
-    "This example demonstrates TimeFrame's default mode where time is treated as a static feature without strict ordering requirements. The following examples are for illustrative and academic purposes only. This software is distributed under the Apache License 2.0 \"AS IS\" and without warranties or conditions of any kind.\n",
-    "\n",
-    "| Domain | Example Use Case | Time Column Treatment | Benefits |\n",
-    "|--------|-----------------|----------------------|-----------|\n",
-    "| Engineering/Signal Processing | Sensor data analysis where multiple sensors record at different frequencies | Time as a feature allows handling asynchronous measurements | - Handles mixed sampling rates<br>- Supports missing timestamps<br>- Enables cross-sensor analysis |\n",
-    "| Financial Markets | Factor model analysis examining price dynamics over different windows | Timestamps from different exchanges/assets treated as features | - Handles market hours differences<br>- Supports varying trade frequencies<br>- Enables cross-asset analysis |\n",
-    "| Healthcare Analytics | Patient monitoring where different measurements occur at irregular intervals | Treatment timestamps as features rather than strict sequence | - Handles irregular visit schedules<br>- Supports missing measurements<br>- Enables cross-patient analysis |\n",
-    "\n",
-    "In this mode (enforce_temporal_uniqueness=False), TimeFrame treats time as just another feature, allowing for flexible analysis of mixed-frequency and asynchronous data. Let's demonstrate this default behavior:"
+    "This separation ensures lightweight production deployments while maintaining robust testing across backends."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "Loading dataset: 'macrodata'\n",
+      "======================================================================\n",
+      "DataFrame shape: (203, 13)\n",
+      "Target column: realgdp\n",
+      "======================================================================\n",
+      "TimeFrame Configuration:\n",
+      "Mode: single_target\n",
+      "Sort Order: Ascending\n",
+      "\n",
+      "Data Preview:\n",
+      "    realgdp  realcons  realinv  realgovt  realdpi    cpi     m1  tbilrate  \\\n",
+      "0  2710.349    1707.4  286.898   470.045   1886.9  28.98  139.7      2.82   \n",
+      "1  2778.801    1733.7  310.859   481.301   1919.7  29.15  141.7      3.08   \n",
+      "2  2775.488    1751.8  289.226   491.260   1916.4  29.35  140.5      3.82   \n",
+      "3  2785.204    1753.7  299.356   484.052   1931.3  29.37  140.0      4.33   \n",
+      "4  2847.699    1770.5  331.722   462.199   1955.5  29.54  139.6      3.50   \n",
+      "\n",
+      "   unemp      pop  infl  realint         ds  \n",
+      "0    5.8  177.146  0.00     0.00 1959-01-01  \n",
+      "1    5.1  177.830  2.34     0.74 1959-04-01  \n",
+      "2    5.3  178.657  2.74     1.09 1959-07-01  \n",
+      "3    5.6  179.386  0.27     4.06 1959-10-01  \n",
+      "4    5.2  180.007  2.31     1.19 1960-01-01  \n"
+     ]
+    }
+   ],
    "source": [
-    "import modin.pandas as mpd\n",
     "import pandas as pd\n",
-    "import polars as pl\n",
+    "import narwhals as nw\n",
     "\n",
-    "from temporalscope.core.core_utils import print_divider, get_temporalscope_backends\n",
-    "from temporalscope.core.temporal_data_loader import TimeFrame as tf\n",
+    "from temporalscope.core.temporal_data_loader import TimeFrame\n",
     "from temporalscope.datasets.datasets import DatasetLoader\n",
     "\n",
-    "# First, let's see all supported backends\n",
-    "print(\"Supported TemporalScope backends:\")\n",
-    "print(get_temporalscope_backends())\n",
-    "print_divider()\n",
+    "# Load example data\n",
+    "loader = DatasetLoader(\"macrodata\")\n",
+    "data = loader.load_data()\n",
     "\n",
-    "def init_timeframes_for_backends(target_col: str):\n",
-    "    \"\"\"Initialize TimeFrame objects for demonstration backends.\n",
-    "    \n",
-    "    :param target_col: The target column for prediction\n",
-    "    :type target_col: str\n",
-    "    :return: A dictionary containing TimeFrame objects for each backend\n",
-    "    :rtype: dict\n",
-    "    \"\"\"\n",
-    "    loader = DatasetLoader(\"macrodata\")\n",
-    "    timeframes = {}\n",
-    "    \n",
-    "    for backend in [\"pandas\", \"polars\", \"modin\"]:\n",
-    "        print(f\"Loading data with {backend} backend...\")\n",
-    "        data = loader.load_data(backend=backend)\n",
-    "        # Default mode: time treated as static feature\n",
-    "        timeframes[backend] = tf(data, time_col=\"ds\", target_col=target_col, enforce_temporal_uniqueness=False)\n",
-    "        print(f\"Successfully created TimeFrame with {backend} backend\")\n",
-    "        print_divider()\n",
-    "    \n",
-    "    return timeframes\n",
+    "# Create TimeFrame (default mode: time as static feature)\n",
+    "tf = TimeFrame(data, time_col=\"ds\", target_col=\"realgdp\")\n",
     "\n",
-    "if __name__ == \"__main__\":\n",
-    "    timeframes = init_timeframes_for_backends(target_col=\"realgdp\")\n",
-    "    \n",
-    "    # Demonstrate with Modin backend\n",
-    "    print(\"\\nDetailed Example with Modin Backend:\")\n",
-    "    macro_modin_tf = timeframes[\"modin\"]\n",
-    "    \n",
-    "    # Verify backend\n",
-    "    print(f\"Backend type: {macro_modin_tf.backend}\")\n",
-    "    \n",
-    "    print(\"\\nPreview of the DataFrame:\")\n",
-    "    print(macro_modin_tf.df.head())\n",
-    "    print_divider()\n",
-    "    \n",
-    "    print(\"TimeFrame Configuration:\")\n",
-    "    print(f\"Mode: {macro_modin_tf.mode}\")\n",
-    "    print(f\"Sort Order: {'Ascending' if macro_modin_tf.ascending else 'Descending'}\")\n",
-    "    print_divider()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Show all available attributes of TimeFrame\n",
-    "print(\"All TimeFrame attributes:\")\n",
-    "print([attr for attr in dir(macro_modin_tf) if not attr.startswith('_')])\n",
-    "print_divider()\n",
+    "# Display configuration\n",
+    "print(\"TimeFrame Configuration:\")\n",
+    "print(f\"Mode: {tf.mode}\")\n",
+    "print(f\"Sort Order: {'Ascending' if tf.ascending else 'Descending'}\")\n",
     "\n",
-    "# Show key properties\n",
-    "print(\"Key TimeFrame Properties:\")\n",
-    "print(f\"Backend: {macro_modin_tf.backend}\")\n",
-    "print(f\"Mode: {macro_modin_tf.mode}\")\n",
-    "print(f\"Sort Order: {'Ascending' if macro_modin_tf.ascending else 'Descending'}\")\n",
-    "print(f\"Metadata: {macro_modin_tf.metadata}\")\n",
-    "print_divider()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "macro_modin_tf.df.head()"
+    "# Preview data\n",
+    "print(\"\\nData Preview:\")\n",
+    "print(tf.df.head())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Example 2: Group-Level Temporal Uniqueness Validation\n",
-    "\n",
-    "The TimeFrame class supports validation of temporal uniqueness at the group level, which is essential for multi-entity time series applications. When enabled, this validation ensures that within each group (defined by id_col), timestamps are unique, while different groups can share the same timestamps. This aligns with modern approaches in time series analysis where multiple entities can be observed concurrently.\n",
-    "\n",
-    "Key points:\n",
-    "- `enforce_temporal_uniqueness`: When True, ensures unique timestamps within each group\n",
-    "- `id_col`: Defines the grouping entity (e.g., patient_id, sensor_id)\n",
-    "- Different groups can have events at the same timestamps\n",
-    "- Supports mixed-frequency and asynchronous observations\n",
-    "\n",
-    "Note: Modern machine learning and deep learning frameworks often treat observations as IID (Independent and Identically Distributed), making strict temporal ordering optional. This includes recent transformer architectures that can learn from irregularly sampled or mixed-frequency data. The choice of temporal validation should match your specific modeling requirements.\n",
-    "\n",
-    "| Domain | Use Case | Group-Level Uniqueness | Cross-Group Flexibility |\n",
-    "|--------|-----------|----------------------|------------------------|\n",
-    "| Time Series Forecasting | Multi-asset price prediction | Each asset requires unique timestamps in its history | Different assets can share observation times |\n",
-    "| Clinical Trials | Multi-patient monitoring | Each patient needs unique measurement times | Different patients can have concurrent visits |\n",
-    "| IoT Monitoring | Multi-sensor networks | Each sensor needs unique reading times | Different sensors can record simultaneously |\n",
-    "| Anomaly Detection | Multi-system monitoring | Each system needs unique state recordings | Different systems can have concurrent states |\n",
+    "## Example: Group-Level Temporal Uniqueness\n",
     "\n",
-    "This aligns with findings in recent literature on universal time series representation learning (Trirat et al., 2024) and mixed-frequency forecasting (Filho et al., 2024), where group-level temporal consistency is maintained while allowing cross-group temporal overlap."
+    "TimeFrame supports validation of temporal uniqueness at the group level, essential for multi-entity time series applications:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data with valid temporal uniqueness within groups:\n",
+      "   id  time  feature  target\n",
+      "0   1     1      0.1      10\n",
+      "2   2     1      0.3      30\n",
+      "1   1     2      0.2      20\n",
+      "3   2     3      0.4      40\n"
+     ]
+    }
+   ],
    "source": [
-    "import modin.pandas as mpd\n",
-    "import pandas as pd\n",
-    "import polars as pl\n",
-    "import numpy as np\n",
-    "\n",
-    "from temporalscope.core.core_utils import print_divider, get_temporalscope_backends, convert_to_backend\n",
-    "from temporalscope.core.temporal_data_loader import TimeFrame as tf\n",
-    "\n",
-    "# First, let's see all supported backends\n",
-    "print(\"Supported TemporalScope backends:\")\n",
-    "print(get_temporalscope_backends())\n",
-    "print_divider()\n",
-    "\n",
-    "# Create synthetic data with temporal uniqueness violations\n",
-    "# Case 1: Data with duplicate timestamps within same ID group\n",
-    "df_duplicates = pd.DataFrame({\n",
-    "    'id': [1, 1, 2, 2],  # Two groups with IDs 1 and 2\n",
-    "    'time': [1, 1, 2, 2],  # Duplicate timestamps within each group\n",
-    "    'feature1': [0.1, 0.2, 0.3, 0.4],\n",
-    "    'feature2': [1.1, 1.2, 1.3, 1.4],\n",
-    "    'target': [10, 20, 30, 40]\n",
-    "})\n",
-    "\n",
-    "print(\"Created DataFrame with temporal uniqueness violations (duplicate timestamps per ID):\")\n",
-    "print(df_duplicates)\n",
-    "print_divider()\n",
-    "\n",
-    "# Case 2: Valid data with unique timestamps within each ID group\n",
-    "df_valid = pd.DataFrame({\n",
-    "    'id': [1, 1, 2, 2],  # Two groups with IDs 1 and 2\n",
-    "    'time': [1, 2, 1, 3],  # Each ID has unique timestamps\n",
-    "    'feature1': [0.1, 0.2, 0.3, 0.4],\n",
-    "    'feature2': [1.1, 1.2, 1.3, 1.4],\n",
+    "# Create sample multi-entity data\n",
+    "df = pd.DataFrame({\n",
+    "    'id': [1, 1, 2, 2],\n",
+    "    'time': [1, 2, 1, 3],  # Note: Different groups can share timestamps\n",
+    "    'feature': [0.1, 0.2, 0.3, 0.4],\n",
     "    'target': [10, 20, 30, 40]\n",
     "})\n",
     "\n",
-    "print(\"Created DataFrame with valid temporal uniqueness (unique timestamps per ID):\")\n",
-    "print(df_valid)\n",
-    "print_divider()\n",
-    "\n",
-    "# Try to initialize with each backend and test temporal uniqueness validation\n",
-    "for backend in [\"pandas\", \"polars\", \"modin\"]:\n",
-    "    print(f\"\\nTesting with {backend} backend...\")\n",
-    "    \n",
-    "    # Test data with temporal uniqueness violations\n",
-    "    print(\"\\nTesting data with temporal uniqueness violations:\")\n",
-    "    data = convert_to_backend(df_duplicates, backend=backend)\n",
-    "    print(f\"Converted data type: {type(data)}\")\n",
-    "    try:\n",
-    "        timeframe = tf(data, \n",
-    "                      time_col=\"time\", \n",
-    "                      target_col=\"target\",\n",
-    "                      enforce_temporal_uniqueness=True,  # Enable temporal uniqueness validation\n",
-    "                      id_col=\"id\",  # Group by ID for validation\n",
-    "                      verbose=True\n",
-    "                     )\n",
-    "        print(\"WARNING: Expected validation error did not occur!\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"Got expected error with {backend} backend: {str(e)}\")\n",
-    "    \n",
-    "    # Test valid data\n",
-    "    print(\"\\nTesting valid data:\")\n",
-    "    data = convert_to_backend(df_valid, backend=backend)\n",
-    "    print(f\"Converted data type: {type(data)}\")\n",
-    "    try:\n",
-    "        timeframe = tf(data, \n",
-    "                      time_col=\"time\", \n",
-    "                      target_col=\"target\",\n",
-    "                      enforce_temporal_uniqueness=True,  # Enable temporal uniqueness validation\n",
-    "                      id_col=\"id\",  # Group by ID for validation\n",
-    "                      verbose=True\n",
-    "                     )\n",
-    "        print(\"Successfully created TimeFrame with valid data\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"Unexpected error with {backend} backend: {str(e)}\")\n",
-    "    \n",
-    "    print_divider()"
+    "# Create TimeFrame with group-level temporal validation\n",
+    "tf = TimeFrame(\n",
+    "    df,\n",
+    "    time_col=\"time\",\n",
+    "    target_col=\"target\",\n",
+    "    enforce_temporal_uniqueness=True,\n",
+    "    id_col=\"id\"\n",
+    ")\n",
+    "\n",
+    "print(\"Data with valid temporal uniqueness within groups:\")\n",
+    "print(tf.df)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Example 3: Temporal Uniqueness Validation - Invalid Case\n",
-    "\n",
-    "The TimeFrame class enforces temporal uniqueness within groups when id_col is provided. Let's examine a case where this validation fails due to duplicate timestamps within groups.\n",
-    "\n",
-    "## Invalid Case Example\n",
-    "In this example, we create a DataFrame where:\n",
-    "- Group 1 (id=1) has two rows with timestamp=1\n",
-    "- Group 2 (id=2) has two rows with timestamp=2\n",
+    "## Example: TimeFrame Metadata\n",
     "\n",
-    "This violates temporal uniqueness because each group contains duplicate timestamps. When we try to create a TimeFrame with this data and enforce_temporal_uniqueness=True, it will raise a ValueError with the message \"Duplicate timestamps in id_col 'id' column 'time'.\""
+    "TimeFrame includes a metadata container for extensibility and future ML framework integrations:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import modin.pandas as mpd\n",
-    "import pandas as pd\n",
-    "import polars as pl\n",
-    "import numpy as np\n",
-    "\n",
-    "from temporalscope.core.core_utils import print_divider, get_temporalscope_backends, convert_to_backend\n",
-    "from temporalscope.core.temporal_data_loader import TimeFrame as tf\n",
-    "\n",
-    "# Create synthetic data with temporal uniqueness violations\n",
-    "df_duplicates = pd.DataFrame({\n",
-    "    'id': [1, 1, 2, 2],      # Two groups with IDs 1 and 2\n",
-    "    'time': [1, 1, 2, 2],    # Duplicate timestamps within each group\n",
-    "    'feature1': [0.1, 0.2, 0.3, 0.4],\n",
-    "    'feature2': [1.1, 1.2, 1.3, 1.4],\n",
-    "    'target': [10, 20, 30, 40]\n",
-    "})\n",
-    "\n",
-    "print(\"Created DataFrame with temporal uniqueness violations (duplicate timestamps per ID):\")\n",
-    "print(df_duplicates)\n",
-    "print_divider()\n",
-    "\n",
-    "# Try to initialize with each backend\n",
-    "for backend in [\"pandas\", \"polars\", \"modin\"]:\n",
-    "    print(f\"\\nTesting with {backend} backend...\")\n",
-    "    data = convert_to_backend(df_duplicates, backend=backend)\n",
-    "    print(f\"Converted data type: {type(data)}\")\n",
-    "    try:\n",
-    "        timeframe = tf(data, \n",
-    "                      time_col=\"time\", \n",
-    "                      target_col=\"target\",\n",
-    "                      enforce_temporal_uniqueness=True,  # Enable temporal uniqueness validation\n",
-    "                      id_col=\"id\",                      # Group by ID for validation\n",
-    "                      verbose=True\n",
-    "                     )\n",
-    "        print(\"WARNING: Expected validation error did not occur!\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"Got expected error with {backend} backend: {str(e)}\")\n",
-    "    print_divider()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 3,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TimeFrame Metadata:\n",
+      "{'model_config': {'type': 'LSTM', 'framework': 'PyTorch', 'hyperparameters': {'hidden_size': 64, 'num_layers': 2}}}\n"
+     ]
+    }
+   ],
    "source": [
-    "# Example 4: TimeFrame Metadata for XAI Workflows\n",
-    "\n",
-    "TimeFrame's metadata container enables custom end-to-end workflows, particularly for XAI analysis. The metadata property can store temporal feature importance metrics like MASV (Moving Aggregated SHAP Values) and support custom partitioning schemes.\n",
+    "# Store custom metadata\n",
+    "tf.metadata['model_config'] = {\n",
+    "    'type': 'LSTM',\n",
+    "    'framework': 'PyTorch',\n",
+    "    'hyperparameters': {\n",
+    "        'hidden_size': 64,\n",
+    "        'num_layers': 2\n",
+    "    }\n",
+    "}\n",
     "\n",
-    "Note: This example is for illustrative and academic purposes only. This software is distributed under the Apache License 2.0 \"AS IS\" and without warranties or conditions of any kind."
+    "print(\"TimeFrame Metadata:\")\n",
+    "print(tf.metadata)"
    ]
   },
   {
@@ -387,54 +213,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import modin.pandas as mpd\n",
-    "import pandas as pd\n",
-    "import polars as pl\n",
-    "import numpy as np\n",
-    "\n",
-    "from temporalscope.core.core_utils import print_divider, get_temporalscope_backends, convert_to_backend\n",
-    "from temporalscope.core.temporal_data_loader import TimeFrame as tf\n",
-    "\n",
-    "# Create sample data\n",
-    "df = pd.DataFrame({\n",
-    "    'time': pd.date_range(start='2023-01-01', periods=5),\n",
-    "    'feature1': np.random.randn(5),\n",
-    "    'feature2': np.random.randn(5),\n",
-    "    'target': np.random.randn(5)\n",
-    "})\n",
-    "\n",
-    "# Initialize TimeFrame\n",
-    "timeframe = tf(df, time_col='time', target_col='target', verbose=True)\n",
-    "\n",
-    "# Store temporal feature importance metadata\n",
-    "timeframe.metadata['temporal_importance'] = {\n",
-    "    'config': {\n",
-    "        'window_size': 30,\n",
-    "        'stride': 10,\n",
-    "        'feature_importance': 'shap'\n",
-    "    },\n",
-    "    'partitions': [\n",
-    "        {\n",
-    "            'time_bounds': ('2023-01-01', '2023-01-30'),\n",
-    "            'feature_importance': {\n",
-    "                'feature1': 0.5,\n",
-    "                'feature2': 0.3\n",
-    "            }\n",
-    "        },\n",
-    "        {\n",
-    "            'time_bounds': ('2023-01-10', '2023-02-09'),\n",
-    "            'feature_importance': {\n",
-    "                'feature1': 0.4,\n",
-    "                'feature2': 0.6\n",
-    "            }\n",
-    "        }\n",
-    "    ]\n",
-    "}\n",
-    "\n",
-    "print(\"TimeFrame Metadata Structure:\")\n",
-    "print(timeframe.metadata['temporal_importance'])"
-   ]
+   "source": []
   }
  ],
  "metadata": {
@@ -457,5 +236,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/tutorial_notebooks/introduction/2_single_step_target_shifting.ipynb b/tutorial_notebooks/introduction/2_single_step_target_shifting.ipynb
index 51d3edb..ebeb7f6 100644
--- a/tutorial_notebooks/introduction/2_single_step_target_shifting.ipynb
+++ b/tutorial_notebooks/introduction/2_single_step_target_shifting.ipynb
@@ -8,19 +8,19 @@
     "\n",
     "## Engineering Design Overview\n",
     "\n",
-    "The `SingleStepTargetShifter` class provides backend-agnostic target shifting for time series data, following clear separation between validation and transformation phases:\n",
+    "The `SingleStepTargetShifter` class uses Narwhals for backend-agnostic target shifting in time series data, following a clear separation between validation and transformation phases:\n",
     "\n",
     "### Core Components\n",
     "\n",
     "1. **Validation Phase (fit)**:\n",
     "   - Validates TimeFrame or supported DataFrame type\n",
     "   - Ensures target column is set or can be inferred\n",
-    "   - No Narwhals operations at this stage\n",
+    "   - Performs input validation before any operations\n",
     "\n",
     "2. **Transformation Phase (transform)**:\n",
-    "   - Uses pure Narwhals operations\n",
-    "   - Shifts target using backend-agnostic operations\n",
+    "   - Uses Narwhals operations for backend-agnostic shifting\n",
     "   - Preserves TimeFrame metadata if present\n",
+    "   - Returns consistent output across all backends\n",
     "\n",
     "### Engineering Design Assumptions\n",
     "\n",
@@ -31,21 +31,19 @@
     "   - Supports scalar target prediction tasks\n",
     "\n",
     "2. **Backend Agnostic**:\n",
-    "   - Validation in fit() before any operations\n",
-    "   - Pure Narwhals operations in transform()\n",
-    "   - Clean separation of concerns\n",
+    "   - Uses Narwhals for all DataFrame operations\n",
+    "   - Production environment uses pandas + narwhals\n",
+    "   - Test environment supports multiple backends via hatch\n",
     "\n",
     "3. **Input Handling**:\n",
     "   - TimeFrame: Uses existing metadata\n",
     "   - DataFrame: Validates in fit\n",
-    "   - numpy array: Converts in fit\n",
-    "\n",
-    "## Example 1: Using with TimeFrame"
+    "   - numpy array: Converts in fit"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -72,7 +70,9 @@
       "2    5.3  178.657  2.74     1.09 1959-07-01  \n",
       "3    5.6  179.386  0.27     4.06 1959-10-01  \n",
       "4    5.2  180.007  2.31     1.19 1960-01-01  \n",
-      "======================================================================\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\n",
       "Initialized SingleStepTargetShifter with target_col=None, n_lags=1\n",
       "Rows before: 203; Rows after: 202; Dropped: 1\n",
       "\n",
@@ -90,37 +90,35 @@
       "2  178.657  2.74     1.09 1959-07-01         2785.204  \n",
       "3  179.386  0.27     4.06 1959-10-01         2847.699  \n",
       "4  180.007  2.31     1.19 1960-01-01         2834.390  \n",
-      "======================================================================\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\n",
       "TimeFrame Configuration:\n",
-      "Backend: pandas\n",
-      "Mode: single_step\n",
+      "Mode: single_target\n",
       "Sort Order: Ascending\n"
      ]
     }
    ],
    "source": [
     "import pandas as pd\n",
-    "import polars as pl\n",
-    "import modin.pandas as mpd\n",
+    "import narwhals as nw\n",
     "\n",
-    "from temporalscope.core.core_utils import print_divider\n",
     "from temporalscope.core.temporal_data_loader import TimeFrame\n",
     "from temporalscope.datasets.datasets import DatasetLoader\n",
     "from temporalscope.target_shifters.single_step import SingleStepTargetShifter\n",
     "\n",
-    "# Load data using DatasetLoader\n",
+    "# Load example data\n",
     "loader = DatasetLoader(\"macrodata\")\n",
-    "data = loader.load_data(backend=\"pandas\")\n",
+    "data = loader.load_data()\n",
     "\n",
     "# Create TimeFrame\n",
     "tf = TimeFrame(data, time_col=\"ds\", target_col=\"realgdp\")\n",
     "\n",
     "print(\"Original TimeFrame:\")\n",
     "print(tf.df.head())\n",
-    "print_divider()\n",
+    "print(\"\\n\" + \"-\"*80 + \"\\n\")\n",
     "\n",
-    "# Initialize SingleStepTargetShifter\n",
-    "# Note: target_col can be inferred from TimeFrame\n",
+    "# Initialize SingleStepTargetShifter (target_col inferred from TimeFrame)\n",
     "shifter = SingleStepTargetShifter(n_lags=1, verbose=True)\n",
     "\n",
     "# Transform data - target will be shifted for future prediction\n",
@@ -128,377 +126,32 @@
     "\n",
     "print(\"\\nTransformed TimeFrame:\")\n",
     "print(transformed_tf.df.head())\n",
-    "print_divider()\n",
+    "print(\"\\n\" + \"-\"*80 + \"\\n\")\n",
     "\n",
     "# Verify metadata preservation\n",
     "print(\"TimeFrame Configuration:\")\n",
-    "print(f\"Backend: {transformed_tf.backend}\")\n",
     "print(f\"Mode: {transformed_tf.mode}\")\n",
     "print(f\"Sort Order: {'Ascending' if transformed_tf.ascending else 'Descending'}\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original TimeFrame columns:\n",
-      "Index(['realgdp', 'realcons', 'realinv', 'realgovt', 'realdpi', 'cpi', 'm1',\n",
-      "       'tbilrate', 'unemp', 'pop', 'infl', 'realint', 'ds'],\n",
-      "      dtype='object')\n",
-      "\n",
-      "First few rows:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>realgdp</th>\n",
-       "      <th>realcons</th>\n",
-       "      <th>realinv</th>\n",
-       "      <th>realgovt</th>\n",
-       "      <th>realdpi</th>\n",
-       "      <th>cpi</th>\n",
-       "      <th>m1</th>\n",
-       "      <th>tbilrate</th>\n",
-       "      <th>unemp</th>\n",
-       "      <th>pop</th>\n",
-       "      <th>infl</th>\n",
-       "      <th>realint</th>\n",
-       "      <th>ds</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2710.349</td>\n",
-       "      <td>1707.4</td>\n",
-       "      <td>286.898</td>\n",
-       "      <td>470.045</td>\n",
-       "      <td>1886.9</td>\n",
-       "      <td>28.98</td>\n",
-       "      <td>139.7</td>\n",
-       "      <td>2.82</td>\n",
-       "      <td>5.8</td>\n",
-       "      <td>177.146</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1959-01-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2778.801</td>\n",
-       "      <td>1733.7</td>\n",
-       "      <td>310.859</td>\n",
-       "      <td>481.301</td>\n",
-       "      <td>1919.7</td>\n",
-       "      <td>29.15</td>\n",
-       "      <td>141.7</td>\n",
-       "      <td>3.08</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>177.830</td>\n",
-       "      <td>2.34</td>\n",
-       "      <td>0.74</td>\n",
-       "      <td>1959-04-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2775.488</td>\n",
-       "      <td>1751.8</td>\n",
-       "      <td>289.226</td>\n",
-       "      <td>491.260</td>\n",
-       "      <td>1916.4</td>\n",
-       "      <td>29.35</td>\n",
-       "      <td>140.5</td>\n",
-       "      <td>3.82</td>\n",
-       "      <td>5.3</td>\n",
-       "      <td>178.657</td>\n",
-       "      <td>2.74</td>\n",
-       "      <td>1.09</td>\n",
-       "      <td>1959-07-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2785.204</td>\n",
-       "      <td>1753.7</td>\n",
-       "      <td>299.356</td>\n",
-       "      <td>484.052</td>\n",
-       "      <td>1931.3</td>\n",
-       "      <td>29.37</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>4.33</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>179.386</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>4.06</td>\n",
-       "      <td>1959-10-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2847.699</td>\n",
-       "      <td>1770.5</td>\n",
-       "      <td>331.722</td>\n",
-       "      <td>462.199</td>\n",
-       "      <td>1955.5</td>\n",
-       "      <td>29.54</td>\n",
-       "      <td>139.6</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>180.007</td>\n",
-       "      <td>2.31</td>\n",
-       "      <td>1.19</td>\n",
-       "      <td>1960-01-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    realgdp  realcons  realinv  realgovt  realdpi    cpi     m1  tbilrate  \\\n",
-       "0  2710.349    1707.4  286.898   470.045   1886.9  28.98  139.7      2.82   \n",
-       "1  2778.801    1733.7  310.859   481.301   1919.7  29.15  141.7      3.08   \n",
-       "2  2775.488    1751.8  289.226   491.260   1916.4  29.35  140.5      3.82   \n",
-       "3  2785.204    1753.7  299.356   484.052   1931.3  29.37  140.0      4.33   \n",
-       "4  2847.699    1770.5  331.722   462.199   1955.5  29.54  139.6      3.50   \n",
-       "\n",
-       "   unemp      pop  infl  realint         ds  \n",
-       "0    5.8  177.146  0.00     0.00 1959-01-01  \n",
-       "1    5.1  177.830  2.34     0.74 1959-04-01  \n",
-       "2    5.3  178.657  2.74     1.09 1959-07-01  \n",
-       "3    5.6  179.386  0.27     4.06 1959-10-01  \n",
-       "4    5.2  180.007  2.31     1.19 1960-01-01  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# You can explore the TimeFrame objects further here\n",
-    "\n",
-    "# Original TimeFrame data\n",
-    "print(\"Original TimeFrame columns:\")\n",
-    "print(tf.df.columns)\n",
-    "print(\"\\nFirst few rows:\")\n",
-    "tf.df.head()\n",
-    "\n",
-    "# Note: The target column 'realgdp' is present in the original data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Transformed TimeFrame columns:\n",
-      "Index(['realcons', 'realinv', 'realgovt', 'realdpi', 'cpi', 'm1', 'tbilrate',\n",
-      "       'unemp', 'pop', 'infl', 'realint', 'ds', 'realgdp_shift_1'],\n",
-      "      dtype='object')\n",
-      "\n",
-      "First few rows:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>realcons</th>\n",
-       "      <th>realinv</th>\n",
-       "      <th>realgovt</th>\n",
-       "      <th>realdpi</th>\n",
-       "      <th>cpi</th>\n",
-       "      <th>m1</th>\n",
-       "      <th>tbilrate</th>\n",
-       "      <th>unemp</th>\n",
-       "      <th>pop</th>\n",
-       "      <th>infl</th>\n",
-       "      <th>realint</th>\n",
-       "      <th>ds</th>\n",
-       "      <th>realgdp_shift_1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1707.4</td>\n",
-       "      <td>286.898</td>\n",
-       "      <td>470.045</td>\n",
-       "      <td>1886.9</td>\n",
-       "      <td>28.98</td>\n",
-       "      <td>139.7</td>\n",
-       "      <td>2.82</td>\n",
-       "      <td>5.8</td>\n",
-       "      <td>177.146</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1959-01-01</td>\n",
-       "      <td>2778.801</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1733.7</td>\n",
-       "      <td>310.859</td>\n",
-       "      <td>481.301</td>\n",
-       "      <td>1919.7</td>\n",
-       "      <td>29.15</td>\n",
-       "      <td>141.7</td>\n",
-       "      <td>3.08</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>177.830</td>\n",
-       "      <td>2.34</td>\n",
-       "      <td>0.74</td>\n",
-       "      <td>1959-04-01</td>\n",
-       "      <td>2775.488</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1751.8</td>\n",
-       "      <td>289.226</td>\n",
-       "      <td>491.260</td>\n",
-       "      <td>1916.4</td>\n",
-       "      <td>29.35</td>\n",
-       "      <td>140.5</td>\n",
-       "      <td>3.82</td>\n",
-       "      <td>5.3</td>\n",
-       "      <td>178.657</td>\n",
-       "      <td>2.74</td>\n",
-       "      <td>1.09</td>\n",
-       "      <td>1959-07-01</td>\n",
-       "      <td>2785.204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1753.7</td>\n",
-       "      <td>299.356</td>\n",
-       "      <td>484.052</td>\n",
-       "      <td>1931.3</td>\n",
-       "      <td>29.37</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>4.33</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>179.386</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>4.06</td>\n",
-       "      <td>1959-10-01</td>\n",
-       "      <td>2847.699</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1770.5</td>\n",
-       "      <td>331.722</td>\n",
-       "      <td>462.199</td>\n",
-       "      <td>1955.5</td>\n",
-       "      <td>29.54</td>\n",
-       "      <td>139.6</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>180.007</td>\n",
-       "      <td>2.31</td>\n",
-       "      <td>1.19</td>\n",
-       "      <td>1960-01-01</td>\n",
-       "      <td>2834.390</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   realcons  realinv  realgovt  realdpi    cpi     m1  tbilrate  unemp  \\\n",
-       "0    1707.4  286.898   470.045   1886.9  28.98  139.7      2.82    5.8   \n",
-       "1    1733.7  310.859   481.301   1919.7  29.15  141.7      3.08    5.1   \n",
-       "2    1751.8  289.226   491.260   1916.4  29.35  140.5      3.82    5.3   \n",
-       "3    1753.7  299.356   484.052   1931.3  29.37  140.0      4.33    5.6   \n",
-       "4    1770.5  331.722   462.199   1955.5  29.54  139.6      3.50    5.2   \n",
-       "\n",
-       "       pop  infl  realint         ds  realgdp_shift_1  \n",
-       "0  177.146  0.00     0.00 1959-01-01         2778.801  \n",
-       "1  177.830  2.34     0.74 1959-04-01         2775.488  \n",
-       "2  178.657  2.74     1.09 1959-07-01         2785.204  \n",
-       "3  179.386  0.27     4.06 1959-10-01         2847.699  \n",
-       "4  180.007  2.31     1.19 1960-01-01         2834.390  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Explore the transformed TimeFrame\n",
-    "\n",
-    "# Note: The original target column is replaced with a shifted version\n",
-    "print(\"Transformed TimeFrame columns:\")\n",
-    "print(transformed_tf.df.columns)\n",
-    "print(\"\\nFirst few rows:\")\n",
-    "transformed_tf.df.head()\n",
-    "\n",
-    "# Notice how 'realgdp' is now 'realgdp_shift_1', containing future values"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Example 2: Using with DataFrame Directly\n",
+    "## Example: Using with DataFrame Directly\n",
     "\n",
-    "SingleStepTargetShifter also works directly with DataFrames from any supported backend (pandas, polars, modin, etc.)."
+    "SingleStepTargetShifter also works directly with DataFrames:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "Using pandas backend:\n",
       "======================================================================\n",
       "Loading dataset: 'macrodata'\n",
       "======================================================================\n",
@@ -507,461 +160,7 @@
       "======================================================================\n",
       "Initialized SingleStepTargetShifter with target_col=realgdp, n_lags=1\n",
       "Rows before: 203; Rows after: 202; Dropped: 1\n",
-      "Original shape: (203, 13)\n",
-      "Transformed shape: (202, 13)\n",
-      "Target column: realgdp_shift_1\n",
-      "======================================================================\n",
-      "\n",
-      "Using polars backend:\n",
-      "======================================================================\n",
-      "Loading dataset: 'macrodata'\n",
-      "======================================================================\n",
-      "DataFrame shape: (203, 13)\n",
-      "Target column: realgdp\n",
-      "======================================================================\n",
-      "Initialized SingleStepTargetShifter with target_col=realgdp, n_lags=1\n",
-      "Rows before: 203; Rows after: 202; Dropped: 1\n",
-      "Original shape: (203, 13)\n",
-      "Transformed shape: (202, 13)\n",
-      "Target column: realgdp_shift_1\n",
-      "======================================================================\n",
-      "\n",
-      "Using modin backend:\n",
-      "======================================================================\n",
-      "Loading dataset: 'macrodata'\n",
-      "======================================================================\n",
-      "DataFrame shape: (203, 13)\n",
-      "Target column: realgdp\n",
-      "======================================================================\n",
-      "Initialized SingleStepTargetShifter with target_col=realgdp, n_lags=1\n",
-      "Rows before: 203; Rows after: 202; Dropped: 1\n",
-      "Original shape: (203, 13)\n",
-      "Transformed shape: (202, 13)\n",
-      "Target column: realgdp_shift_1\n",
-      "======================================================================\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "UserWarning: <function DataFrame.shift> is not currently supported by PandasOnRay, defaulting to pandas implementation.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import polars as pl\n",
-    "import modin.pandas as mpd\n",
-    "\n",
-    "from temporalscope.core.core_utils import print_divider\n",
-    "from temporalscope.datasets.datasets import DatasetLoader\n",
-    "from temporalscope.target_shifters.single_step import SingleStepTargetShifter\n",
-    "\n",
-    "# Load data using DatasetLoader\n",
-    "loader = DatasetLoader(\"macrodata\")\n",
-    "\n",
-    "# Demonstrate with different backends\n",
-    "for backend in [\"pandas\", \"polars\", \"modin\"]:\n",
-    "    print(f\"\\nUsing {backend} backend:\")\n",
-    "    \n",
-    "    # Load data in specific backend\n",
-    "    data = loader.load_data(backend=backend)\n",
-    "    \n",
-    "    # Initialize SingleStepTargetShifter\n",
-    "    # Note: target_col must be specified when using raw DataFrames\n",
-    "    shifter = SingleStepTargetShifter(target_col=\"realgdp\", n_lags=1, verbose=True)\n",
-    "    \n",
-    "    # Transform data\n",
-    "    transformed = shifter.fit_transform(data)\n",
-    "    \n",
-    "    print(f\"Original shape: {data.shape}\")\n",
-    "    print(f\"Transformed shape: {transformed.shape}\")\n",
-    "    print(f\"Target column: {shifter.target_col}_shift_{shifter.n_lags}\")\n",
-    "    print_divider()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "======================================================================\n",
-      "Loading dataset: 'macrodata'\n",
-      "======================================================================\n",
-      "DataFrame shape: (203, 13)\n",
-      "Target column: realgdp\n",
-      "======================================================================\n",
-      "Original pandas DataFrame:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>realgdp</th>\n",
-       "      <th>realcons</th>\n",
-       "      <th>realinv</th>\n",
-       "      <th>realgovt</th>\n",
-       "      <th>realdpi</th>\n",
-       "      <th>cpi</th>\n",
-       "      <th>m1</th>\n",
-       "      <th>tbilrate</th>\n",
-       "      <th>unemp</th>\n",
-       "      <th>pop</th>\n",
-       "      <th>infl</th>\n",
-       "      <th>realint</th>\n",
-       "      <th>ds</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2710.349</td>\n",
-       "      <td>1707.4</td>\n",
-       "      <td>286.898</td>\n",
-       "      <td>470.045</td>\n",
-       "      <td>1886.9</td>\n",
-       "      <td>28.98</td>\n",
-       "      <td>139.7</td>\n",
-       "      <td>2.82</td>\n",
-       "      <td>5.8</td>\n",
-       "      <td>177.146</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1959-01-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2778.801</td>\n",
-       "      <td>1733.7</td>\n",
-       "      <td>310.859</td>\n",
-       "      <td>481.301</td>\n",
-       "      <td>1919.7</td>\n",
-       "      <td>29.15</td>\n",
-       "      <td>141.7</td>\n",
-       "      <td>3.08</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>177.830</td>\n",
-       "      <td>2.34</td>\n",
-       "      <td>0.74</td>\n",
-       "      <td>1959-04-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2775.488</td>\n",
-       "      <td>1751.8</td>\n",
-       "      <td>289.226</td>\n",
-       "      <td>491.260</td>\n",
-       "      <td>1916.4</td>\n",
-       "      <td>29.35</td>\n",
-       "      <td>140.5</td>\n",
-       "      <td>3.82</td>\n",
-       "      <td>5.3</td>\n",
-       "      <td>178.657</td>\n",
-       "      <td>2.74</td>\n",
-       "      <td>1.09</td>\n",
-       "      <td>1959-07-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2785.204</td>\n",
-       "      <td>1753.7</td>\n",
-       "      <td>299.356</td>\n",
-       "      <td>484.052</td>\n",
-       "      <td>1931.3</td>\n",
-       "      <td>29.37</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>4.33</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>179.386</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>4.06</td>\n",
-       "      <td>1959-10-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2847.699</td>\n",
-       "      <td>1770.5</td>\n",
-       "      <td>331.722</td>\n",
-       "      <td>462.199</td>\n",
-       "      <td>1955.5</td>\n",
-       "      <td>29.54</td>\n",
-       "      <td>139.6</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>180.007</td>\n",
-       "      <td>2.31</td>\n",
-       "      <td>1.19</td>\n",
-       "      <td>1960-01-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    realgdp  realcons  realinv  realgovt  realdpi    cpi     m1  tbilrate  \\\n",
-       "0  2710.349    1707.4  286.898   470.045   1886.9  28.98  139.7      2.82   \n",
-       "1  2778.801    1733.7  310.859   481.301   1919.7  29.15  141.7      3.08   \n",
-       "2  2775.488    1751.8  289.226   491.260   1916.4  29.35  140.5      3.82   \n",
-       "3  2785.204    1753.7  299.356   484.052   1931.3  29.37  140.0      4.33   \n",
-       "4  2847.699    1770.5  331.722   462.199   1955.5  29.54  139.6      3.50   \n",
-       "\n",
-       "   unemp      pop  infl  realint         ds  \n",
-       "0    5.8  177.146  0.00     0.00 1959-01-01  \n",
-       "1    5.1  177.830  2.34     0.74 1959-04-01  \n",
-       "2    5.3  178.657  2.74     1.09 1959-07-01  \n",
-       "3    5.6  179.386  0.27     4.06 1959-10-01  \n",
-       "4    5.2  180.007  2.31     1.19 1960-01-01  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Let's examine the pandas DataFrame in detail\n",
-    "\n",
-    "# Load pandas data\n",
-    "pandas_data = loader.load_data(backend=\"pandas\")\n",
-    "print(\"Original pandas DataFrame:\")\n",
-    "pandas_data.head()\n",
-    "\n",
-    "# Note: You can see the original 'realgdp' column here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Transformed pandas DataFrame:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>realcons</th>\n",
-       "      <th>realinv</th>\n",
-       "      <th>realgovt</th>\n",
-       "      <th>realdpi</th>\n",
-       "      <th>cpi</th>\n",
-       "      <th>m1</th>\n",
-       "      <th>tbilrate</th>\n",
-       "      <th>unemp</th>\n",
-       "      <th>pop</th>\n",
-       "      <th>infl</th>\n",
-       "      <th>realint</th>\n",
-       "      <th>ds</th>\n",
-       "      <th>realgdp_shift_1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1707.4</td>\n",
-       "      <td>286.898</td>\n",
-       "      <td>470.045</td>\n",
-       "      <td>1886.9</td>\n",
-       "      <td>28.98</td>\n",
-       "      <td>139.7</td>\n",
-       "      <td>2.82</td>\n",
-       "      <td>5.8</td>\n",
-       "      <td>177.146</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1959-01-01</td>\n",
-       "      <td>2778.801</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1733.7</td>\n",
-       "      <td>310.859</td>\n",
-       "      <td>481.301</td>\n",
-       "      <td>1919.7</td>\n",
-       "      <td>29.15</td>\n",
-       "      <td>141.7</td>\n",
-       "      <td>3.08</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>177.830</td>\n",
-       "      <td>2.34</td>\n",
-       "      <td>0.74</td>\n",
-       "      <td>1959-04-01</td>\n",
-       "      <td>2775.488</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1751.8</td>\n",
-       "      <td>289.226</td>\n",
-       "      <td>491.260</td>\n",
-       "      <td>1916.4</td>\n",
-       "      <td>29.35</td>\n",
-       "      <td>140.5</td>\n",
-       "      <td>3.82</td>\n",
-       "      <td>5.3</td>\n",
-       "      <td>178.657</td>\n",
-       "      <td>2.74</td>\n",
-       "      <td>1.09</td>\n",
-       "      <td>1959-07-01</td>\n",
-       "      <td>2785.204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1753.7</td>\n",
-       "      <td>299.356</td>\n",
-       "      <td>484.052</td>\n",
-       "      <td>1931.3</td>\n",
-       "      <td>29.37</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>4.33</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>179.386</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>4.06</td>\n",
-       "      <td>1959-10-01</td>\n",
-       "      <td>2847.699</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1770.5</td>\n",
-       "      <td>331.722</td>\n",
-       "      <td>462.199</td>\n",
-       "      <td>1955.5</td>\n",
-       "      <td>29.54</td>\n",
-       "      <td>139.6</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>180.007</td>\n",
-       "      <td>2.31</td>\n",
-       "      <td>1.19</td>\n",
-       "      <td>1960-01-01</td>\n",
-       "      <td>2834.390</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   realcons  realinv  realgovt  realdpi    cpi     m1  tbilrate  unemp  \\\n",
-       "0    1707.4  286.898   470.045   1886.9  28.98  139.7      2.82    5.8   \n",
-       "1    1733.7  310.859   481.301   1919.7  29.15  141.7      3.08    5.1   \n",
-       "2    1751.8  289.226   491.260   1916.4  29.35  140.5      3.82    5.3   \n",
-       "3    1753.7  299.356   484.052   1931.3  29.37  140.0      4.33    5.6   \n",
-       "4    1770.5  331.722   462.199   1955.5  29.54  139.6      3.50    5.2   \n",
-       "\n",
-       "       pop  infl  realint         ds  realgdp_shift_1  \n",
-       "0  177.146  0.00     0.00 1959-01-01         2778.801  \n",
-       "1  177.830  2.34     0.74 1959-04-01         2775.488  \n",
-       "2  178.657  2.74     1.09 1959-07-01         2785.204  \n",
-       "3  179.386  0.27     4.06 1959-10-01         2847.699  \n",
-       "4  180.007  2.31     1.19 1960-01-01         2834.390  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Transform pandas data\n",
-    "shifter = SingleStepTargetShifter(target_col=\"realgdp\", n_lags=1)\n",
-    "transformed_pandas = shifter.fit_transform(pandas_data)\n",
-    "\n",
-    "print(\"Transformed pandas DataFrame:\")\n",
-    "transformed_pandas.head()\n",
-    "\n",
-    "# Note: 'realgdp' is now 'realgdp_shift_1' containing future values\n",
-    "# The DataFrame has one fewer row due to the shift"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Supported TemporalScope backends:\n",
-      "['pandas', 'modin', 'pyarrow', 'polars', 'dask']\n",
-      "======================================================================\n",
-      "Loading data with pandas backend...\n",
-      "======================================================================\n",
-      "Loading dataset: 'macrodata'\n",
-      "======================================================================\n",
-      "DataFrame shape: (203, 13)\n",
-      "Target column: realgdp\n",
-      "======================================================================\n",
-      "Successfully created TimeFrame with pandas backend\n",
-      "======================================================================\n",
-      "Loading data with polars backend...\n",
-      "======================================================================\n",
-      "Loading dataset: 'macrodata'\n",
-      "======================================================================\n",
-      "DataFrame shape: (203, 13)\n",
-      "Target column: realgdp\n",
-      "======================================================================\n",
-      "Successfully created TimeFrame with polars backend\n",
-      "======================================================================\n",
-      "Loading data with modin backend...\n",
-      "======================================================================\n",
-      "Loading dataset: 'macrodata'\n",
-      "======================================================================\n",
-      "DataFrame shape: (203, 13)\n",
-      "Target column: realgdp\n",
-      "======================================================================\n",
-      "Successfully created TimeFrame with modin backend\n",
-      "======================================================================\n",
-      "\n",
-      "Detailed Example with Modin Backend:\n",
-      "Backend type: modin\n",
-      "\n",
-      "Preview of the DataFrame:\n",
+      "Original DataFrame:\n",
       "    realgdp  realcons  realinv  realgovt  realdpi    cpi     m1  tbilrate  \\\n",
       "0  2710.349    1707.4  286.898   470.045   1886.9  28.98  139.7      2.82   \n",
       "1  2778.801    1733.7  310.859   481.301   1919.7  29.15  141.7      3.08   \n",
@@ -975,75 +174,53 @@
       "2    5.3  178.657  2.74     1.09 1959-07-01  \n",
       "3    5.6  179.386  0.27     4.06 1959-10-01  \n",
       "4    5.2  180.007  2.31     1.19 1960-01-01  \n",
-      "======================================================================\n",
-      "TimeFrame Configuration:\n",
-      "Mode: single_step\n",
-      "Sort Order: Ascending\n",
-      "======================================================================\n"
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\n",
+      "Transformed DataFrame:\n",
+      "   realcons  realinv  realgovt  realdpi    cpi     m1  tbilrate  unemp  \\\n",
+      "0    1707.4  286.898   470.045   1886.9  28.98  139.7      2.82    5.8   \n",
+      "1    1733.7  310.859   481.301   1919.7  29.15  141.7      3.08    5.1   \n",
+      "2    1751.8  289.226   491.260   1916.4  29.35  140.5      3.82    5.3   \n",
+      "3    1753.7  299.356   484.052   1931.3  29.37  140.0      4.33    5.6   \n",
+      "4    1770.5  331.722   462.199   1955.5  29.54  139.6      3.50    5.2   \n",
+      "\n",
+      "       pop  infl  realint         ds  realgdp_shift_1  \n",
+      "0  177.146  0.00     0.00 1959-01-01         2778.801  \n",
+      "1  177.830  2.34     0.74 1959-04-01         2775.488  \n",
+      "2  178.657  2.74     1.09 1959-07-01         2785.204  \n",
+      "3  179.386  0.27     4.06 1959-10-01         2847.699  \n",
+      "4  180.007  2.31     1.19 1960-01-01         2834.390  \n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\n",
+      "Original shape: (203, 13)\n",
+      "Transformed shape: (202, 13)\n",
+      "Target column: realgdp_shift_1\n"
      ]
     }
    ],
    "source": [
-    "import modin.pandas as mpd\n",
-    "import pandas as pd\n",
-    "import polars as pl\n",
+    "# Load data\n",
+    "data = loader.load_data()\n",
     "\n",
-    "from temporalscope.core.core_utils import print_divider, get_temporalscope_backends\n",
-    "from temporalscope.core.temporal_data_loader import TimeFrame as tf\n",
-    "from temporalscope.datasets.datasets import DatasetLoader\n",
+    "# Initialize SingleStepTargetShifter (target_col must be specified)\n",
+    "shifter = SingleStepTargetShifter(target_col=\"realgdp\", n_lags=1, verbose=True)\n",
     "\n",
-    "# First, let's see all supported backends\n",
-    "print(\"Supported TemporalScope backends:\")\n",
-    "print(get_temporalscope_backends())\n",
-    "print_divider()\n",
+    "# Transform data\n",
+    "transformed = shifter.fit_transform(data)\n",
     "\n",
-    "def init_timeframes_for_backends(target_col: str):\n",
-    "    \"\"\"Initialize TimeFrame objects for demonstration backends.\n",
+    "print(\"Original DataFrame:\")\n",
+    "print(data.head())\n",
+    "print(\"\\n\" + \"-\"*80 + \"\\n\")\n",
     "\n",
-    "    This function demonstrates TimeFrame initialization across different backends,\n",
-    "    ensuring data meets the requirements for temporal XAI workflows:\n",
-    "    - Clean, preprocessed data\n",
-    "    - Proper time column format\n",
-    "    - Numeric features\n",
-    "    \n",
-    "    :param target_col: The target column for prediction\n",
-    "    :type target_col: str\n",
-    "    :return: A dictionary containing TimeFrame objects for each backend\n",
-    "    :rtype: dict\n",
-    "    \"\"\"\n",
-    "    # Initialize DatasetLoader - the recommended way to load data in TemporalScope\n",
-    "    loader = DatasetLoader(\"macrodata\")\n",
-    "    timeframes = {}\n",
-    "    \n",
-    "    # Load and initialize TimeFrames for demonstration backends\n",
-    "    for backend in [\"pandas\", \"polars\", \"modin\"]:\n",
-    "        print(f\"Loading data with {backend} backend...\")\n",
-    "        data = loader.load_data(backend=backend)\n",
-    "        timeframes[backend] = tf(data, time_col=\"ds\", target_col=target_col)\n",
-    "        print(f\"Successfully created TimeFrame with {backend} backend\")\n",
-    "        print_divider()\n",
-    "    \n",
-    "    return timeframes\n",
+    "print(\"Transformed DataFrame:\")\n",
+    "print(transformed.head())\n",
+    "print(\"\\n\" + \"-\"*80 + \"\\n\")\n",
     "\n",
-    "if __name__ == \"__main__\":\n",
-    "    # Initialize TimeFrames\n",
-    "    timeframes = init_timeframes_for_backends(target_col=\"realgdp\")\n",
-    "    \n",
-    "    # Demonstrate with Modin backend\n",
-    "    print(\"\\nDetailed Example with Modin Backend:\")\n",
-    "    macro_modin_tf = timeframes[\"modin\"]\n",
-    "    \n",
-    "    # Verify backend\n",
-    "    print(f\"Backend type: {macro_modin_tf.backend}\")\n",
-    "    \n",
-    "    print(\"\\nPreview of the DataFrame:\")\n",
-    "    print(macro_modin_tf.df.head())\n",
-    "    print_divider()\n",
-    "    \n",
-    "    print(\"TimeFrame Configuration:\")\n",
-    "    print(f\"Mode: {macro_modin_tf.mode}\")\n",
-    "    print(f\"Sort Order: {'Ascending' if macro_modin_tf.ascending else 'Descending'}\")\n",
-    "    print_divider()"
+    "print(f\"Original shape: {data.shape}\")\n",
+    "print(f\"Transformed shape: {transformed.shape}\")\n",
+    "print(f\"Target column: {shifter.target_col}_shift_{shifter.n_lags}\")"
    ]
   },
   {
@@ -1052,25 +229,35 @@
    "source": [
     "## Implementation Details\n",
     "\n",
-    "The SingleStepTargetShifter handles different DataFrame backends through Narwhals operations:\n",
+    "The SingleStepTargetShifter uses Narwhals for backend-agnostic operations:\n",
     "\n",
-    "1. **LazyFrame (Dask/Polars)**:\n",
-    "   - Uses collect() for scalar access\n",
-    "   - Avoids direct indexing\n",
-    "   - Handles lazy evaluation properly\n",
+    "1. **Production Environment**:\n",
+    "   - Uses pandas + narwhals for efficient operations\n",
+    "   - Lightweight deployment with minimal dependencies\n",
+    "   - Consistent behavior in production\n",
     "\n",
-    "2. **PyArrow**:\n",
-    "   - Uses nw.Int64 for numeric operations\n",
-    "   - Handles comparisons through Narwhals\n",
-    "   - Converts types before arithmetic operations\n",
+    "2. **Test Environment**:\n",
+    "   - Supports multiple backends via hatch\n",
+    "   - Validates across different DataFrame implementations\n",
+    "   - Ensures reliability across environments\n",
     "\n",
-    "3. **All Backends**:\n",
+    "3. **Core Operations**:\n",
     "   - Uses @nw.narwhalify for backend conversions\n",
     "   - Pure Narwhals operations throughout\n",
     "   - Consistent behavior across supported types\n",
     "\n",
-    "This design ensures reliable target shifting operations across all supported DataFrame backends, preparing data for machine learning tasks and temporal feature importance analysis."
+    "This design ensures reliable target shifting operations while maintaining a lightweight production footprint."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
@@ -1080,10 +267,18 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
    "version": "3.11.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/tutorial_notebooks/introduction/3_xai_data_quality_checks.ipynb b/tutorial_notebooks/introduction/3_xai_data_quality_checks.ipynb
index 7f078b4..7ee6053 100644
--- a/tutorial_notebooks/introduction/3_xai_data_quality_checks.ipynb
+++ b/tutorial_notebooks/introduction/3_xai_data_quality_checks.ipynb
@@ -38,136 +38,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Import required libraries\n",
-    "import pandas as pd  # For DataFrame operations\n",
-    "import numpy as np   # For numerical operations\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import narwhals as nw\n",
     "\n",
-    "# Import TemporalScope components\n",
     "from temporalscope.datasets.dataset_validator import DatasetValidator, ValidationResult\n",
     "\n",
-    "# Note: We'll import other backends (polars, modin) when needed\n",
-    "# This keeps the initial setup simple and focused"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example 1: Basic Data Validation\n",
-    "\n",
-    "Let's start with a simple example to understand the core validation concepts.\n",
-    "\n",
-    "### What We're Doing\n",
-    "1. Create a sample time series dataset\n",
-    "2. Set up the validator with research-backed thresholds\n",
-    "3. Run validation checks\n",
-    "4. Understand the validation results\n",
-    "\n",
-    "### Why It Matters\n",
-    "- Poor data quality leads to unreliable XAI results\n",
-    "- Research-backed thresholds ensure meaningful analysis\n",
-    "- Early validation prevents downstream issues"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Step 1: Create a sample time series dataset\n",
-    "# - Using 5000 samples (meets minimum requirement of 3000)\n",
-    "# - One feature plus target (meets minimum feature requirement)\n",
-    "# - Daily timestamps for time series structure\n",
+    "# Create sample time series data\n",
     "data = pd.DataFrame({\n",
-    "    'time': pd.date_range('2023-01-01', periods=5000),  # Daily timestamps\n",
-    "    'price': np.random.normal(100, 10, 5000),           # Continuous feature\n",
-    "    'target': np.random.choice([0, 1], 5000)            # Binary target\n",
+    "    'time': pd.date_range('2023-01-01', periods=5000),\n",
+    "    'price': np.random.normal(100, 10, 5000),\n",
+    "    'target': np.random.choice([0, 1], 5000)\n",
     "})\n",
     "\n",
-    "# Step 2: Initialize validator with research-backed thresholds\n",
+    "# Initialize validator with research-backed thresholds\n",
     "validator = DatasetValidator(\n",
-    "    time_col='time',      # Column containing timestamps\n",
-    "    target_col='target',  # Column to predict\n",
-    "    \n",
-    "    # Research-backed thresholds from Grinsztajn et al. (2022)\n",
-    "    min_samples=3000,         # Minimum samples for reliable training\n",
-    "    max_feature_ratio=0.1,    # Prevent overfitting\n",
-    "    \n",
-    "    # Enable warnings to see detailed messages\n",
+    "    time_col='time',\n",
+    "    target_col='target',\n",
+    "    min_samples=3000,\n",
+    "    max_feature_ratio=0.1,\n",
     "    enable_warnings=True\n",
     ")\n",
     "\n",
-    "# Step 3: Run validation\n",
+    "# Run validation\n",
     "results = validator.fit_transform(data)\n",
     "\n",
-    "# Step 4: Print detailed report\n",
     "print(\"Data Quality Validation Report:\")\n",
-    "validator.print_report(results)\n",
-    "\n",
-    "# Understanding the results:\n",
-    "# - ✓ means the check passed\n",
-    "# - ✗ means the check failed\n",
-    "# - Details show specific metrics and thresholds"
+    "validator.print_report(results)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Understanding Validation Checks\n",
+    "## Understanding Validation Checks\n",
     "\n",
     "Each validation check has a specific purpose:\n",
     "\n",
     "1. **Sample Size** (min_samples=3000):\n",
     "   - WHY: Too few samples → unstable models\n",
     "   - WHY: Too many samples → computational issues\n",
-    "   \n",
+    "\n",
     "2. **Feature Count**:\n",
     "   - WHY: Too few features → oversimplified model\n",
     "   - WHY: Too many features → curse of dimensionality\n",
-    "   \n",
+    "\n",
     "3. **Feature Ratio** (max_feature_ratio=0.1):\n",
     "   - WHY: High ratio → risk of overfitting\n",
     "   - WHY: Based on statistical learning theory\n",
-    "   \n",
+    "\n",
     "4. **Feature Variability**:\n",
     "   - WHY: Low variability → uninformative features\n",
     "   - WHY: Affects model's learning capacity"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example 2: Handling Data Quality Issues\n",
-    "\n",
-    "Now let's see what happens with problematic data and how to interpret the warnings.\n",
-    "\n",
-    "### Common Issues\n",
-    "1. Too few samples\n",
-    "2. Missing values\n",
-    "3. Low feature variability\n",
-    "4. Improper feature-to-sample ratio"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create data with quality issues\n",
     "problematic_data = pd.DataFrame({\n",
-    "    'time': pd.date_range('2023-01-01', periods=1000),     # Too few samples\n",
-    "    'feature1': np.random.choice([1, 2], 1000),            # Low variability\n",
-    "    'feature2': np.random.normal(0, 1, 1000),              # Good variability\n",
-    "    'feature3': [None] * 100 + list(range(900)),           # Missing values\n",
-    "    'target': np.random.choice([0, 1], 1000)               # Binary target\n",
+    "    'time': pd.date_range('2023-01-01', periods=1000),\n",
+    "    'feature1': np.random.choice([1, 2], 1000),\n",
+    "    'feature2': np.random.normal(0, 1, 1000),\n",
+    "    'feature3': [None] * 100 + list(range(900)),\n",
+    "    'target': np.random.choice([0, 1], 1000)\n",
     "})\n",
     "\n",
     "try:\n",
@@ -175,8 +116,8 @@
     "    validator = DatasetValidator(\n",
     "        time_col='time',\n",
     "        target_col='target',\n",
-    "        min_samples=3000,           # Will fail (only 1000 samples)\n",
-    "        min_unique_values=10,       # Will fail for feature1\n",
+    "        min_samples=3000,\n",
+    "        min_unique_values=10,\n",
     "        enable_warnings=True\n",
     "    )\n",
     "    \n",
@@ -196,29 +137,40 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Example 3: Production Pipeline Integration\n",
+    "## Production Pipeline Integration\n",
+    "\n",
+    "The DatasetValidator uses Narwhals for backend-agnostic operations, making it suitable for production environments:\n",
     "\n",
-    "Learn how to integrate validation into production workflows.\n",
+    "1. **Production Environment**:\n",
+    "   - Uses pandas + narwhals for efficient operations\n",
+    "   - Lightweight deployment with minimal dependencies\n",
+    "   - Consistent behavior in production\n",
     "\n",
-    "### Key Concepts\n",
-    "1. Quality gates in pipelines\n",
-    "2. Monitoring and alerting\n",
-    "3. Error handling and logging"
+    "2. **Test Environment**:\n",
+    "   - Supports multiple backends via hatch\n",
+    "   - Validates across different DataFrame implementations\n",
+    "   - Ensures reliability across environments\n",
+    "\n",
+    "3. **Core Operations**:\n",
+    "   - Uses @nw.narwhalify for backend conversions\n",
+    "   - Pure Narwhals operations throughout\n",
+    "   - Consistent behavior across supported types"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "@nw.narwhalify\n",
     "def validate_production_data(df, time_col, target_col):\n",
-    "    \"\"\"Production-ready validation function.\n",
+    "    \"\"\"Production-ready validation function using Narwhals.\n",
     "    \n",
     "    Key Features:\n",
-    "    1. Proper error handling\n",
-    "    2. Detailed logging\n",
-    "    3. Monitoring integration\n",
+    "    1. Backend-agnostic operations\n",
+    "    2. Proper error handling\n",
+    "    3. Detailed logging\n",
     "    \n",
     "    Args:\n",
     "        df: Input DataFrame to validate\n",
@@ -228,33 +180,24 @@
     "    Returns:\n",
     "        Tuple of (results, summary)\n",
     "    \"\"\"\n",
-    "    # Initialize validator with production thresholds\n",
     "    validator = DatasetValidator(\n",
     "        time_col=time_col,\n",
     "        target_col=target_col,\n",
-    "        min_samples=3000,     # Research-backed minimum\n",
-    "        max_feature_ratio=0.1  # Prevent overfitting\n",
+    "        min_samples=3000,\n",
+    "        max_feature_ratio=0.1\n",
     "    )\n",
     "    \n",
     "    try:\n",
-    "        # Run validation\n",
     "        results = validator.fit_transform(df)\n",
-    "        \n",
-    "        # Get monitoring metrics\n",
     "        summary = ValidationResult.get_validation_summary(results)\n",
     "        failed = ValidationResult.get_failed_checks(results)\n",
     "        \n",
-    "        # Handle failures\n",
     "        if failed:\n",
     "            for check_name, result in failed.items():\n",
-    "                # Get structured log entry\n",
     "                log_entry = result.to_log_entry()\n",
-    "                \n",
-    "                # Log failure details\n",
     "                print(f\"Failed Check: {check_name}\")\n",
     "                print(f\"Details: {log_entry}\")\n",
     "                \n",
-    "                # Critical failures stop the pipeline\n",
     "                if result.severity == \"ERROR\":\n",
     "                    raise ValueError(f\"Critical validation failure: {check_name}\")\n",
     "        \n",
@@ -281,37 +224,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Best Practices for Production\n",
-    "\n",
-    "### 1. Pipeline Integration\n",
-    "```python\n",
-    "# Airflow DAG Example\n",
-    "with DAG('data_validation_pipeline') as dag:\n",
-    "    validate_task = PythonOperator(\n",
-    "        task_id='validate_dataframe',\n",
-    "        python_callable=validate_production_data,\n",
-    "        op_kwargs={\n",
-    "            'df': '{{ task_instance.xcom_pull(task_ids=\"load_data\") }}',\n",
-    "            'time_col': 'timestamp',\n",
-    "            'target_col': 'target'\n",
-    "        }\n",
-    "    )\n",
-    "```\n",
-    "\n",
-    "### 2. Monitoring Setup\n",
-    "- Track validation metrics over time\n",
-    "- Set up alerts for critical failures\n",
-    "- Monitor feature drift\n",
-    "\n",
-    "### 3. Threshold Configuration\n",
-    "- Start with research-backed defaults\n",
-    "- Adjust based on domain requirements\n",
-    "- Document threshold decisions\n",
-    "\n",
-    "### 4. Error Handling\n",
-    "- Define clear failure policies\n",
-    "- Set up fallback procedures\n",
-    "- Maintain audit trails\n",
+    "## Best Practices\n",
+    "\n",
+    "1. **Pipeline Integration**:\n",
+    "   ```python\n",
+    "   # Airflow DAG Example\n",
+    "   with DAG('data_validation_pipeline') as dag:\n",
+    "       validate_task = PythonOperator(\n",
+    "           task_id='validate_dataframe',\n",
+    "           python_callable=validate_production_data,\n",
+    "           op_kwargs={\n",
+    "               'df': '{{ task_instance.xcom_pull(task_ids=\"load_data\") }}',\n",
+    "               'time_col': 'timestamp',\n",
+    "               'target_col': 'target'\n",
+    "           }\n",
+    "       )\n",
+    "   ```\n",
+    "\n",
+    "2. **Monitoring Setup**:\n",
+    "   - Track validation metrics over time\n",
+    "   - Set up alerts for critical failures\n",
+    "   - Monitor feature drift\n",
+    "\n",
+    "3. **Threshold Configuration**:\n",
+    "   - Start with research-backed defaults\n",
+    "   - Adjust based on domain requirements\n",
+    "   - Document threshold decisions\n",
+    "\n",
+    "4. **Error Handling**:\n",
+    "   - Define clear failure policies\n",
+    "   - Set up fallback procedures\n",
+    "   - Maintain audit trails\n",
     "\n",
     "## Further Reading\n",
     "\n",
@@ -337,9 +280,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/tutorial_notebooks/narwhals/1_narwhals_ml_patterns.ipynb b/tutorial_notebooks/narwhals/1_narwhals_ml_patterns.ipynb
new file mode 100644
index 0000000..444cff6
--- /dev/null
+++ b/tutorial_notebooks/narwhals/1_narwhals_ml_patterns.ipynb
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Narwhals Machine Learning Patterns\n",
+    "\n",
+    "Narwhals provides a unified interface for DataFrame operations across Pandas, Polars, and Dask. This tutorial outlines patterns for building backend-agnostic functions tailored to typical machine learning workflows, including data validation, feature engineering, and time series processing. These patterns help create scalable and maintainable pipelines suitable for both prototyping and production, benefiting both OSS and enterprise-grade developers.\n",
+    "\n",
+    "## Overview of Patterns\n",
+    "\n",
+    "1. **Backward Compatibility Policy**:  \n",
+    "   Narwhals ensures stability for library maintainers with strict backward compatibility. Code written with `import narwhals.stable.v1 as nw` will remain functional indefinitely, even if breaking changes occur. Breaking changes are isolated in `narwhals.stable.v2` or higher, while `narwhals.stable.v1` remains unaffected, enabling safe coexistence of multiple versions.\n",
+    "\n",
+    "2. **Data Validation**:  \n",
+    "   Use `@nw.narwhalify` to decorate functions for consistent backend-agnostic validation. Leverage `nw.col(...)` for type casting, null handling, and basic statistics across backends.\n",
+    "3. **Feature Engineering**:  \n",
+    "   Chain transformations like `.cast(nw.Float64())` or `.fill_null(...)` to preprocess numeric and categorical data. Defer materialization until necessary to optimize memory usage.\n",
+    "4. **Time Series Handling**:  \n",
+    "   Validate temporal data by grouping with `.group_by([id_col, time_col])`, checking for uniqueness or applying rolling windows. Maintain backend independence without additional logic.\n",
+    "5. **Workflow Optimization**:  \n",
+    "   Begin with lazy mode (e.g., `nw.from_native(dask_df)`) for scalability and switch to eager mode using `.collect()` or `.to_native()` for tasks needing immediate results.\n",
+    "6. **OSS and Enterprise Notes**:  \n",
+    "   Use tools like Hatch to manage lean environments for production and comprehensive setups for testing.  \n",
+    "   - Define lean setups in `[tool.hatch.envs.default]` for minimal dependencies (e.g., Narwhals and Pandas).  \n",
+    "   - Use `[tool.hatch.envs.test]` for broader testing across multiple backends (e.g., Polars and Dask).  \n",
+    "   Handle unsupported objects gracefully by setting `pass_through=True` or `strict=False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import narwhals as nw\n",
+    "from narwhals.typing import FrameT\n",
+    "import pandas as pd\n",
+    "import polars as pl\n",
+    "import dask.dataframe as dd\n",
+    "import numpy as np\n",
+    "from typing import Dict, List, Optional, Union, Any"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 1: Backward Compatibility Policy\n",
+    "\n",
+    "This pattern demonstrates how Narwhals guarantees **backward compatibility**, ensuring stability for production-grade workflows and eliminating breaking changes across updates.\n",
+    "\n",
+    "1. **Stable Namespace**: Code written with `narwhals.stable.v1` remains functional indefinitely, even if breaking changes occur in Narwhals or its backends.\n",
+    "2. **Version Migration**: Developers can adopt new features by explicitly switching to updated namespaces, such as `narwhals.stable.v2`.\n",
+    "3. **Integration**: Multiple Narwhals versions can coexist in a single project, ensuring smooth collaboration without dependency conflicts.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result using Narwhals stable API v1:\n",
+      "   mean_feature1  sum_feature2\n",
+      "0            2.0            15\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import the stable API for guaranteed compatibility\n",
+    "import narwhals.stable.v1 as nw\n",
+    "from narwhals.typing import IntoFrameT\n",
+    "\n",
+    "# Example dataset\n",
+    "data = {\"feature1\": [1, 2, 3], \"feature2\": [4, 5, 6]}\n",
+    "\n",
+    "# Workflow using Narwhals stable.v1\n",
+    "def backward_compatible_workflow(df: IntoFrameT) -> IntoFrameT:\n",
+    "    \"\"\"Use Narwhals stable.v1 API to process data.\"\"\"\n",
+    "    # Convert to Narwhals lazy frame\n",
+    "    df_nw = nw.from_native(df)\n",
+    "    \n",
+    "    # Perform transformations\n",
+    "    df_transformed = df_nw.select([\n",
+    "        nw.col(\"feature1\").mean().alias(\"mean_feature1\"),\n",
+    "        nw.col(\"feature2\").sum().alias(\"sum_feature2\"),\n",
+    "    ])\n",
+    "    \n",
+    "    # Convert back to native format (e.g., Pandas)\n",
+    "    return df_transformed.to_native()\n",
+    "\n",
+    "# Testing the backward-compatible workflow\n",
+    "import pandas as pd\n",
+    "df_pd = pd.DataFrame(data)\n",
+    "result = backward_compatible_workflow(df_pd)\n",
+    "\n",
+    "print(\"Result using Narwhals stable API v1:\")\n",
+    "print(result)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 1: Lazy-to-Eager Frame Transitions\n",
+    "\n",
+    "This pattern demonstrates how Narwhals provides a unified interface for transitioning between lazy and eager evaluation, regardless of the backend. There's no need to distinguish between different materialization methods - Narwhals handles this automatically. The example shows:\n",
+    "\n",
+    "- **Unified Collection**: Using `collect()` provides a consistent way to materialize results, automatically handling the transition from any lazy backend (like Dask) to an eager Pandas DataFrame\n",
+    "- **Lazy Operations**: Start with any lazy backend for memory-efficient processing of large datasets, with operations optimized and deferred until needed\n",
+    "- **Backend Transitions**: Narwhals automatically manages the transition from lazy (e.g., Dask) to eager (Pandas) evaluation, simplifying ML workflows\n",
+    "- **ML Integration**: Final `to_native()` call provides the DataFrame in the format needed by ML libraries\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Dask DataFrame (lazy):\n",
+      "Dask DataFrame Structure:\n",
+      "              numeric_feature categorical_feature\n",
+      "npartitions=2                                    \n",
+      "0                     float64              string\n",
+      "3                         ...                 ...\n",
+      "4                         ...                 ...\n",
+      "Dask Name: frompandas, 1 expression\n",
+      "Expr=df\n",
+      "\n",
+      "Processed Pandas DataFrame (eager):\n",
+      "   numeric_feature categorical_feature\n",
+      "0              1.5                   A\n",
+      "1              2.0                   B\n",
+      "2              0.0                   A\n",
+      "3              4.0                   C\n",
+      "4              5.5                   B\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create sample ML dataset\n",
+    "data = {\n",
+    "    \"numeric_feature\": [1.5, 2.0, None, 4.0, 5.5],    # Has missing value\n",
+    "    \"categorical_feature\": [\"A\", \"B\", \"A\", \"C\", \"B\"]  # Needs encoding\n",
+    "}\n",
+    "df_pd = pd.DataFrame(data)\n",
+    "\n",
+    "# Start with any lazy backend (e.g., Dask)\n",
+    "df_dask = dd.from_pandas(df_pd, npartitions=2)\n",
+    "\n",
+    "# Unified lazy-to-eager pattern\n",
+    "df_nw = nw.from_native(df_dask)           # Works with any lazy frame\n",
+    "df_processed = df_nw.select([             # Lazy operations\n",
+    "    nw.col(\"numeric_feature\")\n",
+    "       .fill_null(0)\n",
+    "       .cast(nw.Float64())\n",
+    "       .alias(\"numeric_feature\"),\n",
+    "    nw.col(\"categorical_feature\")\n",
+    "       .cast(nw.String())\n",
+    "       .alias(\"categorical_feature\")\n",
+    "])\n",
+    "df_collected = df_processed.collect()      # Unified collection - no compute() needed\n",
+    "df_pandas = df_collected.to_native()       # Ready for ML libraries\n",
+    "\n",
+    "print(\"Original Dask DataFrame (lazy):\")\n",
+    "print(df_dask)\n",
+    "print(\"\\nProcessed Pandas DataFrame (eager):\")\n",
+    "print(df_pandas)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 2: Data Validation - Handling Unsupported Types\n",
+    "\n",
+    "This pattern demonstrates how to build robust data validation pipelines that can handle unexpected or unsupported data types. In data engineering workflows, you often need to validate and process data from various sources that may contain custom objects, mixed types, or other non-standard formats. The pass_through parameter provides explicit control over validation behavior:\n",
+    "\n",
+    "- **Development Mode**: Using `pass_through=True` enables initial data exploration and debugging by allowing inspection of problematic data types. This is crucial when investigating data quality issues or understanding new data sources.\n",
+    "- **Production Mode**: Using `pass_through=False` (default) enforces strict validation rules in production pipelines, preventing unexpected data types from silently propagating through the system and causing downstream issues.\n",
+    "- **Error Recovery**: Both modes provide clear error messages that help identify and handle data quality issues at the appropriate stage of your pipeline.\n",
+    "- **Pipeline Integration**: Choose development mode during data exploration and pipeline development, then switch to production mode for robust, production-grade data validation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Development Mode (pass_through=True):\n",
+      "Expected: Can load and view all data\n",
+      "   feature1 unsupported\n",
+      "0         1   Custom(1)\n",
+      "1         2   Custom(2)\n",
+      "2         3   Custom(3)\n",
+      "\n",
+      "Trying operations on normal column:\n",
+      "Expected: Should work normally\n",
+      "   feature1\n",
+      "0       1.0\n",
+      "1       2.0\n",
+      "2       3.0\n",
+      "\n",
+      "Trying operations on unsupported column:\n",
+      "Expected: Should fail gracefully\n",
+      "Error: Cannot cast custom objects to Float64\n",
+      "\n",
+      "Production Mode (pass_through=False):\n",
+      "Expected: Should fail on unsupported types\n",
+      "Error: Unsupported types not allowed in strict mode\n"
+     ]
+    }
+   ],
+   "source": [
+    "class CustomObject:\n",
+    "    def __init__(self, value):\n",
+    "        self.value = value\n",
+    "    def __str__(self):\n",
+    "        return f\"Custom({self.value})\"\n",
+    "\n",
+    "# Create DataFrame with unsupported objects\n",
+    "data = {\n",
+    "    \"feature1\": [1, 2, 3],\n",
+    "    \"unsupported\": [CustomObject(1), CustomObject(2), CustomObject(3)]\n",
+    "}\n",
+    "df_pd = pd.DataFrame(data)\n",
+    "\n",
+    "# Development mode - allows inspection\n",
+    "print(\"Development Mode (pass_through=True):\")\n",
+    "print(\"Expected: Can load and view all data\")\n",
+    "df_nw1 = nw.from_native(df_pd, pass_through=True)\n",
+    "print(df_nw1.to_native())\n",
+    "\n",
+    "print(\"\\nTrying operations on normal column:\")\n",
+    "print(\"Expected: Should work normally\")\n",
+    "result = df_nw1.select([\n",
+    "    nw.col(\"feature1\").cast(nw.Float64())\n",
+    "])\n",
+    "print(result.to_native())\n",
+    "\n",
+    "print(\"\\nTrying operations on unsupported column:\")\n",
+    "print(\"Expected: Should fail gracefully\")\n",
+    "try:\n",
+    "    result = df_nw1.select([\n",
+    "        nw.col(\"unsupported\").cast(nw.Float64())\n",
+    "    ])\n",
+    "except Exception as e:\n",
+    "    print(f\"Error: Cannot cast custom objects to Float64\")\n",
+    "\n",
+    "# Production mode - strict type checking\n",
+    "print(\"\\nProduction Mode (pass_through=False):\")\n",
+    "print(\"Expected: Should fail on unsupported types\")\n",
+    "try:\n",
+    "    df_nw2 = nw.from_native(df_pd, pass_through=False)\n",
+    "    result = df_nw2.select([\n",
+    "        nw.col(\"unsupported\").cast(nw.Float64())\n",
+    "    ])\n",
+    "except Exception as e:\n",
+    "    print(\"Error: Unsupported types not allowed in strict mode\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 3: Data Validation\n",
+    "\n",
+    "This pattern shows how to validate data types and quality across DataFrame operations in a backend-agnostic way. By using Narwhals native types and operations, you can ensure consistent validation behavior regardless of the underlying DataFrame implementation. The example shows:\n",
+    "\n",
+    "- **Type Safety**: Using Narwhals native types (Float64, String) ensures consistent type handling across backends, preventing type-related errors in ML pipelines\n",
+    "- **Validation Workflow**: Backend-agnostic operations for checking nulls, type compatibility, and data quality enable robust validation pipelines\n",
+    "- **Error Handling**: Graceful error recovery and clear error messages help identify data quality issues early in the pipeline\n",
+    "- **ML Integration**: Consistent validation behavior across training and inference ensures reliable model deployment\n",
+    "\n",
+    "Note: Dask backend requires different handling for lazy evaluation. See Pattern 1 for lazy-to-eager transition patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Testing Pandas backend:\n",
+      "==================================================\n",
+      "\n",
+      "Validating numeric column with nulls:\n",
+      "Expected: Should compute mean and null count\n",
+      "Success - Mean: 2.3333333333333335\n",
+      "Success - Null count: 1\n",
+      "\n",
+      "Trying to convert invalid strings to float:\n",
+      "Expected: Should fail with type conversion error\n",
+      "Failed as expected: could not convert string to float: 'bad'\n",
+      "\n",
+      "Testing Polars backend:\n",
+      "==================================================\n",
+      "\n",
+      "Validating numeric column with nulls:\n",
+      "Expected: Should compute mean and null count\n",
+      "Success - Mean: 2.3333333333333335\n",
+      "Success - Null count: 1\n",
+      "\n",
+      "Trying to convert invalid strings to float:\n",
+      "Expected: Should fail with type conversion error\n",
+      "Failed as expected: conversion from `str` to `f64` failed in column 'mixed' for 1 out of 4 values: [\"bad\"]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create sample data with validation issues\n",
+    "data = {\n",
+    "    'numeric': [1, 2, None, 4],           # Has null\n",
+    "    'mixed': ['1', '2', 'bad', '4'],      # Has invalid value\n",
+    "}\n",
+    "\n",
+    "# Test across backends\n",
+    "backends = {\n",
+    "    'Pandas': pd.DataFrame(data),\n",
+    "    'Polars': pl.DataFrame(data)\n",
+    "}\n",
+    "\n",
+    "for name, df in backends.items():\n",
+    "    print(f\"\\nTesting {name} backend:\")\n",
+    "    print(\"=\" * 50)\n",
+    "    df_nw = nw.from_native(df)\n",
+    "    \n",
+    "    # Validate numeric column (should succeed with nulls)\n",
+    "    print(\"\\nValidating numeric column with nulls:\")\n",
+    "    print(\"Expected: Should compute mean and null count\")\n",
+    "    try:\n",
+    "        result = df_nw.select([\n",
+    "            nw.col(\"numeric\")\n",
+    "               .cast(nw.Float64())\n",
+    "               .mean()\n",
+    "               .alias(\"mean\"),\n",
+    "            nw.col(\"numeric\")\n",
+    "               .is_null()\n",
+    "               .sum()\n",
+    "               .alias(\"nulls\")\n",
+    "        ])\n",
+    "        print(f\"Success - Mean: {result['mean'].item()}\")\n",
+    "        print(f\"Success - Null count: {result['nulls'].item()}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Failed as expected: {str(e)}\")\n",
+    "    \n",
+    "    # Try invalid column (should fail with type error)\n",
+    "    print(\"\\nTrying to convert invalid strings to float:\")\n",
+    "    print(\"Expected: Should fail with type conversion error\")\n",
+    "    try:\n",
+    "        result = df_nw.select([\n",
+    "            nw.col(\"mixed\")\n",
+    "               .cast(nw.Float64())\n",
+    "               .mean()\n",
+    "        ])\n",
+    "        print(\"Unexpected success!\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Failed as expected: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 4: Feature Engineering & Collect-Then-Item Pattern\n",
+    "\n",
+    "This pattern demonstrates how to build efficient feature engineering pipelines for ML workflows. A critical challenge in ML pipelines is handling both lazy backends (like Dask for large datasets) and eager backends (like Pandas for interactive development). The key is following a consistent materialization pattern when you need concrete values (like computing means for imputation).\n",
+    "\n",
+    "Key principles for consistent feature engineering:\n",
+    "- **Consistent Materialization**: Always check if collect() is needed before item() - use `result.item()` for eager frames and `result.collect().item()` for lazy frames. This pattern ensures your code works correctly whether using Dask for large-scale processing or Pandas for development.\n",
+    "- **Evaluation Strategy**: Use `eager_only=True` for functions that compute statistics (means, medians, etc.) since these require materialized values. Keep other transformations (type casting, null filling) lazy to let Narwhals optimize them.\n",
+    "- **Backend Independence**: By following the collect-then-item pattern, your functions work automatically with any backend - they'll use collect() when needed (Dask) and skip it when unnecessary (Pandas).\n",
+    "- **Memory Efficiency**: Only use the materialization pattern when you absolutely need concrete values (like computing statistics). Let other operations stay lazy so Narwhals can optimize them together.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Pandas backend:\n",
+      "==================================================\n",
+      "\n",
+      "Processing numeric feature:\n",
+      "Expected: Nulls filled with mean value, cast to Float64\n",
+      "   integer_feature\n",
+      "0              1.0\n",
+      "1              2.0\n",
+      "2              3.0\n",
+      "3              4.0\n",
+      "4              5.0\n",
+      "\n",
+      "Processing categorical feature:\n",
+      "Expected: Uppercase strings, nulls filled with UNKNOWN\n",
+      "  category_messy\n",
+      "0              A\n",
+      "1              B\n",
+      "2        UNKNOWN\n",
+      "3              C\n",
+      "4              B\n",
+      "\n",
+      "Polars backend:\n",
+      "==================================================\n",
+      "\n",
+      "Processing numeric feature:\n",
+      "Expected: Nulls filled with mean value, cast to Float64\n",
+      "shape: (5, 1)\n",
+      "┌─────────────────┐\n",
+      "│ integer_feature │\n",
+      "│ ---             │\n",
+      "│ f64             │\n",
+      "╞═════════════════╡\n",
+      "│ 1.0             │\n",
+      "│ 2.0             │\n",
+      "│ 3.0             │\n",
+      "│ 4.0             │\n",
+      "│ 5.0             │\n",
+      "└─────────────────┘\n",
+      "\n",
+      "Processing categorical feature:\n",
+      "Expected: Uppercase strings, nulls filled with UNKNOWN\n",
+      "shape: (5, 1)\n",
+      "┌────────────────┐\n",
+      "│ category_messy │\n",
+      "│ ---            │\n",
+      "│ str            │\n",
+      "╞════════════════╡\n",
+      "│ A              │\n",
+      "│ B              │\n",
+      "│ UNKNOWN        │\n",
+      "│ C              │\n",
+      "│ B              │\n",
+      "└────────────────┘\n",
+      "\n",
+      "Dask backend:\n",
+      "==================================================\n",
+      "\n",
+      "Processing numeric feature:\n",
+      "Expected: Nulls filled with mean value, cast to Float64\n",
+      "Dask DataFrame Structure:\n",
+      "              integer_feature\n",
+      "npartitions=2                \n",
+      "0                     float64\n",
+      "3                         ...\n",
+      "4                         ...\n",
+      "Dask Name: getitem, 9 expressions\n",
+      "Expr=Assign(frame=df)[['integer_feature']]\n",
+      "\n",
+      "Processing categorical feature:\n",
+      "Expected: Uppercase strings, nulls filled with UNKNOWN\n",
+      "Dask DataFrame Structure:\n",
+      "              category_messy\n",
+      "npartitions=2               \n",
+      "0                     string\n",
+      "3                        ...\n",
+      "4                        ...\n",
+      "Dask Name: getitem, 11 expressions\n",
+      "Expr=Assign(frame=df)[['category_messy']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create sample data with preprocessing needs\n",
+    "data = {\n",
+    "    'integer_feature': [1, 2, None, 4, 5],           # Needs mean imputation\n",
+    "    'category_messy': ['a', 'B', None, 'c', 'b']     # Needs standardization\n",
+    "}\n",
+    "\n",
+    "# Test across backends\n",
+    "backends = {\n",
+    "    'Pandas': pd.DataFrame(data),\n",
+    "    'Polars': pl.DataFrame(data),\n",
+    "    'Dask': dd.from_pandas(pd.DataFrame(data), npartitions=2)  # Lazy Dask frame\n",
+    "}\n",
+    "\n",
+    "@nw.narwhalify(eager_only=True)  # Decorator handles materialization consistently\n",
+    "def process_numeric_feature(df: FrameT, column: str) -> FrameT:\n",
+    "    \"\"\"Process a numeric feature for ML.\n",
+    "    \n",
+    "    Common ML transformations:\n",
+    "    - Convert to float\n",
+    "    - Fill nulls with mean\n",
+    "    - Standardize format\n",
+    "    \"\"\"\n",
+    "    # Get mean first - handle both lazy and eager cases\n",
+    "    result = df.select([\n",
+    "        nw.col(column)\n",
+    "           .cast(nw.Float64())\n",
+    "           .mean()\n",
+    "    ])\n",
+    "    mean_val = result.item() if not hasattr(result, 'collect') else result.collect().item()\n",
+    "    \n",
+    "    # Then use it for filling nulls\n",
+    "    return df.select([\n",
+    "        nw.col(column)\n",
+    "           .cast(nw.Float64())\n",
+    "           .fill_null(mean_val)\n",
+    "           .alias(column)\n",
+    "    ])\n",
+    "\n",
+    "@nw.narwhalify  # Default lazy evaluation for chainable operations\n",
+    "def process_categorical_feature(df: FrameT, column: str) -> FrameT:\n",
+    "    \"\"\"Process a categorical feature for ML.\n",
+    "    \n",
+    "    Common ML transformations:\n",
+    "    - Handle nulls first\n",
+    "    - Standardize case\n",
+    "    - Ensure string format\n",
+    "    \"\"\"\n",
+    "    return df.select([\n",
+    "        nw.col(column)\n",
+    "           .fill_null(\"UNKNOWN\")     # Handle nulls before casting\n",
+    "           .cast(nw.String())\n",
+    "           .str.to_uppercase()\n",
+    "           .alias(column)\n",
+    "    ])\n",
+    "\n",
+    "# Test features across backends\n",
+    "for name, df in backends.items():\n",
+    "    print(f\"\\n{name} backend:\")\n",
+    "    print(\"=\" * 50)\n",
+    "    df_nw = nw.from_native(df)  # Works with any backend (lazy or eager)\n",
+    "    \n",
+    "    print(\"\\nProcessing numeric feature:\")\n",
+    "    print(\"Expected: Nulls filled with mean value, cast to Float64\")\n",
+    "    numeric_result = process_numeric_feature(df_nw, \"integer_feature\")\n",
+    "    print(numeric_result)\n",
+    "    \n",
+    "    print(\"\\nProcessing categorical feature:\")\n",
+    "    print(\"Expected: Uppercase strings, nulls filled with UNKNOWN\")\n",
+    "    categorical_result = process_categorical_feature(df_nw, \"category_messy\")\n",
+    "    print(categorical_result)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 5: Time Series Validation & Materialization\n",
+    "\n",
+    "This pattern demonstrates how to validate temporal data quality in ML/DS workflows. Time series data processing often requires complex transformations (interpolation, resampling, windowing) that need to work efficiently across different scales - from interactive analysis in Pandas to large-scale processing in Dask. By handling lazy and eager evaluation consistently, you can write temporal validation code once and use it across your entire ML pipeline.\n",
+    "\n",
+    "Key principles for time series validation:\n",
+    "- **Consistent Materialization**: Follow the collect-then-item pattern when materializing results, ensuring consistent behavior across Pandas (eager) and Dask (lazy) backends\n",
+    "- **Entity Grouping**: Handle multiple time series efficiently by grouping operations, letting Narwhals optimize the execution plan\n",
+    "- **Data Quality**: Detect and report duplicate timestamps that could skew temporal analysis, using lazy evaluation for memory efficiency\n",
+    "- **Backend Independence**: Write validation functions that work identically whether processing historical data with Dask or real-time data with Pandas\n",
+    "\n",
+    "The output shows the same validation working seamlessly across backends (Pandas, Polars, Dask), with each backend displaying results in its native format while maintaining consistent behavior. This enables developers to focus on temporal logic rather than backend-specific implementations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Temporal Uniqueness Validation Results\n",
+      "==================================================\n",
+      "\n",
+      "Pandas Backend Results:\n",
+      "------------------------------\n",
+      "   id  timestamp  count\n",
+      "0   1 2023-01-01      2\n",
+      "\n",
+      "Polars Backend Results:\n",
+      "------------------------------\n",
+      "shape: (1, 3)\n",
+      "┌─────┬─────────────────────┬───────┐\n",
+      "│ id  ┆ timestamp           ┆ count │\n",
+      "│ --- ┆ ---                 ┆ ---   │\n",
+      "│ i64 ┆ datetime[μs]        ┆ u32   │\n",
+      "╞═════╪═════════════════════╪═══════╡\n",
+      "│ 1   ┆ 2023-01-01 00:00:00 ┆ 2     │\n",
+      "└─────┴─────────────────────┴───────┘\n",
+      "\n",
+      "Dask Backend Results:\n",
+      "------------------------------\n",
+      "Dask DataFrame Structure:\n",
+      "                  id       timestamp  count\n",
+      "npartitions=1                              \n",
+      "               int64  datetime64[ns]  int64\n",
+      "                 ...             ...    ...\n",
+      "Dask Name: loc, 10 expressions\n",
+      "Expr=Loc(frame=ResetIndex(frame=ColumnsSetter(frame=(GroupbyAggregation(frame=df, arg=defaultdict(<class 'list'>, {'timestamp': ['count']}), observed=True, dropna=False))[[('timestamp', 'count')]], columns=('count',))), iindexer=RenameSeries(frame=RenameSeries(frame=(ResetIndex(frame=ColumnsSetter(frame=(GroupbyAggregation(frame=df, arg=defaultdict(<class 'list'>, {'timestamp': ['count']}), observed=True, dropna=False))[[('timestamp', 'count')]], columns=('count',))))['count'] > 1, index='count'), index='count'))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate hourly timestamps with duplicates\n",
+    "base_timestamps = pd.date_range(\n",
+    "    start=\"2023-01-01\",\n",
+    "    periods=3,\n",
+    "    freq=\"h\"\n",
+    ")\n",
+    "timestamps = [\n",
+    "    base_timestamps[0],  # First timestamp\n",
+    "    base_timestamps[0],  # Duplicate for id=1\n",
+    "    base_timestamps[1],\n",
+    "    base_timestamps[0],  # Duplicate for id=2\n",
+    "    base_timestamps[2],\n",
+    "]\n",
+    "\n",
+    "# Create synthetic dataset with known properties\n",
+    "data = {\n",
+    "    # Entity identifier and temporal index\n",
+    "    'id': [1, 1, 1, 2, 2],\n",
+    "    'timestamp': timestamps,\n",
+    "    \n",
+    "    # Numeric features for validation\n",
+    "    'feature1': [1.0, 2.0, None, 4.0, 5.0],     # Float with missing values\n",
+    "    'feature2': [1.5, 2.5, 3.5, None, 5.5],     # Float with missing values\n",
+    "    'feature3': [10, 20, 30, 40, 50],           # Integer without missing values\n",
+    "}\n",
+    "\n",
+    "@nw.narwhalify\n",
+    "def validate_temporal_uniqueness(df: FrameT, id_col: str, time_col: str) -> FrameT:\n",
+    "    \"\"\"Validate temporal uniqueness within entity groups.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    df : FrameT\n",
+    "        Input DataFrame with time series data\n",
+    "    id_col : str\n",
+    "        Column name for entity identifier\n",
+    "    time_col : str\n",
+    "        Column name for temporal index\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    FrameT\n",
+    "        DataFrame containing any duplicate timestamps found\n",
+    "    \"\"\"\n",
+    "    # Group by entity and timestamp - stays lazy for efficiency\n",
+    "    counts = df.group_by([id_col, time_col]).agg([\n",
+    "        nw.col(time_col).count().alias(\"count\")\n",
+    "    ])\n",
+    "    \n",
+    "    # Filter for duplicates - still lazy until results needed\n",
+    "    return counts.filter(nw.col(\"count\") > 1)\n",
+    "\n",
+    "# Initialize DataFrames for each backend\n",
+    "df_pd = pd.DataFrame(data)           # Pandas backend\n",
+    "df_pl = pl.DataFrame(data)          # Polars backend\n",
+    "df_dask = dd.from_pandas(df_pd, npartitions=2)  # Dask backend\n",
+    "\n",
+    "# Test across backends\n",
+    "print(\"Temporal Uniqueness Validation Results\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "for name, df in [\n",
+    "    (\"Pandas\", df_pd),\n",
+    "    (\"Polars\", df_pl),\n",
+    "    (\"Dask\", df_dask)\n",
+    "]:\n",
+    "    print(f\"\\n{name} Backend Results:\")\n",
+    "    print(\"-\" * 30)\n",
+    "    df_nw = nw.from_native(df)\n",
+    "    result = validate_temporal_uniqueness(df_nw, \"id\", \"timestamp\")\n",
+    "    print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "test",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorial_notebooks/narwhals/1_narwhals_patterns.ipynb b/tutorial_notebooks/narwhals/2_narwhals_general_patterns.ipynb
similarity index 100%
rename from tutorial_notebooks/narwhals/1_narwhals_patterns.ipynb
rename to tutorial_notebooks/narwhals/2_narwhals_general_patterns.ipynb
diff --git a/tutorial_notebooks/narwhals/2_narwhals_lazy_vs_eager.ipynb b/tutorial_notebooks/narwhals/3_narwhals_lazy_vs_eager.ipynb
similarity index 100%
rename from tutorial_notebooks/narwhals/2_narwhals_lazy_vs_eager.ipynb
rename to tutorial_notebooks/narwhals/3_narwhals_lazy_vs_eager.ipynb
diff --git a/tutorial_notebooks/narwhals/3_narwhals_complex_functions.ipynb b/tutorial_notebooks/narwhals/4_narwhals_complex_functions.ipynb
similarity index 100%
rename from tutorial_notebooks/narwhals/3_narwhals_complex_functions.ipynb
rename to tutorial_notebooks/narwhals/4_narwhals_complex_functions.ipynb
diff --git a/tutorial_notebooks/narwhals/5_narwhals_ai_data_validation.ipynb b/tutorial_notebooks/narwhals/5_narwhals_ai_data_validation.ipynb
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@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5. Narwhals Data Validation Patterns\n",
+    "\n",
+    "This notebook demonstrates essential patterns for validating data in AI/ML pipelines using Narwhals. These patterns ensure data quality and consistency across both training and inference:\n",
+    "\n",
+    "| Task | ✅ Good Pattern (Backend-Agnostic) | ❌ Bad Pattern (Backend-Specific) |\n",
+    "|------|-----------------------------------|----------------------------------|\n",
+    "| DataFrame Creation | `df = nw.from_native(df_pd)` | `df_pd = pd.DataFrame(data)` (without conversion) |\n",
+    "| Column Access | `nw.col(\"feature\")` | `df[\"feature\"]` or `df.feature` |\n",
+    "| Type Casting | `nw.col(\"x\").cast(nw.Float64())` | `df[\"x\"].astype(\"float64\")` |\n",
+    "| Null Checking | `nw.col(\"x\").is_null().sum()` | `df[\"x\"].isnull().sum()` |\n",
+    "| Mean Imputation | `nw.col(\"x\").fill_null(mean_val)` | `df[\"x\"].fillna(df[\"x\"].mean())` |\n",
+    "| String Operations | `nw.col(\"x\").str.to_uppercase()` | `df[\"x\"].str.upper()` |\n",
+    "\n",
+    "We'll explore two fundamental ML workflow patterns:\n",
+    "\n",
+    "1. **Feature Validation (Eager)**\n",
+    "   - Use `eager_only=True` for immediate validation results\n",
+    "   - Return Python types for ML pipeline decisions\n",
+    "   - Example: Checking numeric features before training\n",
+    "\n",
+    "2. **Feature Processing (Lazy)**\n",
+    "   - Use lazy evaluation for transformation chains\n",
+    "   - Let Narwhals optimize the operations\n",
+    "   - Example: Converting features to ML-ready format\n",
+    "\n",
+    "The examples show how to handle common ML scenarios (missing values, mixed types, inconsistent categories) using proper Narwhals patterns that work across any DataFrame backend."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80ada2bc",
+   "metadata": {},
+   "source": [
+    "## Backend-Agnostic DataFrame Creation\n",
+    "\n",
+    "Narwhals provides a consistent interface across different DataFrame backends (Pandas, Polars, etc.). The key pattern for backend-agnostic code is:\n",
+    "\n",
+    "1. Create your DataFrame with any supported backend (e.g., Pandas)\n",
+    "2. Convert to Narwhals format using `nw.from_native()`\n",
+    "3. Use Narwhals operations that work across all backends\n",
+    "\n",
+    "This pattern ensures your code works regardless of the underlying DataFrame implementation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5d2ca4af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌───────────────────────────────────────┐\n",
+       "| Narwhals DataFrame                    |\n",
+       "| Use `.to_native` to see native output |\n",
+       "└───────────────────────────────────────┘"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import narwhals as nw\n",
+    "from narwhals.typing import FrameT  # Type hint for backend-agnostic DataFrames\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from typing import Dict, List, Optional, Union, Any\n",
+    "\n",
+    "# Create sample data with common ML data issues\n",
+    "data = {\n",
+    "    # Numeric features with different representations\n",
+    "    'integer_feature': [1, 2, None, 4, 5],           # Has null\n",
+    "    'float_feature': [1.5, 2.5, 3.5, None, 5.5],    # Has null\n",
+    "    'string_number': ['1.0', '2.0', 'bad', '4.0', '5.0'],  # Has invalid value\n",
+    "    \n",
+    "    # Categorical features with inconsistencies\n",
+    "    'category_clean': ['A', 'B', 'A', 'C', 'B'],\n",
+    "    'category_messy': ['a', 'B', None, 'c', 'b'],   # Mixed case + null\n",
+    "    \n",
+    "    # Target variable\n",
+    "    'target': [0, 1, 1, 0, 1]                       # Binary classification\n",
+    "}\n",
+    "\n",
+    "# Pattern: Backend-Agnostic Conversion\n",
+    "df_pd = pd.DataFrame(data)           # Create with any backend\n",
+    "df = nw.from_native(df_pd)          # Convert to Narwhals format\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ad5b90b",
+   "metadata": {},
+   "source": [
+    "## Pattern 1: Simple ML Type Validation Functions\n",
+    "\n",
+    "In ML workflows, we commonly need to validate two types of features:\n",
+    "1. Numeric features (can be converted to float, handle nulls)\n",
+    "2. Categorical features (consistent categories, handle case sensitivity)\n",
+    "\n",
+    "Let's create simple validation functions that work across any backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aea98014",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing with Pandas backend:\n",
+      "\n",
+      "Numeric validation:\n",
+      "integer_feature: {'valid': True, 'null_count': np.int64(1), 'mean': np.float64(3.0)}\n",
+      "string_number: {'valid': False, 'error': \"could not convert string to float: 'bad'\"}\n",
+      "\n",
+      "Categorical validation:\n",
+      "category_clean: {'valid': True, 'null_count': np.int64(0), 'unique_count': np.int64(3), 'n_categories': np.int64(3)}\n",
+      "category_messy: {'valid': True, 'null_count': np.int64(1), 'unique_count': np.int64(5), 'n_categories': np.int64(5)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "@nw.narwhalify(eager_only=True)\n",
+    "def validate_numeric_column(df: FrameT, column: str) -> Dict[str, Any]:\n",
+    "    \"\"\"Validate if a column can be used as numeric feature.\n",
+    "    \n",
+    "    Common ML checks:\n",
+    "    - Can convert to float\n",
+    "    - Count of nulls\n",
+    "    - Basic statistics\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        # Try float conversion\n",
+    "        stats = df.select([\n",
+    "            nw.col(column)\n",
+    "               .cast(nw.Float64())\n",
+    "               .mean()\n",
+    "               .alias(\"mean\"),\n",
+    "            nw.col(column)\n",
+    "               .is_null()\n",
+    "               .sum()\n",
+    "               .alias(\"nulls\")\n",
+    "        ])\n",
+    "        \n",
+    "        return {\n",
+    "            \"valid\": True,\n",
+    "            \"null_count\": stats[\"nulls\"].item(),\n",
+    "            \"mean\": stats[\"mean\"].item()\n",
+    "        }\n",
+    "    except Exception as e:\n",
+    "        return {\n",
+    "            \"valid\": False,\n",
+    "            \"error\": str(e)\n",
+    "        }\n",
+    "\n",
+    "@nw.narwhalify(eager_only=True)\n",
+    "def validate_categorical_column(df: FrameT, column: str) -> Dict[str, Any]:\n",
+    "    \"\"\"Validate if a column can be used as categorical feature.\n",
+    "    \n",
+    "    Common ML checks:\n",
+    "    - Unique categories\n",
+    "    - Null handling\n",
+    "    - Case consistency\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        # Get stats using Narwhals operations only\n",
+    "        stats = df.select([\n",
+    "            nw.col(column)\n",
+    "               .cast(nw.String())\n",
+    "               .n_unique()\n",
+    "               .alias(\"unique\"),\n",
+    "            nw.col(column)\n",
+    "               .is_null()\n",
+    "               .sum()\n",
+    "               .alias(\"nulls\")\n",
+    "        ])\n",
+    "        \n",
+    "        # Get categories using Narwhals operations\n",
+    "        categories = df.select([\n",
+    "            nw.col(column)\n",
+    "               .cast(nw.String())\n",
+    "               .alias(column)\n",
+    "        ]).unique()\n",
+    "        \n",
+    "        return {\n",
+    "            \"valid\": True,\n",
+    "            \"null_count\": stats[\"nulls\"].item(),\n",
+    "            \"unique_count\": stats[\"unique\"].item(),\n",
+    "            \"n_categories\": categories.select([nw.col(column).count()]).item()\n",
+    "        }\n",
+    "    except Exception as e:\n",
+    "        return {\n",
+    "            \"valid\": False,\n",
+    "            \"error\": str(e)\n",
+    "        }\n",
+    "\n",
+    "# Test with Pandas backend\n",
+    "print(\"Testing with Pandas backend:\")\n",
+    "print(\"\\nNumeric validation:\")\n",
+    "print(\"integer_feature:\", validate_numeric_column(df, \"integer_feature\"))\n",
+    "print(\"string_number:\", validate_numeric_column(df, \"string_number\"))\n",
+    "\n",
+    "print(\"\\nCategorical validation:\")\n",
+    "print(\"category_clean:\", validate_categorical_column(df, \"category_clean\"))\n",
+    "print(\"category_messy:\", validate_categorical_column(df, \"category_messy\"))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d46cbea",
+   "metadata": {},
+   "source": [
+    "### Next we'll show these same functions working with Polars:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "593a6da1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing with Polars backend:\n",
+      "\n",
+      "Numeric validation:\n",
+      "integer_feature: {'valid': True, 'null_count': 1, 'mean': 3.0}\n",
+      "string_number: {'valid': False, 'error': 'conversion from `str` to `f64` failed in column \\'string_number\\' for 1 out of 5 values: [\"bad\"]'}\n",
+      "\n",
+      "Categorical validation:\n",
+      "category_clean: {'valid': True, 'null_count': 0, 'unique_count': 3, 'n_categories': 3}\n",
+      "category_messy: {'valid': True, 'null_count': 1, 'unique_count': 5, 'n_categories': 4}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import polars as pl\n",
+    "\n",
+    "# Create Polars DataFrame\n",
+    "df_pl = pl.DataFrame(data)\n",
+    "df_pl_nw = nw.from_native(df_pl)\n",
+    "\n",
+    "print(\"Testing with Polars backend:\")\n",
+    "print(\"\\nNumeric validation:\")\n",
+    "print(\"integer_feature:\", validate_numeric_column(df_pl_nw, \"integer_feature\"))\n",
+    "print(\"string_number:\", validate_numeric_column(df_pl_nw, \"string_number\"))\n",
+    "\n",
+    "print(\"\\nCategorical validation:\")\n",
+    "print(\"category_clean:\", validate_categorical_column(df_pl_nw, \"category_clean\"))\n",
+    "print(\"category_messy:\", validate_categorical_column(df_pl_nw, \"category_messy\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d8c3d84",
+   "metadata": {},
+   "source": [
+    "## Results Analysis\n",
+    "\n",
+    "The validation functions work consistently across backends with some notable differences:\n",
+    "\n",
+    "1. **Numeric Validation**\n",
+    "   - Both backends detect invalid numeric values (\"bad\" in string_number)\n",
+    "   - Error messages differ but convey the same information\n",
+    "   - Null counting and mean calculation work identically\n",
+    "\n",
+    "2. **Categorical Validation**\n",
+    "   - Both backends count nulls consistently\n",
+    "   - Category counting differs slightly:\n",
+    "     * Pandas counts None as a category (5 categories)\n",
+    "     * Polars excludes None (4 categories)\n",
+    "   - This backend difference is important to note for ML pipelines\n",
+    "\n",
+    "## Pattern 2: Feature Processing\n",
+    "\n",
+    "Now that we can validate features, let's look at processing them for ML. This uses lazy evaluation since we're transforming data, not validating it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "782f688b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pandas backend:\n",
+      "\n",
+      "Processing numeric feature:\n",
+      "   integer_feature\n",
+      "0              1.0\n",
+      "1              2.0\n",
+      "2              3.0\n",
+      "3              4.0\n",
+      "4              5.0\n",
+      "\n",
+      "Processing categorical feature:\n",
+      "  category_messy\n",
+      "0              A\n",
+      "1              B\n",
+      "2           NONE\n",
+      "3              C\n",
+      "4              B\n",
+      "\n",
+      "Polars backend:\n",
+      "\n",
+      "Processing numeric feature:\n",
+      "shape: (5, 1)\n",
+      "┌─────────────────┐\n",
+      "│ integer_feature │\n",
+      "│ ---             │\n",
+      "│ f64             │\n",
+      "╞═════════════════╡\n",
+      "│ 1.0             │\n",
+      "│ 2.0             │\n",
+      "│ 3.0             │\n",
+      "│ 4.0             │\n",
+      "│ 5.0             │\n",
+      "└─────────────────┘\n",
+      "\n",
+      "Processing categorical feature:\n",
+      "shape: (5, 1)\n",
+      "┌────────────────┐\n",
+      "│ category_messy │\n",
+      "│ ---            │\n",
+      "│ str            │\n",
+      "╞════════════════╡\n",
+      "│ A              │\n",
+      "│ B              │\n",
+      "│ UNKNOWN        │\n",
+      "│ C              │\n",
+      "│ B              │\n",
+      "└────────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "@nw.narwhalify\n",
+    "def process_numeric_feature(df: FrameT, column: str) -> FrameT:\n",
+    "    \"\"\"Process a numeric feature for ML.\n",
+    "    \n",
+    "    Common ML transformations:\n",
+    "    - Convert to float\n",
+    "    - Fill nulls with mean\n",
+    "    - Standardize format\n",
+    "    \"\"\"\n",
+    "    # Get mean first\n",
+    "    mean_val = df.select([\n",
+    "        nw.col(column)\n",
+    "           .cast(nw.Float64())\n",
+    "           .mean()\n",
+    "    ]).item()\n",
+    "    \n",
+    "    # Then use it for filling nulls\n",
+    "    return df.select([\n",
+    "        nw.col(column)\n",
+    "           .cast(nw.Float64())\n",
+    "           .fill_null(mean_val)\n",
+    "           .alias(column)\n",
+    "    ])\n",
+    "\n",
+    "@nw.narwhalify\n",
+    "def process_categorical_feature(df: FrameT, column: str) -> FrameT:\n",
+    "    \"\"\"Process a categorical feature for ML.\n",
+    "    \n",
+    "    Common ML transformations:\n",
+    "    - Standardize case\n",
+    "    - Fill nulls with UNKNOWN\n",
+    "    - Consistent string format\n",
+    "    \"\"\"\n",
+    "    return df.select([\n",
+    "        nw.col(column)\n",
+    "           .cast(nw.String())\n",
+    "           .str.to_uppercase()\n",
+    "           .fill_null(\"UNKNOWN\")\n",
+    "           .alias(column)\n",
+    "    ])\n",
+    "\n",
+    "# Test with both backends\n",
+    "print(\"Pandas backend:\")\n",
+    "print(\"\\nProcessing numeric feature:\")\n",
+    "numeric_result = process_numeric_feature(df, \"integer_feature\")\n",
+    "print(numeric_result)\n",
+    "\n",
+    "print(\"\\nProcessing categorical feature:\")\n",
+    "categorical_result = process_categorical_feature(df, \"category_messy\")\n",
+    "print(categorical_result)\n",
+    "\n",
+    "print(\"\\nPolars backend:\")\n",
+    "print(\"\\nProcessing numeric feature:\")\n",
+    "numeric_result_pl = process_numeric_feature(df_pl_nw, \"integer_feature\")\n",
+    "print(numeric_result_pl)\n",
+    "\n",
+    "print(\"\\nProcessing categorical feature:\")\n",
+    "categorical_result_pl = process_categorical_feature(df_pl_nw, \"category_messy\")\n",
+    "print(categorical_result_pl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7474a99d",
+   "metadata": {},
+   "source": [
+    "# Summary: Core Narwhals Patterns\n",
+    "\n",
+    "| Pattern | When to Use | Why This Pattern | Example Use Cases |\n",
+    "|---------|-------------|------------------|-------------------|\n",
+    "| **Eager Validation** <br> (`eager_only=True`) | • Need immediate results <br> • Returning Python types <br> • Checking data quality | • Validation needs results now <br> • Can't defer error checking <br> • Must verify before processing | • Type compatibility checks <br> • Null value detection <br> • Category validation |\n",
+    "| **Lazy Transformation** <br> (default) | • Chaining operations <br> • Data transformations <br> • Feature engineering | • Let Narwhals optimize <br> • Better memory usage <br> • More efficient pipelines | • Type conversions <br> • Missing value imputation <br> • Feature standardization |\n",
+    "\n",
+    "### Typical Validation Workflows\n",
+    "\n",
+    "1. **Data Quality Validation (Eager)**\n",
+    "   - Use when immediate validation results needed\n",
+    "   - Return Python types for pipeline decisions\n",
+    "   - Example: Checking numeric features before training\n",
+    "\n",
+    "2. **Data Transformation (Lazy)**\n",
+    "   - Use for transformation chains\n",
+    "   - Let Narwhals optimize operations\n",
+    "   - Example: Converting features to ML-ready format\n",
+    "\n",
+    "These patterns ensure your data validation code:\n",
+    "- Works consistently across DataFrame backends\n",
+    "- Uses appropriate evaluation strategies\n",
+    "- Follows best practices for validation\n",
+    "- Maintains code clarity and purpose\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "test",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorial_notebooks/narwhals/6_narwhals_ai_time_series.ipynb b/tutorial_notebooks/narwhals/6_narwhals_ai_time_series.ipynb
new file mode 100644
index 0000000..5332c94
--- /dev/null
+++ b/tutorial_notebooks/narwhals/6_narwhals_ai_time_series.ipynb
@@ -0,0 +1,488 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 6: Time Series Operations with Narwhals\n",
+    "\n",
+    "This notebook demonstrates essential patterns for working with time series data using Narwhals, focusing on:\n",
+    "\n",
+    "1. **Group-by Time Series Operations**\n",
+    "   - Efficient grouping by ID columns\n",
+    "   - Temporal aggregations within groups\n",
+    "   - Mixed frequency handling\n",
+    "\n",
+    "2. **Time Series Validation**\n",
+    "   - Temporal uniqueness checks\n",
+    "   - Group-level validation\n",
+    "   - Data quality assurance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 1: Time Series Group-by Operations\n",
+    "\n",
+    "**NOTICE**: The following examples are provided AS-IS for academic purposes and are subject to the user's specific requirements and use cases. Always refer to the latest API documentation for production use.\n",
+    "\n",
+    "Common time series tasks require grouping by an ID column and performing temporal operations within each group. These patterns are essential for ML workflows in various domains.\n",
+    "\n",
+    "The following table shows key use cases where these patterns are critical:\n",
+    "\n",
+    "| Use Case | Description |\n",
+    "|----------|-------------|\n",
+    "| Healthcare Analytics | Patient vitals monitoring over time (patient_id), treatment response analysis, longitudinal health studies |\n",
+    "| Quantitative Finance | Multi-asset portfolio analysis (ticker_id), market regime detection, cross-sectional momentum studies |\n",
+    "| Signal Processing | Multi-sensor data fusion (sensor_id), anomaly detection across sensor networks, distributed system monitoring |\n",
+    "\n",
+    "Key Narwhals patterns for time series operations:\n",
+    "\n",
+    "| Operation | ✅ Good Pattern | ❌ Bad Pattern |\n",
+    "|-----------|----------------|----------------|\n",
+    "| Group By | `df.group_by(id_col).agg([nw.col(\"value\").mean()])` | `df.groupby(id_col).agg({\"value\": \"mean\"})` |\n",
+    "| Rolling | `df.with_columns([nw.col(\"value\").rolling_mean(2)])` | `df[\"value\"].rolling(2).mean()` |\n",
+    "| Time Sort | `df.sort([time_col])` | `df.sort_values(time_col)` |\n",
+    "| Null Check | `nw.col(\"value\").is_null().sum()` | `df[\"value\"].isnull().sum()` |\n",
+    "\n",
+    "Implementation considerations for robust time series processing:\n",
+    "\n",
+    "| Consideration | Description |\n",
+    "|---------------|-------------|\n",
+    "| Lazy Evaluation | • Chain operations for optimization<br>• Let Narwhals handle backend-specific optimizations<br>• Avoid unnecessary materializations |\n",
+    "| Temporal Ordering | • Ensure proper time-based sorting<br>• Handle mixed frequencies<br>• Maintain group boundaries |\n",
+    "| Backend Compatibility | • Use elementary aggregations<br>• Follow Narwhals patterns for operations<br>• Let Narwhals handle backend-specific details |\n",
+    "| Data Quality | • Validate temporal uniqueness<br>• Handle missing values<br>• Check frequency consistency |\n",
+    "\n",
+    "These patterns ensure robust time series processing across different ML scenarios and DataFrame backends. Note that some DataFrame implementations like Dask have specific requirements (e.g., known divisions for rolling operations) due to their distributed nature. While Narwhals handles these backend-specific details, always consult the latest API documentation if you need to customize error handling or implement special cases.\n",
+    "\n",
+    "**Best Practices Summary:**\n",
+    "1. ✅ Use elementary operations that work across all backends\n",
+    "2. ✅ Let Narwhals handle backend-specific optimizations\n",
+    "3. ✅ Pre-compute complex operations before grouping\n",
+    "4. ✅ Document backend-specific requirements in docstrings\n",
+    "5. ❌ Don't try to handle backend-specific details in your code\n",
+    "6. ❌ Don't chain operations after group-by\n",
+    "7. ❌ Don't use backend-specific operations\n",
+    "\n",
+    "The recommended approach is to use Narwhals patterns and let it manage backend-specific optimizations and limitations. This ensures your code remains maintainable and works consistently across different DataFrame implementations.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing compute_group_metrics:\n",
+      "   id start_time   end_time  avg_value  total_records  missing_records\n",
+      "0   1 2023-01-01 2023-01-02       15.0              2                0\n",
+      "1   2 2023-01-03 2023-01-04       30.0              1                1\n",
+      "2   3 2023-01-05 2023-01-05       50.0              1                0\n",
+      "shape: (3, 6)\n",
+      "┌─────┬─────────────────────┬─────────────────────┬───────────┬───────────────┬─────────────────┐\n",
+      "│ id  ┆ start_time          ┆ end_time            ┆ avg_value ┆ total_records ┆ missing_records │\n",
+      "│ --- ┆ ---                 ┆ ---                 ┆ ---       ┆ ---           ┆ ---             │\n",
+      "│ i64 ┆ datetime[ns]        ┆ datetime[ns]        ┆ f64       ┆ u32           ┆ u32             │\n",
+      "╞═════╪═════════════════════╪═════════════════════╪═══════════╪═══════════════╪═════════════════╡\n",
+      "│ 2   ┆ 2023-01-03 00:00:00 ┆ 2023-01-04 00:00:00 ┆ 30.0      ┆ 1             ┆ 1               │\n",
+      "│ 1   ┆ 2023-01-01 00:00:00 ┆ 2023-01-02 00:00:00 ┆ 15.0      ┆ 2             ┆ 0               │\n",
+      "│ 3   ┆ 2023-01-05 00:00:00 ┆ 2023-01-05 00:00:00 ┆ 50.0      ┆ 1             ┆ 0               │\n",
+      "└─────┴─────────────────────┴─────────────────────┴───────────┴───────────────┴─────────────────┘\n",
+      "Dask DataFrame Structure:\n",
+      "                  id      start_time        end_time avg_value total_records missing_records\n",
+      "npartitions=1                                                                               \n",
+      "               int64  datetime64[ns]  datetime64[ns]   float64         int64           int64\n",
+      "                 ...             ...             ...       ...           ...             ...\n",
+      "Dask Name: reset_index, 10 expressions\n",
+      "Expr=ResetIndex(frame=ColumnsSetter(frame=(GroupbyAggregation(frame=Assign(frame=df), arg=defaultdict(<class 'list'>, {'timestamp': ['min', 'max'], 'value': ['mean', 'count'], 'is_null': ['sum']}), observed=True, dropna=False))[[('timestamp', 'min'), ('timestamp', 'max'), ('value', 'mean'), ('value', 'count'), ('is_null', 'sum')]], columns=('start_time', 'end_time', 'avg_value', 'total_records', 'missing_records')))\n",
+      "\n",
+      "Testing compute_rolling_stats:\n",
+      "   id  rolling_mean_2\n",
+      "0   1            15.0\n",
+      "1   2            25.0\n",
+      "2   3             NaN\n",
+      "shape: (3, 2)\n",
+      "┌─────┬────────────────┐\n",
+      "│ id  ┆ rolling_mean_2 │\n",
+      "│ --- ┆ ---            │\n",
+      "│ i64 ┆ f64            │\n",
+      "╞═════╪════════════════╡\n",
+      "│ 1   ┆ 15.0           │\n",
+      "│ 2   ┆ 25.0           │\n",
+      "│ 3   ┆ null           │\n",
+      "└─────┴────────────────┘\n",
+      "\n",
+      "Dask rolling operations require known divisions:\n",
+      "Can only rolling dataframes with known divisions\n",
+      "See https://docs.dask.org/en/latest/dataframe-design.html#partitions\n",
+      "for more information.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required libraries\n",
+    "import narwhals as nw\n",
+    "from narwhals.typing import FrameT\n",
+    "import pandas as pd\n",
+    "import polars as pl\n",
+    "import dask.dataframe as dd\n",
+    "from typing import Dict, List, Optional, Union, Any, Literal\n",
+    "\n",
+    "@nw.narwhalify\n",
+    "def compute_group_metrics(df: FrameT, id_col: str, time_col: str, value_col: str) -> FrameT:\n",
+    "    \"\"\"Compute time series metrics by group.\n",
+    "    \n",
+    "    This function demonstrates proper Narwhals patterns for universal backend support:\n",
+    "    1. Uses only elementary aggregations (min, max, mean, count, sum)\n",
+    "    2. Works consistently across Pandas, Polars, and Dask\n",
+    "    3. Properly handles missing values and time ranges\n",
+    "    \n",
+    "    Returns a DataFrame with metrics per group:\n",
+    "    - start_time: First timestamp in group\n",
+    "    - end_time: Last timestamp in group\n",
+    "    - avg_value: Mean value in group\n",
+    "    - total_records: Count of records\n",
+    "    - missing_records: Count of nulls\n",
+    "    \"\"\"\n",
+    "    # Pre-compute null indicators\n",
+    "    df_prep = df.with_columns([\n",
+    "        nw.col(value_col).is_null().alias(\"is_null\")\n",
+    "    ])\n",
+    "    \n",
+    "    # Use elementary aggregations\n",
+    "    return df_prep.group_by(id_col).agg([\n",
+    "        # Time range metrics (elementary)\n",
+    "        nw.col(time_col).min().alias(\"start_time\"),\n",
+    "        nw.col(time_col).max().alias(\"end_time\"),\n",
+    "        \n",
+    "        # Value statistics (elementary)\n",
+    "        nw.col(value_col).mean().alias(\"avg_value\"),\n",
+    "        \n",
+    "        # Count metrics (elementary)\n",
+    "        nw.col(value_col).count().alias(\"total_records\"),\n",
+    "        nw.col(\"is_null\").sum().alias(\"missing_records\")\n",
+    "    ])\n",
+    "\n",
+    "@nw.narwhalify\n",
+    "def compute_rolling_stats(df: FrameT, id_col: str, time_col: str, value_col: str, window: int) -> FrameT:\n",
+    "    \"\"\"Compute rolling statistics within groups.\n",
+    "    \n",
+    "    This function demonstrates proper handling of complex operations:\n",
+    "    1. Pre-computes rolling means before grouping\n",
+    "    2. Uses elementary aggregations for group operations\n",
+    "    3. Lets Narwhals handle backend-specific details\n",
+    "    \n",
+    "    Note: Rolling operations have backend-specific requirements:\n",
+    "    - Works with Pandas and Polars out of the box\n",
+    "    - Dask requires known divisions (see Dask documentation)\n",
+    "    - Let Narwhals handle these requirements through its error handling\n",
+    "    \n",
+    "    Returns a DataFrame with rolling metrics per group:\n",
+    "    - rolling_mean_{window}: Mean of rolling window values\n",
+    "    \"\"\"\n",
+    "    # First sort and compute rolling means\n",
+    "    df_prep = (df.sort([id_col, time_col])\n",
+    "               .with_columns([\n",
+    "                   nw.col(value_col)\n",
+    "                     .rolling_mean(window)\n",
+    "                     .alias(\"rolling_value\")\n",
+    "               ]))\n",
+    "    \n",
+    "    # Then do elementary group-by aggregation\n",
+    "    return df_prep.group_by(id_col).agg([\n",
+    "        nw.col(\"rolling_value\")\n",
+    "          .mean()\n",
+    "          .alias(f\"rolling_mean_{window}\")\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "# Test data\n",
+    "test_data = {\n",
+    "    \"id\": [1, 1, 2, 2, 3],\n",
+    "    \"timestamp\": pd.date_range(\"2023-01-01\", periods=5, freq=\"D\"),\n",
+    "    \"value\": [10.0, 20.0, 30.0, None, 50.0]\n",
+    "}\n",
+    "\n",
+    "# Create DataFrames\n",
+    "df_pd = pd.DataFrame(test_data)\n",
+    "df_pl = pl.DataFrame(test_data)\n",
+    "df_dd = dd.from_pandas(df_pd, npartitions=2)\n",
+    "\n",
+    "# Test functions\n",
+    "print(\"Testing compute_group_metrics:\")\n",
+    "print(compute_group_metrics(nw.from_native(df_pd), \"id\", \"timestamp\", \"value\"))\n",
+    "print(compute_group_metrics(nw.from_native(df_pl), \"id\", \"timestamp\", \"value\"))\n",
+    "print(compute_group_metrics(nw.from_native(df_dd), \"id\", \"timestamp\", \"value\"))\n",
+    "\n",
+    "print(\"\\nTesting compute_rolling_stats:\")\n",
+    "window = 2\n",
+    "print(compute_rolling_stats(nw.from_native(df_pd), \"id\", \"timestamp\", \"value\", window))\n",
+    "print(compute_rolling_stats(nw.from_native(df_pl), \"id\", \"timestamp\", \"value\", window))\n",
+    "try:\n",
+    "    print(compute_rolling_stats(nw.from_native(df_dd), \"id\", \"timestamp\", \"value\", window))\n",
+    "except ValueError as e:\n",
+    "    print(f\"\\nDask rolling operations require known divisions:\\n{str(e)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 2: Time Series Validation\n",
+    "\n",
+    "Time series data often requires validation to ensure quality and consistency. Here are key validation patterns us\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing temporal uniqueness validation:\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Pandas Result:\n",
+      "   id  timestamp  count\n",
+      "0   1 2023-01-01      2\n",
+      "\n",
+      "Polars Result:\n",
+      "shape: (1, 3)\n",
+      "┌─────┬─────────────────────┬───────┐\n",
+      "│ id  ┆ timestamp           ┆ count │\n",
+      "│ --- ┆ ---                 ┆ ---   │\n",
+      "│ i64 ┆ datetime[μs]        ┆ u32   │\n",
+      "╞═════╪═════════════════════╪═══════╡\n",
+      "│ 1   ┆ 2023-01-01 00:00:00 ┆ 2     │\n",
+      "└─────┴─────────────────────┴───────┘\n",
+      "\n",
+      "Dask Result:\n",
+      "   id  timestamp  count\n",
+      "0   1 2023-01-01      2\n",
+      "\n",
+      "Testing uniform frequency validation:\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Pandas Result:\n",
+      "   id start_time   end_time  points\n",
+      "0   1 2023-01-01 2023-01-02       3\n",
+      "1   2 2023-01-01 2023-01-04       3\n",
+      "\n",
+      "Polars Result:\n",
+      "shape: (2, 4)\n",
+      "┌─────┬─────────────────────┬─────────────────────┬────────┐\n",
+      "│ id  ┆ start_time          ┆ end_time            ┆ points │\n",
+      "│ --- ┆ ---                 ┆ ---                 ┆ ---    │\n",
+      "│ i64 ┆ datetime[μs]        ┆ datetime[μs]        ┆ u32    │\n",
+      "╞═════╪═════════════════════╪═════════════════════╪════════╡\n",
+      "│ 1   ┆ 2023-01-01 00:00:00 ┆ 2023-01-02 00:00:00 ┆ 3      │\n",
+      "│ 2   ┆ 2023-01-01 00:00:00 ┆ 2023-01-04 00:00:00 ┆ 3      │\n",
+      "└─────┴─────────────────────┴─────────────────────┴────────┘\n",
+      "\n",
+      "Dask Result:\n",
+      "   id start_time   end_time  points\n",
+      "0   1 2023-01-01 2023-01-02       3\n",
+      "1   2 2023-01-01 2023-01-04       3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required libraries\n",
+    "import narwhals as nw\n",
+    "from narwhals.typing import FrameT\n",
+    "import pandas as pd\n",
+    "import polars as pl\n",
+    "import dask.dataframe as dd\n",
+    "from typing import Dict, List, Optional, Union, Any, Literal\n",
+    "\n",
+    "@nw.narwhalify\n",
+    "def validate_temporal_uniqueness(df: FrameT, id_col: str, time_col: str) -> FrameT:\n",
+    "    \"\"\"Validate and report temporal uniqueness.\n",
+    "    \n",
+    "    This demonstrates lazy evaluation for validation:\n",
+    "    - Group and count operations can be optimized\n",
+    "    - Return DataFrame for further processing\n",
+    "    - Let caller decide when to materialize\n",
+    "    \"\"\"\n",
+    "    # Stage 1: Group by ID and time\n",
+    "    counts = df.group_by([id_col, time_col]).agg([\n",
+    "        nw.col(time_col).count().alias(\"count\")\n",
+    "    ])\n",
+    "    \n",
+    "    # Stage 2: Filter for duplicates\n",
+    "    return counts.filter(nw.col(\"count\") > 1)\n",
+    "\n",
+    "@nw.narwhalify\n",
+    "def validate_uniform_frequency(df: FrameT, id_col: str, time_col: str) -> FrameT:\n",
+    "    \"\"\"Validate time frequency consistency.\n",
+    "    \n",
+    "    This demonstrates lazy evaluation for validation:\n",
+    "    - Group and compute time spans\n",
+    "    - Return DataFrame for further processing\n",
+    "    - Let caller decide when to materialize\n",
+    "    \"\"\"\n",
+    "    # Stage 1: Group by ID and compute time spans\n",
+    "    return df.group_by(id_col).agg([\n",
+    "        nw.col(time_col).min().alias(\"start_time\"),\n",
+    "        nw.col(time_col).max().alias(\"end_time\"),\n",
+    "        nw.col(time_col).count().alias(\"points\")\n",
+    "    ])\n",
+    "\n",
+    "# Test data\n",
+    "test_data = {\n",
+    "    \"id\": [1, 1, 1, 2, 2, 2],\n",
+    "    \"timestamp\": [\n",
+    "        pd.Timestamp(\"2023-01-01\"),\n",
+    "        pd.Timestamp(\"2023-01-01\"),  # Duplicate\n",
+    "        pd.Timestamp(\"2023-01-02\"),\n",
+    "        pd.Timestamp(\"2023-01-01\"),\n",
+    "        pd.Timestamp(\"2023-01-02\"),\n",
+    "        pd.Timestamp(\"2023-01-04\"),  # Non-uniform gap\n",
+    "    ],\n",
+    "    \"value\": [10.0, 20.0, 30.0, 40.0, 50.0, 60.0]\n",
+    "}\n",
+    "\n",
+    "# Test with different backends\n",
+    "print(\"Testing temporal uniqueness validation:\")\n",
+    "print(\"-\" * 50)\n",
+    "\n",
+    "for backend, df in [\n",
+    "    (\"Pandas\", pd.DataFrame(test_data)),\n",
+    "    (\"Polars\", pl.DataFrame(test_data)),\n",
+    "    (\"Dask\", dd.from_pandas(pd.DataFrame(test_data), npartitions=2))\n",
+    "]:\n",
+    "    print(f\"\\n{backend} Result:\")\n",
+    "    try:\n",
+    "        result = validate_temporal_uniqueness(\n",
+    "            nw.from_native(df), \"id\", \"timestamp\"\n",
+    "        )\n",
+    "        if hasattr(result, \"compute\"):\n",
+    "            result = result.compute()\n",
+    "        print(result)\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error: {str(e)}\")\n",
+    "\n",
+    "print(\"\\nTesting uniform frequency validation:\")\n",
+    "print(\"-\" * 50)\n",
+    "\n",
+    "for backend, df in [\n",
+    "    (\"Pandas\", pd.DataFrame(test_data)),\n",
+    "    (\"Polars\", pl.DataFrame(test_data)),\n",
+    "    (\"Dask\", dd.from_pandas(pd.DataFrame(test_data), npartitions=2))\n",
+    "]:\n",
+    "    print(f\"\\n{backend} Result:\")\n",
+    "    try:\n",
+    "        result = validate_uniform_frequency(\n",
+    "            nw.from_native(df), \"id\", \"timestamp\"\n",
+    "        )\n",
+    "        if hasattr(result, \"compute\"):\n",
+    "            result = result.compute()\n",
+    "        print(result)\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error: {str(e)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways - Time Series Validation Patterns\n",
+    "\n",
+    "This example demonstrates robust patterns for validating time series data across different DataFrame backends.\n",
+    "\n",
+    "### Key Patterns\n",
+    "\n",
+    "1. **Keep Operations Lazy**\n",
+    "   - ✅ Return DataFrames for further processing\n",
+    "   - ✅ Use elementary operations (group_by, agg)\n",
+    "   - ✅ Let caller decide when to materialize\n",
+    "   - ❌ Don't extract scalars in validation functions\n",
+    "   - ❌ Don't use over() for window operations\n",
+    "\n",
+    "2. **Backend Compatibility**\n",
+    "   - ✅ Use group_by and agg for aggregations\n",
+    "   - ✅ Handle compute() at caller level\n",
+    "   - ✅ Use only Narwhals operations\n",
+    "   - ❌ Don't use backend-specific operations\n",
+    "   - ❌ Don't assume eager evaluation\n",
+    "\n",
+    "3. **Validation Results**\n",
+    "   - ✅ Return DataFrames with validation details\n",
+    "   - ✅ Include all relevant information\n",
+    "   - ✅ Allow further processing if needed\n",
+    "   - ❌ Don't force immediate materialization\n",
+    "   - ❌ Don't return only boolean results\n",
+    "\n",
+    "### Example Results\n",
+    "\n",
+    "1. **Temporal Uniqueness**\n",
+    "```\n",
+    "   id  timestamp  count\n",
+    "0   1 2023-01-01      2  # Shows duplicate timestamps\n",
+    "```\n",
+    "\n",
+    "2. **Frequency Validation**\n",
+    "```\n",
+    "   id start_time   end_time  points\n",
+    "0   1 2023-01-01 2023-01-02      3  # Regular frequency\n",
+    "1   2 2023-01-01 2023-01-04      3  # Irregular frequency\n",
+    "```\n",
+    "\n",
+    "### Why This Pattern Works\n",
+    "\n",
+    "1. **Universal Backend Support**\n",
+    "   - Works with Pandas, Polars, and Dask\n",
+    "   - No backend-specific code needed\n",
+    "   - Consistent results across backends\n",
+    "\n",
+    "2. **Efficient Processing**\n",
+    "   - Lazy evaluation enables optimization\n",
+    "   - No unnecessary materializations\n",
+    "   - Chainable with other operations\n",
+    "\n",
+    "3. **Rich Validation Results**\n",
+    "   - Full details for analysis\n",
+    "   - Supports further processing\n",
+    "   - Clear validation outcomes\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 3: Mixed Frequency Handling\n",
+    "\n",
+    "Time series data often has mixed frequencies that need special handling:"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "test",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}


Test patch:
diff --git a/test/unit/core/test_core_utils.py b/test/unit/core/test_core_utils.py
index 5c517df..efa88a6 100644
--- a/test/unit/core/test_core_utils.py
+++ b/test/unit/core/test_core_utils.py
@@ -15,176 +15,49 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.
-# See the NOTICE file for additional information regarding copyright ownership.
-# The ASF licenses this file to you under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software distributed under the License is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and limitations under the License.
-
 """TemporalScope/test/unit/core/test_core_utils.py
 
-This module contains unit tests for core utility functions.
-"""
+This module contains unit tests for core utility functions. Tests use synthetic data
+generation at runtime to ensure backend-agnostic operations work correctly across
+all supported DataFrame backends (pandas, polars, etc.).
 
-from typing import Any, Callable, Dict
-from unittest import mock
+The testing pattern follows these principles:
+1. Use synthetic_data_generator to create test data for each backend
+2. Test functions with actual DataFrame implementations at runtime
+3. Verify backend-agnostic behavior using Narwhals operations
+"""
 
 import narwhals as nw
 import pandas as pd
 import pytest
 
+# Constants
+# Use TEST_BACKENDS for test environment
 from temporalscope.core.core_utils import (
-    TEMPORALSCOPE_BACKEND_CONVERTERS,
-    TEMPORALSCOPE_CORE_BACKEND_TYPES,
-    TEMPORALSCOPE_OPTIONAL_BACKENDS,
+    TEST_BACKENDS,
     TimeColumnError,
-    UnsupportedBackendError,
-    check_dataframe_empty,
-    check_dataframe_nulls_nans,
-    convert_datetime_column_to_numeric,
-    convert_time_column_to_datetime,
-    convert_to_backend,
-    convert_to_numeric,
+    convert_column_to_datetime_type,
+    convert_datetime_column_to_microseconds,
+    convert_datetime_column_to_timestamp,
+    count_dataframe_column_nulls,
     get_api_keys,
-    get_dataframe_backend,
     get_default_backend_cfg,
     get_narwhals_backends,
-    get_temporalscope_backends,
-    is_lazy_evaluation,
-    is_valid_temporal_backend,
-    is_valid_temporal_dataframe,
+    is_dataframe_empty,
     print_divider,
     sort_dataframe_time,
     validate_and_convert_time_column,
-    validate_dataframe_column_types,
-    validate_temporal_uniqueness,
-    validate_time_column_type,
+    validate_column_numeric_or_datetime,
+    validate_feature_columns_numeric,
+    validate_temporal_ordering,
 )
 from temporalscope.datasets.synthetic_data_generator import generate_synthetic_time_series
 
-# Constants
-VALID_BACKENDS = ["pandas", "modin", "pyarrow", "polars", "dask"]
-INVALID_BACKEND = "unsupported_backend"
-
-# ========================= Fixtures =========================
 
-
-@pytest.fixture(params=VALID_BACKENDS)
+@pytest.fixture(params=TEST_BACKENDS)
 def synthetic_df(request):
-    """Fixture providing synthetic DataFrames for each backend.
-
-    :param request: pytest request object containing the backend parameter
-    :return: DataFrame in the specified backend format
-    """
-    return generate_synthetic_time_series(backend=request.param, num_samples=10, num_features=2)
-
-
-@pytest.fixture
-def narwhalified_df():
-    """Fixture providing a narwhalified DataFrame."""
-    df = generate_synthetic_time_series(backend="pandas", num_samples=10, num_features=2)
-    return nw.from_native(df)
-
-
-# ========================= Tests for get_narwhals_backends =========================
-
-
-def test_get_narwhals_backends():
-    """Test the retrieval of Narwhals-supported backends."""
-    backends = get_narwhals_backends()
-    assert "pandas" in backends, "Expected 'pandas' backend in Narwhals backends list."
-    assert "modin" in backends, "Expected 'modin' backend in Narwhals backends list."
-
-
-# ========================= Tests for get_default_backend_cfg =========================
-
-
-def test_get_default_backend_cfg():
-    """Test retrieval of the default backend configuration for Narwhals."""
-    cfg = get_default_backend_cfg()
-    assert isinstance(cfg, dict), "Expected default backend configuration to be a dictionary."
-    assert "BACKENDS" in cfg, "Expected 'BACKENDS' key in default configuration."
-    assert all(
-        backend in cfg["BACKENDS"] for backend in get_narwhals_backends()
-    ), "Mismatch in default backend configuration."
-
-
-# ========================= Tests for get_temporalscope_backends =========================
-
-
-def test_get_temporalscope_backends():
-    """Test that only TemporalScope-compatible backends are returned."""
-    backends = get_temporalscope_backends()
-    assert all(
-        backend in VALID_BACKENDS for backend in backends
-    ), "Non-compatible backend found in TemporalScope backends."
-
-
-# ========================= Tests for is_valid_temporal_backend =========================
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_is_valid_temporal_backend_supported(backend):
-    """Test that is_valid_temporal_backend passes for supported backends."""
-    try:
-        is_valid_temporal_backend(backend)
-    except UnsupportedBackendError:
-        pytest.fail(f"is_valid_temporal_backend raised UnsupportedBackendError for valid backend '{backend}'.")
-
-
-def test_is_valid_temporal_backend_unsupported():
-    """Test that is_valid_temporal_backend raises error for unsupported backend."""
-    with pytest.raises(UnsupportedBackendError):
-        is_valid_temporal_backend(INVALID_BACKEND)
-
-
-def test_is_valid_temporal_backend_optional_warning():
-    """Test that is_valid_temporal_backend issues a warning for optional backends if available."""
-    # Check if "cudf" is optional in TemporalScope
-    if "cudf" in TEMPORALSCOPE_OPTIONAL_BACKENDS:
-        # Expect a warning if "cudf" is not installed
-        with pytest.warns(UserWarning, match="optional and requires additional setup"):
-            is_valid_temporal_backend("cudf")
-    else:
-        pytest.skip("Skipping test as 'cudf' is not an optional backend in this configuration.")
-
-
-# ========================= Tests for is_valid_temporal_dataframe =========================
-
-
-def test_is_valid_temporal_dataframe_supported(synthetic_df):
-    """Test that is_valid_temporal_dataframe returns True for supported DataFrame types."""
-    # Check if the DataFrame is valid
-    is_valid, df_type = is_valid_temporal_dataframe(synthetic_df)
-    assert is_valid, "Expected DataFrame to be valid."
-    assert df_type == "native", "Expected DataFrame type to be 'native'."
-
-
-def test_is_valid_temporal_dataframe_narwhalified(narwhalified_df):
-    """Test that is_valid_temporal_dataframe handles narwhalified DataFrames."""
-    # Check if the narwhalified DataFrame is valid
-    is_valid, df_type = is_valid_temporal_dataframe(narwhalified_df)
-    assert is_valid, "Expected narwhalified DataFrame to be valid."
-    assert df_type == "narwhals", "Expected DataFrame type to be 'narwhals' for narwhalified DataFrame."
-
-
-def test_is_valid_temporal_dataframe_unsupported():
-    """Test that is_valid_temporal_dataframe returns False for unsupported DataFrame types."""
-
-    class UnsupportedDataFrame:
-        pass
-
-    df = UnsupportedDataFrame()
-
-    is_valid, df_type = is_valid_temporal_dataframe(df)
-    assert not is_valid, "Expected DataFrame to be invalid for unsupported type."
-    assert df_type is None, "Expected DataFrame type to be None for unsupported type."
+    """Generate synthetic DataFrame for each backend."""
+    return generate_synthetic_time_series(backend=request.param, num_samples=10, num_features=2, drop_time=False)
 
 
 # ========================= Tests for get_api_keys =========================
@@ -195,8 +68,8 @@ def test_get_api_keys_present(monkeypatch):
     monkeypatch.setenv("OPENAI_API_KEY", "test_key_openai")
     monkeypatch.setenv("CLAUDE_API_KEY", "test_key_claude")
     api_keys = get_api_keys()
-    assert api_keys["OPENAI_API_KEY"] == "test_key_openai", "Expected OPENAI_API_KEY to match environment variable."
-    assert api_keys["CLAUDE_API_KEY"] == "test_key_claude", "Expected CLAUDE_API_KEY to match environment variable."
+    assert api_keys["OPENAI_API_KEY"] == "test_key_openai"
+    assert api_keys["CLAUDE_API_KEY"] == "test_key_claude"
 
 
 def test_get_api_keys_absent(monkeypatch, capsys):
@@ -205,8 +78,8 @@ def test_get_api_keys_absent(monkeypatch, capsys):
     monkeypatch.delenv("CLAUDE_API_KEY", raising=False)
     get_api_keys()
     captured = capsys.readouterr()
-    assert "OPENAI_API_KEY is not set" in captured.out, "Expected warning for missing OPENAI_API_KEY."
-    assert "CLAUDE_API_KEY is not set" in captured.out, "Expected warning for missing CLAUDE_API_KEY."
+    assert "Warning: OPENAI_API_KEY is not set" in captured.out
+    assert "Warning: CLAUDE_API_KEY is not set" in captured.out
 
 
 # ========================= Tests for print_divider =========================
@@ -216,1029 +89,678 @@ def test_print_divider_default(capsys):
     """Test default divider output."""
     print_divider()
     captured = capsys.readouterr()
-    assert captured.out == "=" * 70 + "\n", "Expected default divider output."
+    assert captured.out == "=" * 70 + "\n"
 
 
 def test_print_divider_custom(capsys):
     """Test custom character and length in divider output."""
     print_divider(char="*", length=30)
     captured = capsys.readouterr()
-    assert captured.out == "*" * 30 + "\n", "Expected custom divider output."
-
-
-# ========================= Tests for is_lazy_evaluation =========================
-
-
-def test_is_lazy_evaluation_invalid_dataframe():
-    """Test is_lazy_evaluation raises error for unsupported DataFrame."""
-
-    class InvalidDataFrame:
-        pass
-
-    df = InvalidDataFrame()
-    with pytest.raises(UnsupportedBackendError, match="The input DataFrame is not supported by TemporalScope."):
-        is_lazy_evaluation(df)
-
-
-def test_is_lazy_evaluation_dask(synthetic_df, request):
-    """Test lazy evaluation detection for dask backend."""
-    if request.node.callspec.params["synthetic_df"] == "dask":
-        assert is_lazy_evaluation(synthetic_df), "Expected dask DataFrame to use lazy evaluation"
-
-
-def test_is_lazy_evaluation_polars_lazy(synthetic_df, request):
-    """Test lazy evaluation detection for polars lazy backend."""
-    if request.node.callspec.params["synthetic_df"] == "polars-lazy":
-        assert is_lazy_evaluation(synthetic_df), "Expected polars lazy DataFrame to use lazy evaluation"
-
-
-def test_is_lazy_evaluation_eager(synthetic_df, request):
-    """Test lazy evaluation detection for eager backends."""
-    backend = request.node.callspec.params["synthetic_df"]
-    if backend not in ["dask", "polars-lazy"]:
-        assert not is_lazy_evaluation(synthetic_df), f"Expected {backend} DataFrame to use eager evaluation"
-
-
-def test_is_lazy_evaluation_narwhalified(narwhalified_df):
-    """Test lazy evaluation detection for narwhalified DataFrame."""
-    assert not is_lazy_evaluation(narwhalified_df), "Expected narwhalified DataFrame to use eager evaluation"
+    assert captured.out == "*" * 30 + "\n"
 
 
-@pytest.fixture(params=get_temporalscope_backends())
-def backend(request) -> str:
-    """Fixture providing all supported backends for testing."""
-    return request.param
-
-
-@pytest.fixture
-def data_config(backend: str) -> Callable[..., Dict[str, Any]]:
-    """Base fixture for data generation configuration."""
+# ========================= Tests for get_narwhals_backends =========================
 
-    def _config(**kwargs) -> Dict[str, Any]:
-        default_config = {
-            "num_samples": 3,
-            "num_features": 2,
-            "with_nulls": False,
-            "with_nans": False,
-            "backend": backend,
-            "drop_time": True,
-            "random_seed": 42,
-        }
-        default_config.update(kwargs)
-        return default_config
 
-    return _config
+def test_get_narwhals_backends():
+    """Test the retrieval of Narwhals-supported backends."""
+    backends = get_narwhals_backends()
+    assert isinstance(backends, list)
+    assert all(isinstance(backend, str) for backend in backends)
+    assert "pandas" in backends
 
 
-# ========================= check_dataframe_empty Tests =========================
+# ========================= Tests for get_default_backend_cfg =========================
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_empty_with_empty_df(backend: str) -> None:
-    """Test check_dataframe_empty returns True for empty DataFrame."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=0, num_features=1, drop_time=True)
-    df = nw.from_native(df)
-    assert check_dataframe_empty(df) is True
+def test_get_default_backend_cfg():
+    """Test retrieval of the default backend configuration."""
+    cfg = get_default_backend_cfg()
+    assert isinstance(cfg, dict)
+    assert "BACKENDS" in cfg
+    assert isinstance(cfg["BACKENDS"], list)
+    assert all(backend in cfg["BACKENDS"] for backend in get_narwhals_backends())
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_empty_with_data(backend: str) -> None:
-    """Test check_dataframe_empty returns False for non-empty DataFrame."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=3, num_features=1, drop_time=True)
-    df = nw.from_native(df)
-    assert check_dataframe_empty(df) is False
+# ========================= Tests for check_dataframe_empty =========================
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_empty_with_lazy_evaluation(backend: str) -> None:
-    """Test check_dataframe_empty works with lazy evaluation."""
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_is_dataframe_empty_with_empty_df(backend):
+    """Test is_dataframe_empty returns True for empty DataFrame."""
     df = generate_synthetic_time_series(backend=backend, num_samples=0, num_features=1, drop_time=True)
-    df = nw.from_native(df)
-    if hasattr(df.to_native(), "lazy"):
-        df = df.to_native().lazy()
-        df = nw.from_native(df)
-    assert check_dataframe_empty(df) is True
+    assert is_dataframe_empty(df) is True
 
 
-@pytest.mark.parametrize("backend", ["dask"])
-def test_check_dataframe_empty_lazy_compute(backend: str) -> None:
-    """Test check_dataframe_empty handles lazy evaluation with compute."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=0, num_features=1, drop_time=True)
-    df = nw.from_native(df)
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_is_dataframe_empty_with_data(backend):
+    """Test is_dataframe_empty returns False for non-empty DataFrame."""
+    df = generate_synthetic_time_series(backend=backend, num_samples=10, num_features=1, drop_time=True)
+    assert is_dataframe_empty(df) is False
 
-    if hasattr(df, "compute"):
-        df.compute = lambda: nw.from_native({"col": []})
-        assert check_dataframe_empty(df) is True
 
-
-# Tests for error handling
-def test_check_dataframe_empty_error_handling() -> None:
-    """Test check_dataframe_empty error handling for None input."""
+def test_is_dataframe_empty_none():
+    """Test is_dataframe_empty error handling for None input."""
     with pytest.raises(ValueError, match="DataFrame cannot be None"):
-        check_dataframe_empty(None)
-
-
-def test_check_dataframe_empty_unsupported_type() -> None:
-    """Test check_dataframe_empty raises ValueError for unsupported DataFrame type."""
-
-    class UnsupportedDataFrame:
-        """Custom DataFrame type not supported by Narwhals API."""
-
-        pass
+        is_dataframe_empty(None)
 
-    df = UnsupportedDataFrame()
-    with pytest.raises(ValueError, match="Unsupported DataFrame type"):
-        check_dataframe_empty(df)
 
+# ========================= Tests for count_dataframe_column_nulls =========================
 
-# ========================= check_dataframe_nulls_nans Tests =========================
 
-
-@pytest.fixture
-def sample_df():
-    """Generate sample DataFrame for testing check_dataframe_nulls_nans function.
-
-    Uses synthetic_data_generator to create consistent test data across backends.
-    """
-    return generate_synthetic_time_series(
-        backend="pandas", num_samples=3, num_features=2, with_nulls=True, null_percentage=0.3, drop_time=True
-    )
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_nulls_nans_no_nulls(backend: str) -> None:
-    """Test check_dataframe_nulls_nans with DataFrame containing no null values.
-
-    Uses synthetic data generator with no nulls to verify check_dataframe_nulls_nans correctly
-    identifies when there are no null values in specified columns.
-    """
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_count_dataframe_column_nulls_no_nulls(backend):
+    """Test count_dataframe_column_nulls with DataFrame containing no null values."""
     df = generate_synthetic_time_series(
-        backend=backend, num_samples=3, num_features=1, with_nulls=False, drop_time=True
+        backend=backend, num_samples=10, num_features=2, with_nulls=False, drop_time=True
     )
-    result = check_dataframe_nulls_nans(df, ["feature_1"])
+    result = count_dataframe_column_nulls(df, ["feature_1"])
     assert result == {"feature_1": 0}
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_nulls_nans_with_nulls(backend: str) -> None:
-    """Test check_dataframe_nulls_nans with DataFrame containing null values.
-
-    Uses synthetic data generator with nulls to verify check_dataframe_nulls_nans correctly
-    counts null values in specified columns.
-    """
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_count_dataframe_column_nulls_with_nulls(backend):
+    """Test count_dataframe_column_nulls with DataFrame containing null values."""
     df = generate_synthetic_time_series(
-        backend=backend, num_samples=10, num_features=1, with_nulls=True, null_percentage=0.5, drop_time=True
+        backend=backend, num_samples=10, num_features=2, with_nulls=True, null_percentage=0.5, drop_time=True
     )
-    result = check_dataframe_nulls_nans(df, ["feature_1"])
-    assert 4 <= result["feature_1"] <= 6  # ~50% nulls
+    result = count_dataframe_column_nulls(df, ["feature_1", "feature_2"])
+    # With 50% null percentage, expect around half the values to be null
+    assert 4 <= result["feature_1"] <= 6
+    assert 4 <= result["feature_2"] <= 6
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_nulls_nans_empty_dataframe(backend: str) -> None:
-    """Test check_dataframe_nulls_nans with an empty DataFrame."""
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_count_dataframe_column_nulls_empty_dataframe(backend):
+    """Test count_dataframe_column_nulls with an empty DataFrame."""
     df = generate_synthetic_time_series(backend=backend, num_samples=0, num_features=1, drop_time=True)
-    with pytest.raises(ValueError, match="Empty"):  # Generic pattern
-        check_dataframe_nulls_nans(df, ["feature_1"])
-
+    with pytest.raises(ValueError, match="Empty DataFrame provided"):
+        count_dataframe_column_nulls(df, ["feature_1"])
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_nulls_nans_nonexistent_column(backend: str) -> None:
-    """Test check_dataframe_nulls_nans with nonexistent column."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=3, num_features=1, drop_time=True)
-    with pytest.raises(ValueError, match="Column 'nonexistent' not found."):  # Match the specific message
-        check_dataframe_nulls_nans(df, ["nonexistent"])
 
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_count_dataframe_column_nulls_nonexistent_column(backend):
+    """Test count_dataframe_column_nulls with nonexistent column."""
+    df = generate_synthetic_time_series(backend=backend, num_samples=10, num_features=1, drop_time=True)
+    with pytest.raises(ValueError, match="Column 'nonexistent' not found"):
+        count_dataframe_column_nulls(df, ["nonexistent"])
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_nulls_nans_empty_column_list(backend: str) -> None:
-    """Test check_dataframe_nulls_nans with empty list of columns.
 
-    Verifies check_dataframe_nulls_nans returns empty dict for empty column list.
-    """
-    df = generate_synthetic_time_series(backend=backend, num_samples=3, num_features=1, drop_time=True)
-    result = check_dataframe_nulls_nans(df, [])
-    assert result == {}
+# ========================= Tests for convert_datetime_column_to_microseconds =========================
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_nulls_nans_all_nulls(backend: str) -> None:
-    """Test check_dataframe_nulls_nans with columns containing all null values.
+def test_convert_datetime_column_to_microseconds(synthetic_df):
+    """Test convert_datetime_column_to_microseconds with valid input."""
+    result = convert_datetime_column_to_microseconds(nw.from_native(synthetic_df), "time")
+    # Check if dtype indicates float (handle different backends)
+    dtype_str = str(result["time"].dtype).lower()
+    assert any(float_type in dtype_str for float_type in ["float", "f64"])
 
-    Uses synthetic data generator with 100% null percentage to verify
-    check_dataframe_nulls_nans correctly identifies when all values are null.
-    """
-    df = generate_synthetic_time_series(
-        backend=backend, num_samples=3, num_features=1, with_nulls=True, null_percentage=1.0, drop_time=True
-    )
-    result = check_dataframe_nulls_nans(df, ["feature_1"])
-    assert result == {"feature_1": 3}
 
+def test_convert_datetime_column_to_microseconds_already_numeric():
+    """Test convert_datetime_column_to_microseconds with already numeric column."""
+    df = pd.DataFrame({"time": [1, 2, 3]})
+    df = nw.from_native(df)
+    result = convert_datetime_column_to_microseconds(df, "time")
+    # Convert both to pandas DataFrames for comparison
+    result_pd = result if isinstance(result, pd.DataFrame) else result.to_native()
+    df_pd = df if isinstance(df, pd.DataFrame) else df.to_native()
+    pd.testing.assert_frame_equal(result_pd, df_pd)
 
-@pytest.mark.parametrize("backend", ["dask"])
-def test_check_dataframe_nulls_nans_lazy_compute(backend: str) -> None:
-    """Test check_dataframe_nulls_nans handles lazy evaluation with compute."""
-    # Generate a real synthetic Dask DataFrame with nulls
-    df = generate_synthetic_time_series(
-        backend=backend, num_samples=10, num_features=1, with_nulls=True, null_percentage=0.5, drop_time=True
-    )
-
-    # Perform the test directly without mocking
-    result = check_dataframe_nulls_nans(df, ["feature_1"])
-    print(f"Result from check_dataframe_nulls_nans:\n{result}")  # Debug: Verify result
-
-    # Verify null counts (depends on generator behavior)
-    assert "feature_1" in result
-    assert 0 <= result["feature_1"] <= 5  # Adjust based on null_percentage
 
+def test_convert_datetime_column_to_microseconds_invalid():
+    """Test convert_datetime_column_to_microseconds with invalid column."""
+    df = pd.DataFrame({"time": ["a", "b", "c"]})
+    df = nw.from_native(df)
+    with pytest.raises(TimeColumnError):
+        convert_datetime_column_to_microseconds(df, "time")
 
-# ========================= validate_and_convert_time_column Tests =========================
 
+# ========================= Tests for convert_column_to_datetime_type =========================
 
-@pytest.mark.parametrize("backend", ["pandas", "modin", "polars", "pyarrow", "dask"])
-def test_validate_and_convert_time_column_numeric_single(backend):
-    """Test validation and numeric conversion of time column for a single backend."""
-    # Generate synthetic data with the specified backend
-    df = generate_synthetic_time_series(
-        backend=backend, num_samples=3, num_features=1, drop_time=False, time_col_numeric=False
-    )
 
-    # Convert the time column to numeric
-    result = validate_and_convert_time_column(df, "time", conversion_type="numeric")
+def test_convert_column_to_datetime_type_missing_column():
+    """Test convert_column_to_datetime_type with missing column."""
+    df = pd.DataFrame({"value": [1, 2, 3]})
+    df = nw.from_native(df)
+    with pytest.raises(TimeColumnError, match="Column 'time' does not exist in DataFrame"):
+        convert_column_to_datetime_type(df, "time")
 
-    # Ensure the column was converted properly to numeric
-    if backend in ["pyarrow"]:
-        # PyArrow: Check if `time` exists in the schema (field names)
-        assert "time" in [
-            field.name for field in result.schema
-        ], f"'time' column not found in result for backend: {backend}"
-    else:
-        assert "time" in result.columns, f"'time' column not found in result for backend: {backend}"
 
-    # Backend-specific dtype validation
-    if backend in ["pandas", "modin"]:
-        # Pandas/Modin: Use DataFrame dtypes directly
-        resolved_dtype = result["time"].dtype
-        assert resolved_dtype in ["float64", "float32"], f"Expected numeric dtype for 'time', got {resolved_dtype}"
+def test_convert_column_to_datetime_type_already_datetime():
+    """Test convert_column_to_datetime_type with already datetime column."""
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3)})
+    df = nw.from_native(df)
+    result = convert_column_to_datetime_type(df, "time")
+    # Verify data is unchanged and type is still datetime
+    assert pd.api.types.is_datetime64_dtype(result["time"].dtype)
+    # Convert both to pandas for comparison
+    result_pd = result if isinstance(result, pd.DataFrame) else result.to_native()
+    df_pd = df if isinstance(df, pd.DataFrame) else df.to_native()
+    pd.testing.assert_frame_equal(result_pd, df_pd)
+
+
+def test_convert_column_to_datetime_type_from_string():
+    """Test convert_column_to_datetime_type with string input."""
+    df = pd.DataFrame({"time": ["2023-01-01", "2023-01-02"]})
+    df = nw.from_native(df)
+    result = convert_column_to_datetime_type(df, "time")
+    assert pd.api.types.is_datetime64_dtype(result["time"].dtype)
 
-    elif backend == "polars":
-        # Polars: Use `schema` for dtype resolution
-        resolved_dtype = result.schema.get("time")
-        assert str(resolved_dtype).lower() in [
-            "float64",
-            "float32",
-        ], f"Expected numeric dtype for Polars, got {resolved_dtype}"
 
-    elif backend == "pyarrow":
-        # PyArrow: Validate `ChunkedArray` dtype
-        resolved_dtype = result.schema.field("time").type
-        assert str(resolved_dtype).lower() in [
-            "double",
-            "float64",
-        ], f"Expected numeric dtype for PyArrow, got {resolved_dtype}"
+def test_convert_column_to_datetime_type_from_numeric():
+    """Test convert_column_to_datetime_type with numeric input."""
+    df = pd.DataFrame({"time": [1672531200, 1672617600]})  # Unix timestamps
+    df = nw.from_native(df)
+    result = convert_column_to_datetime_type(df, "time")
+    assert pd.api.types.is_datetime64_dtype(result["time"].dtype)
 
-    elif backend == "dask":
-        # Dask: Resolve dtype from `_meta`
-        resolved_dtype = result["time"]._meta.dtype
-        assert resolved_dtype in ["float64", "float32"], f"Expected numeric dtype for Dask, got {resolved_dtype}"
 
+def test_convert_column_to_datetime_type_conversion_failure():
+    """Test convert_column_to_datetime_type when all conversions fail."""
+    df = pd.DataFrame({"time": ["invalid", "dates", "here"]})
+    df = nw.from_native(df)
+    with pytest.raises(TimeColumnError, match="Column 'time' must be string or numeric to convert to datetime"):
+        convert_column_to_datetime_type(df, "time")
 
-# ========================= Edge case tests =========================
 
+def test_convert_column_to_datetime_type_invalid():
+    """Test convert_column_to_datetime_type with invalid input."""
+    df = pd.DataFrame({"time": [True, False]})
+    df = nw.from_native(df)
+    with pytest.raises(TimeColumnError):
+        convert_column_to_datetime_type(df, "time")
 
-def test_convert_to_numeric_unsupported_dataframe() -> None:
-    """Test convert_to_numeric raises ValueError for unsupported DataFrame type."""
 
-    class UnsupportedDataFrame:
-        """Mock class to simulate an unsupported DataFrame."""
+# ========================= Tests for validate_column_numeric_or_datetime =========================
 
-        pass
 
-    # Create an instance of the unsupported DataFrame
-    unsupported_df = UnsupportedDataFrame()
+def test_validate_column_numeric_or_datetime_numeric():
+    """Test validate_column_numeric_or_datetime with numeric column."""
+    df = pd.DataFrame({"time": [1, 2, 3]})
+    df = nw.from_native(df)
+    validate_column_numeric_or_datetime(df, "time")
 
-    # Attempt to call convert_to_numeric and expect a ValueError
-    with pytest.raises(ValueError, match=f"Unsupported DataFrame type: {type(unsupported_df).__name__}"):
-        convert_to_numeric(unsupported_df, time_col="time", col_expr=None, col_dtype="datetime64[ns]")
 
+def test_validate_column_numeric_or_datetime_datetime():
+    """Test validate_column_numeric_or_datetime with datetime column."""
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3)})
+    df = nw.from_native(df)
+    validate_column_numeric_or_datetime(df, "time")
 
-def test_convert_to_numeric_with_timezones():
-    """Test convert_to_numeric with timezone-aware and naive datetime columns.
 
-    This test ensures that:
-    - Timezone-aware datetime columns are correctly converted to numeric timestamps.
-    - Naive datetime columns are handled correctly.
-    - Columns with invalid types raise appropriate errors.
-    """
-    # Create a DataFrame with different datetime columns
-    df = pd.DataFrame(
-        {
-            "naive_datetime": pd.date_range("2023-01-01", periods=3),
-            "aware_datetime": pd.date_range("2023-01-01", periods=3, tz="UTC"),
-            "invalid_column": ["not_a_datetime", "still_not", "nope"],
-        }
-    )
+def test_validate_column_numeric_or_datetime_invalid():
+    """Test validate_column_numeric_or_datetime with invalid column."""
+    df = pd.DataFrame({"time": ["a", "b", "c"]})
+    df = nw.from_native(df)
+    with pytest.raises(ValueError):
+        validate_column_numeric_or_datetime(df, "time")
 
-    # Update col_dtype to match actual Pandas dtypes
-    naive_dtype = df["naive_datetime"].dtype
-    aware_dtype = df["aware_datetime"].dtype
 
-    # Test naive datetime column
-    result = convert_to_numeric(df, "naive_datetime", nw.col("naive_datetime"), naive_dtype)
-    assert "naive_datetime" in result.columns
-    assert pd.api.types.is_float_dtype(result["naive_datetime"]), "Expected numeric dtype for naive datetime."
+# ========================= Tests for validate_and_convert_time_column =========================
 
-    # Test timezone-aware datetime column
-    result = convert_to_numeric(df, "aware_datetime", nw.col("aware_datetime"), aware_dtype)
-    assert "aware_datetime" in result.columns
-    assert pd.api.types.is_float_dtype(result["aware_datetime"]), "Expected numeric dtype for timezone-aware datetime."
 
-    # Test invalid column type
-    with pytest.raises(ValueError, match="not a datetime column"):
-        convert_to_numeric(df, "invalid_column", nw.col("invalid_column"), df["invalid_column"].dtype)
+def test_validate_and_convert_time_column_validation_only():
+    """Test validate_and_convert_time_column with validation only (no conversion)."""
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3)})
+    df = nw.from_native(df)
+    result = validate_and_convert_time_column(df, "time", conversion_type=None)
+    # Verify data is unchanged
+    result_pd = result if isinstance(result, pd.DataFrame) else result.to_native()
+    df_pd = df if isinstance(df, pd.DataFrame) else df.to_native()
+    pd.testing.assert_frame_equal(result_pd, df_pd)
 
 
-def test_convert_to_numeric_error_handling():
-    """Test convert_to_numeric error handling for invalid column types."""
-    # Create a DataFrame with an invalid column type
-    df = pd.DataFrame({"invalid_col": ["not_a_datetime", "nope", "still_not"]})
+def test_validate_and_convert_time_column_numeric(synthetic_df):
+    """Test validation and numeric conversion of time column."""
+    result = validate_and_convert_time_column(nw.from_native(synthetic_df), "time", conversion_type="numeric")
+    # Check if dtype indicates float (handle different backends)
+    dtype_str = str(result["time"].dtype).lower()
+    assert any(float_type in dtype_str for float_type in ["float", "f64"])
 
-    # Test invalid column type
-    with pytest.raises(ValueError, match="is not a datetime column"):
-        convert_to_numeric(df, "invalid_col", nw.col("invalid_col"), df["invalid_col"].dtype)
 
+def test_validate_and_convert_time_column_datetime():
+    """Test validation and datetime conversion of time column."""
+    df = pd.DataFrame({"time": [1672531200, 1672617600]})
+    df = nw.from_native(df)
+    result = validate_and_convert_time_column(df, "time", conversion_type="datetime")
+    assert pd.api.types.is_datetime64_dtype(result["time"].dtype)
 
-def test_validate_time_column_type():
-    """Test validate_time_column_type for various scenarios."""
-    # Test valid numeric column
-    validate_time_column_type("numeric_col", "float64")  # Should not raise an error
 
-    # Test valid datetime column
-    validate_time_column_type("datetime_col", "datetime64[ns]")  # Should not raise an error
+def test_validate_and_convert_time_column_missing():
+    """Test for missing time column."""
+    df = pd.DataFrame({"value": [1, 2, 3]})
+    df = nw.from_native(df)
+    with pytest.raises(TimeColumnError):
+        validate_and_convert_time_column(df, "time")
 
-    # Test invalid column type
-    with pytest.raises(ValueError, match="neither numeric nor datetime"):
-        validate_time_column_type("invalid_col", "string")
 
-    # Test mixed-type column (invalid)
-    with pytest.raises(ValueError, match="neither numeric nor datetime"):
-        validate_time_column_type("mixed_col", "object")
+def test_validate_and_convert_time_column_invalid_type():
+    """Test for invalid conversion type."""
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3)})
+    df = nw.from_native(df)
+    with pytest.raises(ValueError):
+        validate_and_convert_time_column(df, "time", conversion_type="invalid")
 
-    # Test custom/user-defined types (invalid)
-    with pytest.raises(ValueError, match="neither numeric nor datetime"):
-        validate_time_column_type("custom_col", "custom_type")
 
+# ========================= Tests for validate_feature_columns_numeric =========================
 
-def test_convert_time_column_to_datetime_error_handling():
-    """Test convert_time_column_to_datetime error handling for invalid column types."""
-    # Create a DataFrame with an invalid column type
-    df = pd.DataFrame({"invalid_col": ["not_a_datetime", "nope", "still_not"]})
 
-    # Test invalid column type
-    with pytest.raises(ValueError, match="neither string nor numeric"):
-        convert_time_column_to_datetime(df, "invalid_col", nw.col("invalid_col"), df["invalid_col"].dtype)
+def test_validate_feature_columns_numeric_all_columns(synthetic_df):
+    """Test validate_feature_columns_numeric with all columns when time_col=None."""
+    # Create DataFrame with only numeric columns
+    df = pd.DataFrame(
+        {
+            "col1": [1, 2, 3],
+            "col2": [1.1, 2.2, 3.3],
+        }
+    )
+    df = nw.from_native(df)
+    validate_feature_columns_numeric(df)  # Should pass
 
 
-def test_validate_time_column_type_edge_cases():
-    """Test validate_time_column_type for edge cases."""
-    # Test very long column name
-    long_col_name = "a" * 300
-    validate_time_column_type(long_col_name, "datetime64[ns]")  # Should not raise an error
+def test_validate_feature_columns_numeric_exclude_time(synthetic_df):
+    """Test validate_feature_columns_numeric with time column excluded."""
+    validate_feature_columns_numeric(synthetic_df, time_col="time")  # Should pass
 
-    # Test numeric column with unusual dtype
-    validate_time_column_type("unusual_numeric", "float128")  # Should not raise an error
 
-    # Test datetime column with timezone
-    validate_time_column_type("tz_datetime", "datetime64[ns, UTC]")  # Should not raise an error
+def test_validate_feature_columns_numeric_missing_time():
+    """Test validate_feature_columns_numeric with missing time column."""
+    df = pd.DataFrame({"value": [1, 2, 3]})
+    df = nw.from_native(df)
+    with pytest.raises(TimeColumnError, match="Column 'time' does not exist"):
+        validate_feature_columns_numeric(df, time_col="time")
 
 
-def test_convert_time_column_to_datetime_with_string_column():
-    """Test convert_time_column_to_datetime with string datetime column."""
-    # Create a DataFrame with a string datetime column
+def test_validate_feature_columns_numeric_invalid_feature():
+    """Test validate_feature_columns_numeric with non-numeric feature column."""
     df = pd.DataFrame(
         {
-            "string_datetime": ["2023-01-01", "2023-01-02", "2023-01-03"],
+            "time": pd.date_range("2023-01-01", periods=3),
+            "value": ["a", "b", "c"],  # String type (invalid)
         }
     )
-
-    # Call convert_time_column_to_datetime for the string column
-    result = convert_time_column_to_datetime(df, "string_datetime", nw.col("string_datetime"), "string")
-
-    # Check that the column was converted correctly
-    assert "string_datetime" in result.columns, "Column 'string_datetime' not found in result."
-    assert pd.api.types.is_datetime64_any_dtype(
-        result["string_datetime"]
-    ), "Expected column 'string_datetime' to be converted to datetime."
+    df = nw.from_native(df)
+    with pytest.raises(ValueError, match="Column 'value' must be numeric"):
+        validate_feature_columns_numeric(df, time_col="time")
 
 
-def test_convert_time_column_to_datetime_with_numeric_column():
-    """Test convert_time_column_to_datetime with numeric timestamp column."""
-    # Create a DataFrame with a numeric timestamp column
+def test_validate_feature_columns_numeric_all_invalid():
+    """Test validate_feature_columns_numeric with all non-numeric columns."""
     df = pd.DataFrame(
         {
-            "numeric_timestamp": [1672531200, 1672617600, 1672704000],  # Unix timestamps for Jan 1-3, 2023
+            "col1": ["a", "b", "c"],
+            "col2": ["d", "e", "f"],
         }
     )
-
-    # Call convert_time_column_to_datetime for the numeric column
-    result = convert_time_column_to_datetime(df, "numeric_timestamp", nw.col("numeric_timestamp"), "float")
-
-    # Check that the column was converted correctly
-    assert "numeric_timestamp" in result.columns, "Column 'numeric_timestamp' not found in result."
-    assert pd.api.types.is_datetime64_any_dtype(
-        result["numeric_timestamp"]
-    ), "Expected column 'numeric_timestamp' to be converted to datetime."
-
-
-def test_validate_and_convert_time_column_missing_time_column():
-    """Test for missing time column in validate_and_convert_time_column."""
-    df = pd.DataFrame({"value": [1, 2, 3]})
-    with pytest.raises(TimeColumnError, match="Column 'time' does not exist in the DataFrame."):
-        validate_and_convert_time_column(df, "time", conversion_type="numeric")
-
-
-def test_validate_and_convert_time_column_invalid_conversion_type():
-    """Test for invalid conversion type in validate_and_convert_time_column."""
-    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3)})
-    with pytest.raises(
-        ValueError, match="Invalid conversion_type 'invalid_type'. Must be one of 'numeric', 'datetime', or None."
-    ):
-        validate_and_convert_time_column(df, "time", conversion_type="invalid_type")
-
-
-def test_validate_and_convert_time_column_to_numeric():
-    """Test validate_and_convert_time_column with numeric conversion."""
-    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3)})
-    result = validate_and_convert_time_column(df, "time", conversion_type="numeric")
-    assert "time" in result.columns
-    assert pd.api.types.is_float_dtype(result["time"]), "Expected numeric dtype for 'time' column."
-
-
-def test_validate_and_convert_time_column_to_datetime():
-    """Test validate_and_convert_time_column with datetime conversion."""
-    df = pd.DataFrame({"time": [1672531200, 1672617600, 1672704000]})
-    result = validate_and_convert_time_column(df, "time", conversion_type="datetime")
-    assert "time" in result.columns
-    assert pd.api.types.is_datetime64_any_dtype(result["time"]), "Expected datetime dtype for 'time' column."
-
-
-def test_validate_and_convert_time_column_validation_only():
-    """Test validate_and_convert_time_column with validation-only path."""
-    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3)})
-    result = validate_and_convert_time_column(df, "time", conversion_type=None)
-    assert "time" in result.columns
-    assert pd.api.types.is_datetime64_any_dtype(result["time"]), "Expected datetime dtype for 'time' column."
-
-
-# ========================= validate_dataframe_column_types Tests =========================
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_validate_dataframe_column_types_basic(backend: str) -> None:
-    """Test validate_dataframe_column_types with valid numeric and datetime columns."""
-    df = generate_synthetic_time_series(
-        backend=backend,
-        num_samples=3,
-        num_features=2,
-        time_col_numeric=False,  # This gives us a datetime time column
-        drop_time=False,
-    )
-    df = nw.from_native(df)
-    validate_dataframe_column_types(df, "time")  # Should not raise error
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_validate_dataframe_column_types_with_lazy_evaluation(backend: str) -> None:
-    """Test validate_dataframe_column_types handles lazy evaluation correctly."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=3, num_features=2, drop_time=False)
     df = nw.from_native(df)
+    with pytest.raises(ValueError, match="Column 'col1' must be numeric"):
+        validate_feature_columns_numeric(df)
 
-    # Make DataFrame lazy if possible
-    if hasattr(df.to_native(), "lazy"):
-        df = df.to_native().lazy()
-        df = nw.from_native(df)
 
-    validate_dataframe_column_types(df, "time")  # Should not raise error
+# ========================= Tests for sort_dataframe_time =========================
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_validate_dataframe_column_types_missing_column(backend: str) -> None:
-    """Test validate_dataframe_column_types raises error for missing column."""
+def test_sort_dataframe_time_numeric():
+    """Test sort_dataframe_time with numeric time column."""
+    df = pd.DataFrame({"time": [3, 1, 2], "value": [30, 10, 20]})
+    df = nw.from_native(df)
+    result = sort_dataframe_time(df, "time")
+    expected = pd.DataFrame({"time": [1, 2, 3], "value": [10, 20, 30]})
+    # Convert result to pandas DataFrame if needed
+    result_pd = result if isinstance(result, pd.DataFrame) else result.to_native()
+    pd.testing.assert_frame_equal(result_pd.reset_index(drop=True), expected)
+
+
+def test_sort_dataframe_time_datetime(synthetic_df):
+    """Test sort_dataframe_time with datetime time column."""
+    df_nw = nw.from_native(synthetic_df)
+    # Create a copy by selecting all columns
+    shuffled = df_nw.select([nw.col(col) for col in df_nw.columns])
+    # Shuffle the DataFrame - handle different backends
+    native_df = shuffled.to_native()
+    # Check for specific DataFrame types
+    if hasattr(native_df, "iloc"):  # pandas-like DataFrame
+        shuffled = nw.from_native(native_df.sample(frac=1, random_state=42))
+    else:  # polars DataFrame
+        shuffled = nw.from_native(native_df.sample(fraction=1.0, seed=42))
+    result = sort_dataframe_time(shuffled, "time")
+    # Convert both to pandas Series for comparison
+    result_time = pd.Series(result["time"].to_native() if hasattr(result["time"], "to_native") else result["time"])
+    df_time = pd.Series(df_nw["time"].to_native() if hasattr(df_nw["time"], "to_native") else df_nw["time"])
+    pd.testing.assert_series_equal(result_time.reset_index(drop=True), df_time.reset_index(drop=True))
+
+
+# ========================= Tests for validate_temporal_ordering =========================
+
+
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_validate_temporal_ordering_basic_backend(backend):
+    """Test basic temporal validation across backends."""
     df = generate_synthetic_time_series(
-        backend=backend,
-        num_samples=3,
-        num_features=2,
-        drop_time=True,  # This removes the time column
+        backend=backend, num_samples=4, num_features=1, time_col_numeric=True, drop_time=False
     )
-    df = nw.from_native(df)
-
-    with pytest.raises(ValueError, match="Column 'nonexistent' does not exist"):
-        validate_dataframe_column_types(df, "nonexistent")
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_validate_dataframe_column_types_invalid_type(backend: str) -> None:
-    """Test validate_dataframe_column_types raises error for invalid column type."""
-    # Create DataFrame with string column
-    if backend == "pandas":
-        df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3), "string_col": ["a", "b", "c"]})
-    else:
-        df = convert_to_backend(
-            pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3), "string_col": ["a", "b", "c"]}), backend
-        )
-
-    with pytest.raises(ValueError, match="Column 'string_col' must be numeric but found type 'String'"):
-        validate_dataframe_column_types(df, "time")
-
-
-@pytest.mark.parametrize("backend", ["dask"])
-def test_validate_dataframe_column_types_lazy_compute(backend: str) -> None:
-    """Test validate_dataframe_column_types handles lazy computation correctly."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=3, num_features=2, drop_time=False)
-    df = nw.from_native(df)
-
-    # The function should handle compute() internally
-    validate_dataframe_column_types(df, "time")  # Should not raise error
-
-
-# ========================= Tests for sort_dataframe_time =========================
+    validate_temporal_ordering(df, "time")
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_sort_dataframe_time_valid(backend: str) -> None:
-    """Test sort_dataframe_time with valid time column for all backends."""
-    # Generate backend-native synthetic data
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_validate_temporal_ordering_equidistant_backend(backend):
+    """Test equidistant sampling across backends."""
     df = generate_synthetic_time_series(
-        backend=backend,
-        num_samples=10,
-        num_features=3,
-        time_col_numeric=True,
-        drop_time=False,
-    )
-
-    # Materialize the original DataFrame if lazy
-    if hasattr(df, "collect"):
-        df = df.collect()
-    elif hasattr(df, "compute"):
-        df = df.compute()
-
-    # Sort using Narwhalified function
-    sorted_df = sort_dataframe_time(df, time_col="time", ascending=True)
-
-    # Materialize the sorted DataFrame if lazy
-    if hasattr(sorted_df, "collect"):
-        sorted_df = sorted_df.collect()
-    elif hasattr(sorted_df, "compute"):
-        sorted_df = sorted_df.compute()
-
-    # Convert both sorted and original to Pandas for validation
-    actual = nw.from_native(sorted_df).to_pandas()
-    expected = nw.from_native(df).to_pandas().sort_values("time", ascending=True)
-
-    # Assert that the time column is sorted
-    pd.testing.assert_frame_equal(
-        actual.reset_index(drop=True),
-        expected.reset_index(drop=True),
-        check_dtype=False,  # Ignore dtype differences caused by backend conversions
+        backend=backend, num_samples=4, num_features=1, time_col_numeric=False, drop_time=False
     )
+    validate_temporal_ordering(df, "time", enforce_equidistant_sampling=True)
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_sort_dataframe_time_missing_time_column(backend: str) -> None:
-    """Test sort_dataframe_time raises an error for missing time column."""
-    # Generate synthetic data without a time column
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_validate_temporal_ordering_multi_entity_backend(backend):
+    """Test multi-entity validation across backends."""
     df = generate_synthetic_time_series(
-        backend=backend,
-        num_samples=10,
-        num_features=3,
-        drop_time=True,  # Drop the time column
+        backend=backend, num_samples=4, num_features=1, time_col_numeric=True, drop_time=False
     )
+    # Test basic validation
+    validate_temporal_ordering(df, "time")
 
-    # Expect ValueError for missing 'time' column
-    with pytest.raises(ValueError, match="Column 'time' does not exist in the DataFrame"):
-        sort_dataframe_time(df, time_col="time", ascending=True)
-
-
-# ========================= convert_to_backend Tests =========================
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_convert_to_backend_narwhalified(backend: str) -> None:
-    """Test converting narwhalified DataFrame to target backend."""
-    df = generate_synthetic_time_series(backend="pandas", num_samples=10, num_features=3)
-    df_narwhals = nw.from_native(df)
-
-    # Mock `to_native` to verify it's called
-    with mock.patch.object(df_narwhals, "to_native", wraps=df_narwhals.to_native) as mock_to_native:
-        converted_df = convert_to_backend(df_narwhals, backend)
-        expected_type = TEMPORALSCOPE_CORE_BACKEND_TYPES[backend]
-        assert isinstance(converted_df, expected_type), f"Expected {expected_type}, got {type(converted_df)}."
-        mock_to_native.assert_called_once()
-
-
-def test_convert_to_backend_lazy_dataframe() -> None:
-    """Test handling of lazy DataFrame materialization."""
-    df = generate_synthetic_time_series(backend="dask", num_samples=10, num_features=3)
-
-    # Mock `is_lazy_evaluation` to verify it's called
-    with mock.patch("temporalscope.core.core_utils.is_lazy_evaluation", wraps=is_lazy_evaluation) as mock_is_lazy:
-        converted_df = convert_to_backend(df, "pandas")
-        assert isinstance(converted_df, TEMPORALSCOPE_CORE_BACKEND_TYPES["pandas"])
-        mock_is_lazy.assert_called_once_with(df)
-
-
-def test_convert_to_backend_lazy_compute() -> None:
-    """Test lazy evaluation materialization using compute."""
-    df = generate_synthetic_time_series(backend="dask", num_samples=10, num_features=3)
-    converted_df = convert_to_backend(df, "pandas")
-    assert isinstance(converted_df, pd.DataFrame)
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_convert_to_backend_valid(backend: str) -> None:
-    """Test DataFrame conversion to each valid backend."""
-    df = generate_synthetic_time_series(backend="pandas", num_samples=10, num_features=3)
-    converted_df = convert_to_backend(df, backend)
-    expected_type = TEMPORALSCOPE_CORE_BACKEND_TYPES[backend]
-    assert isinstance(converted_df, expected_type), f"Expected {expected_type}, got {type(converted_df)}."
-
-
-@pytest.mark.parametrize("backend", ["unsupported_backend"])
-def test_convert_to_backend_invalid_backend(backend: str) -> None:
-    """Test that convert_to_backend raises error for unsupported backend."""
-    df = generate_synthetic_time_series(backend="pandas", num_samples=10, num_features=3)
-    with pytest.raises(
-        UnsupportedBackendError, match=f"Backend '{backend}' is not supported by TemporalScope. Supported backends are:"
-    ):
-        convert_to_backend(df, backend)
-
-
-def test_convert_to_backend_error_handling() -> None:
-    """Test handling of conversion errors."""
-
-    class InvalidDataFrame:
-        def __init__(self):
-            self._df = "Invalid"
-
-        def compute(self):
-            raise Exception("Computation failed")
-
-    df = InvalidDataFrame()
-    with pytest.raises(UnsupportedBackendError, match="Input DataFrame type 'InvalidDataFrame' is not supported"):
-        convert_to_backend(df, "pandas")
-
-
-# ========================= check_dataframe_nulls_nans Tests =========================
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_nulls_nans_valid_columns(backend: str) -> None:
-    """Test check_dataframe_nulls_nans for valid columns."""
-    data = {"col1": [1, None, 2], "col2": [None, 2, 3]}
-
-    # Convert to the specific backend
-    if backend == "dask":
-        df = TEMPORALSCOPE_BACKEND_CONVERTERS[backend](pd.DataFrame(data), npartitions=1)
-    else:
-        df = TEMPORALSCOPE_BACKEND_CONVERTERS[backend](pd.DataFrame(data), 1)
-
-    # Call check_dataframe_nulls_nans and validate the result
-    result = check_dataframe_nulls_nans(df, column_names=["col1", "col2"])
-    assert result == {"col1": 1, "col2": 1}, f"Unexpected result for backend {backend}: {result}"
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_nulls_nans_empty_df(backend: str) -> None:
-    """Test check_dataframe_nulls_nans raises error for empty DataFrame."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=0, num_features=3)
-    with pytest.raises(ValueError, match="Empty DataFrame provided."):
-        check_dataframe_nulls_nans(df, column_names=["col1", "col2"])
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_check_dataframe_nulls_nans_missing_columns(backend: str) -> None:
-    """Test check_dataframe_nulls_nans raises error for missing columns."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=10, num_features=2)
-    with pytest.raises(ValueError, match="Column 'nonexistent' not found."):
-        check_dataframe_nulls_nans(df, column_names=["nonexistent"])
-
-
-def test_check_dataframe_nulls_nans_error_handling() -> None:
-    """Test check_dataframe_nulls_nans raises UnsupportedBackendError for unsupported DataFrame."""
-
-    class InvalidDataFrame:
-        """A mock class for an unsupported DataFrame type."""
-
-        pass
-
-    # Instantiate the invalid DataFrame
-    invalid_df = InvalidDataFrame()
-
-    # Ensure UnsupportedBackendError is raised during validation
-    with pytest.raises(UnsupportedBackendError, match="The input DataFrame is not supported by TemporalScope."):
-        check_dataframe_nulls_nans(invalid_df, column_names=["col1"])
-
-
-# ========================= Tests for get_dataframe_backend =========================
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_get_dataframe_backend_valid(backend: str) -> None:
-    """Test get_dataframe_backend returns correct backend for valid DataFrame types."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=10, num_features=2)
-    result = get_dataframe_backend(df)
-    assert result == backend, f"Expected backend '{backend}', but got '{result}'."
-
-
-def test_get_dataframe_backend_narwhalified(narwhalified_df) -> None:
-    """Test get_dataframe_backend handles narwhalified DataFrames."""
-    # Expected to return the native backend (e.g., 'pandas')
-    backend = get_dataframe_backend(narwhalified_df)
-    assert backend == "pandas", f"Expected backend 'pandas', but got '{backend}'."
-
-
-def test_get_dataframe_backend_unsupported_type() -> None:
-    """Test get_dataframe_backend raises UnsupportedBackendError for unsupported DataFrame types."""
-
-    class UnsupportedDataFrame:
-        pass
-
-    df = UnsupportedDataFrame()
-    with pytest.raises(UnsupportedBackendError, match="Unknown DataFrame type"):
-        get_dataframe_backend(df)
-
-
-def test_get_dataframe_backend_unknown_type() -> None:
-    """Test get_dataframe_backend raises UnsupportedBackendError for unknown valid types."""
-
-    class CustomDataFrame:
-        def __init__(self):
-            self.data = pd.DataFrame({"col": [1, 2, 3]})
-
-        def to_pandas(self):
-            return self.data
-
-    df = CustomDataFrame()
-    with pytest.raises(UnsupportedBackendError, match="Unknown DataFrame type"):
-        get_dataframe_backend(df)
-
-
-# ========================= Unsupported DataFrame Tests =========================
-
-
-def test_convert_time_column_to_datetime_unsupported_dataframe() -> None:
-    """Test convert_time_column_to_datetime raises UnsupportedBackendError for unsupported DataFrame."""
-
-    class UnsupportedDataFrame:
-        pass
-
-    df = UnsupportedDataFrame()
-    with pytest.raises(UnsupportedBackendError, match=f"Unsupported DataFrame type: {type(df).__name__}"):
-        convert_time_column_to_datetime(df, time_col="time", col_expr=None, col_dtype="string")
-
-
-def test_validate_and_convert_time_column_unsupported_dataframe() -> None:
-    """Test validate_and_convert_time_column raises UnsupportedBackendError for unsupported DataFrame."""
-
-    class UnsupportedDataFrame:
-        pass
-
-    df = UnsupportedDataFrame()
-    with pytest.raises(UnsupportedBackendError, match=f"Unsupported DataFrame type: {type(df).__name__}"):
-        validate_and_convert_time_column(df, time_col="time", conversion_type="numeric")
-
-
-def test_validate_dataframe_column_types_unsupported_dataframe() -> None:
-    """Test validate_dataframe_column_types raises UnsupportedBackendError for unsupported DataFrame."""
-
-    class UnsupportedDataFrame:
-        pass
-
-    df = UnsupportedDataFrame()
-    with pytest.raises(UnsupportedBackendError, match=f"Unsupported DataFrame type: {type(df).__name__}"):
-        validate_dataframe_column_types(df, time_col="time")
+    # Test missing id_col error
+    with pytest.raises(ValueError, match="Column 'missing_id' does not exist"):
+        validate_temporal_ordering(df, "time", id_col="missing_id")
 
 
-def test_sort_dataframe_time_unsupported_dataframe() -> None:
-    """Test sort_dataframe_time raises UnsupportedBackendError for unsupported DataFrame."""
+def test_validate_temporal_ordering_basic():
+    """Test validate_temporal_ordering with default ML/DL settings.
 
-    class UnsupportedDataFrame:
-        pass
+    Validates that:
+    1. No information leakage in train/test splits
+    2. Proper sequence ordering for time series models
+    3. Valid feature/target relationships
 
-    df = UnsupportedDataFrame()
-    with pytest.raises(UnsupportedBackendError, match=f"Unsupported DataFrame type: {type(df).__name__}"):
-        sort_dataframe_time(df, time_col="time", ascending=True)
-
-
-def test_sort_dataframe_time_with_numeric_column():
-    """Test sort_dataframe_time function with a numeric time column."""
-    df = pd.DataFrame({"time": [3, 1, 2], "value": [30, 10, 20]})
-
-    # Sort the DataFrame by the numeric time column
-    sorted_df = sort_dataframe_time(df, time_col="time", ascending=True)
-
-    # Assert the time column is sorted
-    expected = pd.DataFrame({"time": [1, 2, 3], "value": [10, 20, 30]})
-    pd.testing.assert_frame_equal(sorted_df.reset_index(drop=True), expected)
+    Note: In ML context, we typically want strict ordering
+    to prevent data leakage and ensure proper temporal splits.
+    """
+    # Simple test data with strictly increasing timestamps
+    df = pd.DataFrame(
+        {
+            "time": [1.0, 2.0, 3.0, 4.0],
+            "value": [10.0, 20.0, 30.0, 40.0],
+        }
+    )
+    df = nw.from_native(df)  # Convert to Narwhals DataFrame
+    validate_temporal_ordering(df, "time")  # Should pass
 
 
-def test_sort_dataframe_time_with_datetime_column():
-    """Test sort_dataframe_time function with a datetime time column."""
-    df = pd.DataFrame({"time": pd.to_datetime(["2023-01-03", "2023-01-01", "2023-01-02"]), "value": [30, 10, 20]})
+def test_validate_temporal_ordering_duplicates():
+    """Test validate_temporal_ordering with duplicate timestamps.
 
-    # Sort the DataFrame by the datetime time column
-    sorted_df = sort_dataframe_time(df, time_col="time", ascending=True)
+    Should raise TimeColumnError when duplicates are found.
+    """
+    # Test data with duplicate timestamps
+    df = pd.DataFrame(
+        {
+            "time": [1.0, 2.0, 2.0, 3.0],  # Duplicate at t=2
+            "value": [10.0, 20.0, 25.0, 30.0],
+        }
+    )
+    df = nw.from_native(df)
 
-    # Assert the time column is sorted
-    expected = pd.DataFrame({"time": pd.to_datetime(["2023-01-01", "2023-01-02", "2023-01-03"]), "value": [10, 20, 30]})
-    pd.testing.assert_frame_equal(sorted_df.reset_index(drop=True), expected)
+    with pytest.raises(TimeColumnError, match="Duplicate timestamps found"):
+        validate_temporal_ordering(df, "time")
 
 
-def test_sort_dataframe_time_with_missing_time_column():
-    """Test sort_dataframe_time raises an error for missing time column."""
-    df = pd.DataFrame({"value": [30, 10, 20]})
+def test_validate_temporal_ordering_multi_entity():
+    """Test validate_temporal_ordering with multiple entities.
 
-    with pytest.raises(ValueError, match="Column 'time' does not exist in the DataFrame."):
-        sort_dataframe_time(df, time_col="time", ascending=True)
+    Validates proper handling of grouped time series data.
+    """
+    # Test data with multiple entities, each with unique timestamps
+    df = pd.DataFrame(
+        {
+            "id": [1, 1, 2, 2],
+            "time": [1.0, 2.0, 1.0, 2.0],  # Same times ok for different ids
+            "value": [10.0, 20.0, 15.0, 25.0],
+        }
+    )
+    df = nw.from_native(df)
+    validate_temporal_ordering(df, "time", id_col="id")  # Should pass
 
+    # Test data with duplicate timestamps within an entity
+    df_bad = pd.DataFrame(
+        {
+            "id": [1, 1, 2, 2],
+            "time": [1.0, 1.0, 2.0, 3.0],  # Duplicate for id=1
+            "value": [10.0, 15.0, 20.0, 30.0],
+        }
+    )
+    df_bad = nw.from_native(df_bad)
 
-# ======================= validate_temporal_uniqueness Tests =======================
+    with pytest.raises(TimeColumnError, match="Duplicate timestamps found within groups"):
+        validate_temporal_ordering(df_bad, "time", id_col="id")
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_validate_temporal_uniqueness_no_duplicates(backend):
-    """Test validate_temporal_uniqueness with no duplicates or violations."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=10, num_features=1, time_col_numeric=False)
-    validate_temporal_uniqueness(df, time_col="time")  # Should not raise any errors
+def test_validate_temporal_ordering_equidistant():
+    """Test validate_temporal_ordering with equidistant sampling requirement.
 
+    Validates proper handling of regular time intervals required by
+    classical time series models like ARIMA.
+    """
+    # Test data with regular intervals
+    df = pd.DataFrame(
+        {
+            "time": [1.0, 2.0, 3.0, 4.0],  # Equal spacing of 1.0
+            "value": [10.0, 20.0, 30.0, 40.0],
+        }
+    )
+    df = nw.from_native(df)
+    validate_temporal_ordering(df, "time", enforce_equidistant_sampling=True)  # Should pass
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_validate_temporal_uniqueness_with_duplicates(backend):
-    """Test that validate_temporal_uniqueness raises ValueError for duplicate timestamps."""
-    # Create a simple pandas DataFrame with duplicates and ensure 'time' is datetime
-    df_pandas = pd.DataFrame({"time": ["2023-01-01", "2023-01-01", "2023-02-15"], "value": [100.0, 150.0, 200.0]})
-    df_pandas["time"] = pd.to_datetime(df_pandas["time"])  # Convert to datetime
+    # Test data with irregular intervals
+    df_irregular = pd.DataFrame(
+        {
+            "time": [1.0, 2.0, 4.0, 7.0],  # Irregular spacing
+            "value": [10.0, 20.0, 30.0, 40.0],
+        }
+    )
+    df_irregular = nw.from_native(df_irregular)
 
-    # Convert this pandas DataFrame to the desired backend
-    df_converted = convert_to_backend(df_pandas, backend=backend)
+    with pytest.raises(TimeColumnError, match="Irregular time sampling found"):
+        validate_temporal_ordering(df_irregular, "time", enforce_equidistant_sampling=True)
 
-    # Expect a ValueError due to duplicates
-    with pytest.raises(ValueError, match="Duplicate timestamps"):
-        validate_temporal_uniqueness(df_converted, time_col="time")
 
+def test_validate_temporal_ordering_datetime():
+    """Test validate_temporal_ordering with datetime values.
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_validate_temporal_uniqueness_with_nulls(backend):
-    """Test that validate_temporal_uniqueness raises ValueError for null values in the time column."""
-    # Create a pandas DataFrame with one of the time values as NaT (null datetime)
-    df_pandas = pd.DataFrame(
-        {"time": [pd.Timestamp("2023-01-01"), pd.NaT, pd.Timestamp("2023-01-03")], "value": [100.0, 200.0, 300.0]}
+    Validates proper handling of datetime timestamps.
+    """
+    # Test data with datetime timestamps
+    df = pd.DataFrame(
+        {
+            "time": pd.date_range("2023-01-01", periods=4, freq="D"),
+            "value": [10.0, 20.0, 30.0, 40.0],
+        }
     )
+    df = nw.from_native(df)
+    validate_temporal_ordering(df, "time")  # Should pass
+    validate_temporal_ordering(df, "time", enforce_equidistant_sampling=True)  # Should pass with daily frequency
 
-    # Convert this pandas DataFrame to the desired backend
-    df_converted = convert_to_backend(df_pandas, backend=backend)
-
-    # Expect a ValueError due to null values in the time column
-    with pytest.raises(ValueError, match="Null values found"):
-        validate_temporal_uniqueness(df_converted, time_col="time")
-
+    # Test data with irregular datetime intervals
+    df_irregular = pd.DataFrame(
+        {
+            "time": [
+                pd.Timestamp("2023-01-01"),
+                pd.Timestamp("2023-01-02"),
+                pd.Timestamp("2023-01-04"),  # Gap
+                pd.Timestamp("2023-01-07"),  # Gap
+            ],
+            "value": [10.0, 20.0, 30.0, 40.0],
+        }
+    )
+    df_irregular = nw.from_native(df_irregular)
 
-def test_validate_temporal_uniqueness_missing_time_column():
-    """Test validate_temporal_uniqueness raises error for missing time column."""
-    df = pd.DataFrame({"value": [1, 2, 3]})  # No 'time' column
-    with pytest.raises(TimeColumnError, match="Invalid time column: Column 'time' does not exist"):
-        validate_temporal_uniqueness(df, time_col="time")
+    validate_temporal_ordering(df_irregular, "time")  # Should pass without equidistant requirement
+    with pytest.raises(TimeColumnError, match="Irregular time sampling found"):
+        validate_temporal_ordering(df_irregular, "time", enforce_equidistant_sampling=True)
 
 
-def test_validate_temporal_uniqueness_non_datetime_column():
-    """Test validate_temporal_uniqueness raises error for non-datetime time column."""
-    df = pd.DataFrame({"time": ["a", "b", "c"]})  # Invalid column type
-    with pytest.raises(TimeColumnError, match="Invalid time column: Column 'time' is neither numeric nor datetime"):
-        validate_temporal_uniqueness(df, time_col="time")
+def test_validate_temporal_ordering_hierarchical():
+    """Test validate_temporal_ordering with hierarchical time series."""
+    # Create test data with different sampling rates
+    df = pd.DataFrame(
+        {
+            "time": [1, 2, 3, 4, 1, 2, 3, 4],
+            "id": ["A", "A", "A", "A", "B", "B", "B", "B"],
+            "value": [10, 20, 30, 40, 50, 60, 70, 80],
+        }
+    )
+    df = nw.from_native(df)
+    validate_temporal_ordering(df, "time", id_col="id")  # Should pass
+    validate_temporal_ordering(df, "time", id_col="id", enforce_equidistant_sampling=True)  # Should pass
 
 
-def test_validate_temporal_uniqueness_warn_on_duplicates_simple():
-    """Check that validate_temporal_uniqueness issues a UserWarning.
+def test_validate_temporal_ordering_multi_sensor():
+    """Test validate_temporal_ordering with multi-sensor data.
 
-    This occurs when duplicates are present and raise_error=False.
+    Special case: Multi-sensor data with different sampling rates
+    - Each sensor (group) has its own timeline
+    - No overlap required between groups
+    - Common in IoT and industrial monitoring
     """
-    # Create a simple Pandas DataFrame with duplicate timestamps and ensure 'time' is datetime
-    df = pd.DataFrame({"time": ["2023-01-01", "2023-01-01", "2023-02-15"], "value": [100.0, 150.0, 200.0]})
-    df["time"] = pd.to_datetime(df["time"])  # Convert to datetime
-
-    # Now call the function with raise_error=False and expect a UserWarning due to duplicates
-    with pytest.warns(UserWarning, match="Duplicate timestamps"):
-        validate_temporal_uniqueness(df, time_col="time", raise_error=False)
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_validate_temporal_uniqueness_with_id_col(backend):
-    """Test validate_temporal_uniqueness includes id_col in the error message."""
-    # Create a pandas DataFrame with duplicates and a datetime time column
-    df_pandas = pd.DataFrame({"time": ["2023-01-01", "2023-01-01", "2023-02-15"], "value": [100.0, 150.0, 200.0]})
-    df_pandas["time"] = pd.to_datetime(df_pandas["time"])  # Ensure datetime
-
-    # Convert to the desired backend
-    df_converted = convert_to_backend(df_pandas, backend=backend)
-
-    # Expect a ValueError mentioning id_col due to duplicates
-    with pytest.raises(ValueError, match=r"Column 'patient_id' does not exist"):
-        validate_temporal_uniqueness(df_converted, time_col="time", id_col="patient_id")
-
+    # Test data with multiple sensors, each with unique timestamps
+    df = pd.DataFrame(
+        {
+            "id": ["A", "A", "B", "B"],  # Two sensors
+            "time": [1.0, 2.0, 1.0, 2.0],  # Same times ok for different sensors
+            "value": [10.0, 20.0, 15.0, 25.0],
+        }
+    )
+    df = nw.from_native(df)
+    validate_temporal_ordering(df, "time", id_col="id")  # Should pass
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_validate_temporal_uniqueness_lazy_evaluation(backend):
-    """Test validate_temporal_uniqueness handles lazy evaluation correctly."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=10, num_features=1, time_col_numeric=False)
-    validate_temporal_uniqueness(df, time_col="time")
-    assert len(df) == 10, "Expected the same number of rows after validation."
+    # Test data with duplicate timestamps within a sensor
+    df_bad = pd.DataFrame(
+        {
+            "id": ["A", "A", "B", "B"],
+            "time": [1.0, 1.0, 2.0, 3.0],  # Duplicate for sensor A
+            "value": [10.0, 15.0, 20.0, 30.0],
+        }
+    )
+    df_bad = nw.from_native(df_bad)
 
+    with pytest.raises(TimeColumnError, match="Duplicate timestamps found within groups"):
+        validate_temporal_ordering(df_bad, "time", id_col="id")
 
-def test_validate_temporal_uniqueness_empty_dataframe():
-    """Test validate_temporal_uniqueness raises error for empty DataFrame."""
-    df = pd.DataFrame({"time": []})
-    with pytest.raises(ValueError, match="Empty DataFrame provided."):
-        validate_temporal_uniqueness(df, time_col="time")
 
+def test_validate_temporal_ordering_mixed_frequency():
+    """Test validate_temporal_ordering with mixed frequency data.
 
-# ========================= Tests for convert_datetime_column_to_numeric =========================
+    Tests handling of:
+    1. Regular sampling within groups
+    2. Different frequencies across groups
+    3. Equidistant sampling validation
+    """
+    # Test data with different but regular frequencies per group
+    df = pd.DataFrame(
+        {
+            "id": ["A", "A", "A", "B", "B", "B"],
+            "time": [1, 2, 3, 2, 4, 6],  # A: interval=1, B: interval=2
+            "value": [10, 20, 30, 40, 50, 60],
+        }
+    )
+    df = nw.from_native(df)
 
+    # Should pass without equidistant requirement
+    validate_temporal_ordering(df, "time", id_col="id")
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-@pytest.mark.parametrize("time_unit", ["us", "ms", "ns"])
-def test_convert_datetime_column_to_numeric_conversion(backend: str, time_unit: str) -> None:
-    """Test convert_datetime_column_to_numeric converts datetime column to numeric format correctly."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=10, num_features=2, time_col_numeric=False)
+    # Should pass with equidistant requirement (each group has regular intervals)
+    validate_temporal_ordering(df, "time", id_col="id", enforce_equidistant_sampling=True)
 
-    result = convert_datetime_column_to_numeric(df, time_col="time", time_unit=time_unit)
+    # Test irregular intervals within a group
+    df_irregular = pd.DataFrame(
+        {
+            "id": ["A", "A", "A"],
+            "time": [1, 2, 4],  # Irregular spacing
+            "value": [10, 20, 30],
+        }
+    )
+    df_irregular = nw.from_native(df_irregular)
 
-    if backend == "pyarrow":
-        assert "time" in result.schema.names, f"Expected 'time' column in PyArrow schema for backend '{backend}'."
-    else:
-        assert "time" in result.columns, f"Expected 'time' column in DataFrame for backend '{backend}'."
+    with pytest.raises(TimeColumnError, match="Irregular time sampling found"):
+        validate_temporal_ordering(df_irregular, "time", enforce_equidistant_sampling=True)
 
 
-def test_convert_datetime_column_to_numeric_invalid_time_col():
-    """Test convert_datetime_column_to_numeric raises ValueError for invalid or missing time column."""
+def test_sort_dataframe_time_missing():
+    """Test sort_dataframe_time with missing time column."""
     df = pd.DataFrame({"value": [1, 2, 3]})
-    with pytest.raises(ValueError, match="Column 'time' does not exist"):
-        convert_datetime_column_to_numeric(df, time_col="time")
+    df = nw.from_native(df)
+    with pytest.raises(ValueError):
+        sort_dataframe_time(df, "time")
 
 
-def test_convert_datetime_column_to_numeric_null_handling():
-    """Test convert_datetime_column_to_numeric raises error for null or NaN values in the time column."""
-    df = pd.DataFrame({"time": [pd.NaT, pd.Timestamp("2023-01-02"), pd.Timestamp("2023-01-03")]})
-    with pytest.raises(ValueError, match="Null or NaN values detected"):
-        convert_datetime_column_to_numeric(df, time_col="time")
+def test_sort_dataframe_time_invalid_type():
+    """Test sort_dataframe_time with invalid time column type."""
+    df = pd.DataFrame({"time": ["a", "b", "c"]})  # String type (invalid)
+    df = nw.from_native(df)
+    with pytest.raises(ValueError, match="Column 'time' is neither numeric nor datetime"):
+        sort_dataframe_time(df, "time")
 
 
-def test_convert_datetime_column_to_numeric_invalid_column_type():
-    """Test convert_datetime_column_to_numeric raises ValueError for unsupported column types."""
-    df = pd.DataFrame({"time": ["a", "b", "c"]})
-    with pytest.raises(ValueError, match="must specifically be a datetime type"):
-        convert_datetime_column_to_numeric(df, time_col="time")
+# ========================= Tests for convert_datetime_column_to_timestamp =========================
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_convert_datetime_column_to_numeric_lazy_evaluation(backend: str) -> None:
-    """Test convert_datetime_column_to_numeric handles lazy evaluation correctly."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=10, num_features=1, time_col_numeric=False)
-    result = convert_datetime_column_to_numeric(df, time_col="time")
-    assert len(result) == 10, "Expected the same number of rows after conversion."
-
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_convert_datetime_column_to_timestamp_microseconds(backend):
+    """Test convert_datetime_column_to_timestamp with microsecond precision."""
+    df = generate_synthetic_time_series(
+        backend=backend, num_samples=3, num_features=1, time_col_numeric=False, drop_time=False
+    )
+    result = convert_datetime_column_to_timestamp(df, "time", "us")
+    # Check if dtype indicates float for microseconds
+    dtype_str = str(result["time"].dtype).lower()
+    assert any(float_type in dtype_str for float_type in ["float", "f64"])
 
-def test_convert_datetime_column_to_numeric_empty_dataframe():
-    """Test convert_datetime_column_to_numeric handles empty DataFrame gracefully."""
-    df = pd.DataFrame({"time": []})
-    result = convert_datetime_column_to_numeric(df, time_col="time")
-    assert result.equals(df), "Empty DataFrame should remain unchanged."
 
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_convert_datetime_column_to_timestamp_nanoseconds(backend):
+    """Test convert_datetime_column_to_timestamp with nanosecond precision."""
+    df = generate_synthetic_time_series(
+        backend=backend, num_samples=3, num_features=1, time_col_numeric=False, drop_time=False
+    )
+    result = convert_datetime_column_to_timestamp(df, "time", "ns")
+    # Check if dtype indicates int for nanoseconds
+    dtype_str = str(result["time"].dtype).lower()
+    assert "int" in dtype_str
 
-def test_convert_datetime_column_to_numeric_invalid_numeric_column():
-    """Test convert_datetime_column_to_numeric skips numeric columns gracefully."""
-    df = pd.DataFrame({"time": [1, 2, 3]})
-    result = convert_datetime_column_to_numeric(df, time_col="time")
-    assert result.equals(df), "Numeric column should remain unchanged."
 
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_convert_datetime_column_to_timestamp_already_numeric(backend):
+    """Test convert_datetime_column_to_timestamp with already numeric column."""
+    df = generate_synthetic_time_series(
+        backend=backend, num_samples=3, num_features=1, time_col_numeric=True, drop_time=False
+    )
+    result = convert_datetime_column_to_timestamp(df, "time")
+    # Both result and df should be identical since numeric columns are returned as-is
+    assert (result["time"] == df["time"]).all()
 
-def test_convert_datetime_column_to_numeric_edge_cases():
-    """Test convert_datetime_column_to_numeric with edge-case inputs."""
-    # Single-row DataFrame
-    single_row_df = pd.DataFrame({"time": [pd.Timestamp("2023-01-01")]})
-    result = convert_datetime_column_to_numeric(single_row_df, time_col="time")
-    assert len(result) == 1, "Expected single-row DataFrame as output."
 
-    # No rows but valid schema
-    no_row_df = pd.DataFrame({"time": pd.Series([], dtype="datetime64[ns]")})
-    with pytest.raises(ValueError, match="Null or NaN values detected"):
-        convert_datetime_column_to_numeric(no_row_df, time_col="time")
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_convert_datetime_column_to_timestamp_missing_column(backend):
+    """Test convert_datetime_column_to_timestamp with missing column."""
+    df = generate_synthetic_time_series(backend=backend, num_samples=3, num_features=1, drop_time=True)
+    with pytest.raises(ValueError, match="Column 'time' does not exist in the DataFrame."):
+        convert_datetime_column_to_timestamp(df, "time")
 
 
-def test_convert_datetime_column_to_numeric_unsupported_dataframe() -> None:
-    """Test convert_datetime_column_to_numeric raises UnsupportedBackendError for unsupported DataFrame."""
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_convert_datetime_column_to_timestamp_empty_df(backend):
+    """Test convert_datetime_column_to_timestamp with empty DataFrame."""
+    df = generate_synthetic_time_series(backend=backend, num_samples=0, num_features=1, drop_time=False)
+    with pytest.raises(ValueError, match="Empty DataFrame provided"):
+        convert_datetime_column_to_timestamp(df, "time")
 
-    class UnsupportedDataFrame:
-        pass
 
-    df = UnsupportedDataFrame()
-    with pytest.raises(UnsupportedBackendError, match=f"Unsupported DataFrame type: {type(df).__name__}"):
-        convert_datetime_column_to_numeric(df, time_col="time")
+@pytest.mark.parametrize("backend", TEST_BACKENDS)
+def test_convert_datetime_column_to_timestamp_invalid_type(backend):
+    """Test convert_datetime_column_to_timestamp with invalid column type."""
+    # Create DataFrame with invalid column type
+    df = pd.DataFrame({"time": ["a", "b", "c"]})  # String type (invalid)
+    df = nw.from_native(df)  # Convert to Narwhals format
+    with pytest.raises(TimeColumnError, match="Column 'time' must be datetime type to convert"):
+        convert_datetime_column_to_timestamp(df, "time")
diff --git a/test/unit/core/test_exceptions.py b/test/unit/core/test_exceptions.py
index c12f825..3e87ad0 100644
--- a/test/unit/core/test_exceptions.py
+++ b/test/unit/core/test_exceptions.py
@@ -15,23 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
 """TemporalScope/test/unit/test_core_exceptions.py
 
 This module contains unit tests for the custom exceptions and warnings defined
@@ -41,23 +24,19 @@ raised correctly and the warnings are issued in the appropriate scenarios.
 
 import warnings
 
+import narwhals as nw
+import pandas as pd
 import pytest
 
 from temporalscope.core.exceptions import (
+    DataFrameValidationError,
     ModeValidationError,
     TargetColumnWarning,
     TimeColumnError,
     TimeFrameError,
-    UnsupportedBackendError,
 )
 
 
-def test_unsupported_backend_error():
-    """Test that UnsupportedBackendError is raised with the correct message."""
-    with pytest.raises(UnsupportedBackendError, match="Unsupported backend"):
-        raise UnsupportedBackendError("invalid_backend")
-
-
 def test_time_frame_error_inheritance():
     """Test that TimeFrameError is the base class for other exceptions."""
     with pytest.raises(TimeFrameError):
@@ -66,17 +45,61 @@ def test_time_frame_error_inheritance():
 
 def test_time_column_error():
     """Test that TimeColumnError is raised for time column validation errors."""
+    # Create test DataFrame
+    df = pd.DataFrame({"time": ["invalid", "data"]})
+    df = nw.from_native(df)
+
     with pytest.raises(TimeColumnError, match="time column"):
-        raise TimeColumnError("Error with the time column")
+        try:
+            # Attempt to cast non-numeric, non-datetime column
+            df.select([nw.col("time").cast(nw.Float64())])
+            df.select([nw.col("time").cast(nw.Datetime())])
+        except Exception:
+            raise TimeColumnError("Invalid time column type")
 
 
 def test_mode_validation_error():
     """Test that ModeValidationError is raised for invalid modes."""
+    # Test with default message
+    error = ModeValidationError("invalid_mode")
+    assert error.mode == "invalid_mode"
+    assert error.message == "Invalid mode specified: invalid_mode"
+
+    # Test with custom message
+    error = ModeValidationError("invalid_mode", "Custom error message")
+    assert error.mode == "invalid_mode"
+    assert error.message == "Custom error message: invalid_mode"
+
+    # Test raising the error
     with pytest.raises(ModeValidationError, match="Invalid mode"):
         raise ModeValidationError("invalid_mode")
 
 
 def test_target_column_warning():
     """Test that TargetColumnWarning is issued for potential target column issues."""
+    # Create test DataFrame with sequence data
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=2), "target": [[0.3, 0.4], [0.5, 0.6]]})
+    df = nw.from_native(df)
+    mode = "multi_target"
+
     with pytest.warns(TargetColumnWarning, match="sequential data"):
-        warnings.warn("`target_col` appears to contain sequential data", TargetColumnWarning)
+        # Check if target column contains sequence data using schema type
+        if mode == "multi_target" and isinstance(df["target"][0], list):
+            warnings.warn(
+                "`target_col` appears to contain sequential data. Ensure it is transformed appropriately for multi_target mode.",
+                TargetColumnWarning,
+            )
+
+
+def test_dataframe_validation_error():
+    """Test that DataFrameValidationError is raised for DataFrame validation issues."""
+    # Create test DataFrame with non-numeric feature
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=2), "feature": ["invalid", "data"]})
+    df = nw.from_native(df)
+
+    with pytest.raises(DataFrameValidationError, match="numeric columns"):
+        try:
+            # Attempt to validate numeric columns
+            df = df.select([nw.col("feature").cast(nw.Float64()).alias("feature")])
+        except Exception as e:
+            raise DataFrameValidationError(f"Failed to validate numeric columns: {str(e)}")
diff --git a/test/unit/core/test_temporal_data_loader.py b/test/unit/core/test_temporal_data_loader.py
index d774c0f..b094923 100644
--- a/test/unit/core/test_temporal_data_loader.py
+++ b/test/unit/core/test_temporal_data_loader.py
@@ -15,614 +15,351 @@
 # specific language governing permissions and limitations
 # under the License.
 
-"""Unit tests for TemporalScope's TimeFrame class.
+"""Unit tests for TimeFrame class.
 
-Testing Strategy:
-1. Use synthetic_data_generator through pytest fixtures for systematic testing
-2. Backend-agnostic operations using Narwhals API
-3. Consistent validation across all backends
+Tests the TimeFrame class which acts as a delegator to core_utils functions
+for temporal data loading and validation. Tests use synthetic data generation
+to ensure backend-agnostic operations work correctly.
 """
 
-from unittest import mock
-
 import pandas as pd
 import pytest
 
-from temporalscope.core.core_utils import (
-    MODE_MULTI_TARGET,
-    MODE_SINGLE_TARGET,
-    TEMPORALSCOPE_CORE_BACKEND_TYPES,
-    SupportedTemporalDataFrame,
-    convert_to_backend,
-)
-from temporalscope.core.exceptions import UnsupportedBackendError
-from temporalscope.core.temporal_data_loader import TimeFrame
+from temporalscope.core.core_utils import TEST_BACKENDS, TimeColumnError
+from temporalscope.core.temporal_data_loader import MODE_MULTI_TARGET, MODE_SINGLE_TARGET, TimeFrame
 from temporalscope.datasets.synthetic_data_generator import generate_synthetic_time_series
 
-# Constants
-VALID_BACKENDS = list(TEMPORALSCOPE_CORE_BACKEND_TYPES.keys())
-
-# ========================= Fixtures =========================
-
-
-@pytest.fixture(params=VALID_BACKENDS)
-def df_basic(request) -> SupportedTemporalDataFrame:
-    """Basic DataFrame with clean data."""
-    return generate_synthetic_time_series(backend=request.param, num_samples=5, num_features=3)
-
-
-@pytest.fixture(params=VALID_BACKENDS)
-def df_nulls(request) -> SupportedTemporalDataFrame:
-    """DataFrame with null values."""
-    return generate_synthetic_time_series(backend=request.param, num_samples=5, num_features=3, with_nulls=True)
-
-
-@pytest.fixture(params=VALID_BACKENDS)
-def df_nans(request) -> SupportedTemporalDataFrame:
-    """DataFrame with NaN values."""
-    return generate_synthetic_time_series(backend=request.param, num_samples=5, num_features=3, with_nans=True)
 
+@pytest.fixture(params=TEST_BACKENDS)
+def synthetic_df(request):
+    """Generate synthetic DataFrame for each backend."""
+    return generate_synthetic_time_series(backend=request.param, num_samples=10, num_features=2, drop_time=False)
 
-@pytest.fixture(params=VALID_BACKENDS)
-def df_datetime(request) -> SupportedTemporalDataFrame:
-    """DataFrame with datetime time column."""
-    return generate_synthetic_time_series(backend=request.param, num_samples=5, num_features=3, time_col_numeric=False)
 
+# ========================= Tests for TimeFrame initialization =========================
 
-# ========================= Initialization Tests =========================
 
-
-def test_init_basic(df_basic):
-    """Test basic initialization."""
-    tf = TimeFrame(df_basic, time_col="time", target_col="target")
+def test_timeframe_init_basic(synthetic_df):
+    """Test basic TimeFrame initialization."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1")
     assert tf.mode == MODE_SINGLE_TARGET
     assert tf.ascending is True
+    # No need to check DataFrame type since we use native pandas
 
 
-def test_init_invalid_backend(df_basic):
-    """Test initialization with an invalid backend."""
-    with pytest.raises(UnsupportedBackendError, match="Unsupported backend"):
-        TimeFrame(df_basic, time_col="time", target_col="target", dataframe_backend="invalid")
-
-
-def test_init_invalid_time_col(df_basic):
-    """Test initialization with invalid `time_col` type."""
-    with pytest.raises(TypeError, match="`time_col` must be a string"):
-        TimeFrame(df_basic, time_col=123, target_col="target")
-
-
-def test_init_invalid_target_col(df_basic):
-    """Test initialization with invalid `target_col` type."""
-    with pytest.raises(TypeError, match="`target_col` must be a string"):
-        TimeFrame(df_basic, time_col="time", target_col=456)
-
-
-def test_init_invalid_backend_type(df_basic):
-    """Test initialization with invalid `dataframe_backend` type."""
-    with pytest.raises(UnsupportedBackendError, match="Unsupported backend: Backend '123' is not supported"):
-        TimeFrame(df_basic, time_col="time", target_col="target", dataframe_backend=123)
-
-
-def test_init_invalid_sort_type(df_basic):
-    """Test initialization with invalid `sort` type."""
-    with pytest.raises(TypeError, match="`sort` must be a boolean"):
-        TimeFrame(df_basic, time_col="time", target_col="target", sort="true")
-
-
-def test_init_invalid_ascending_type(df_basic):
-    """Test initialization with invalid `ascending` type."""
-    with pytest.raises(TypeError, match="`ascending` must be a boolean"):
-        TimeFrame(df_basic, time_col="time", target_col="target", ascending="false")
-
-
-def test_init_invalid_verbose_type(df_basic):
-    """Test initialization with invalid `verbose` type."""
-    with pytest.raises(TypeError, match="`verbose` must be a boolean"):
-        TimeFrame(df_basic, time_col="time", target_col="target", verbose="yes")
-
-
-def test_init_invalid_time_col_conversion(df_basic):
-    """Test initialization with invalid `time_col_conversion` value."""
-    with pytest.raises(ValueError, match="Invalid `time_col_conversion` value 'invalid_value'"):
-        TimeFrame(
-            df_basic,
-            time_col="time",
-            target_col="target",
-            time_col_conversion="invalid_value",
-        )
-
-
-def test_init_invalid_mode(df_basic):
-    """Test initialization with unsupported `mode`."""
-    with pytest.raises(ValueError, match="Invalid mode 'unsupported_mode'. Must be one of"):
-        TimeFrame(df_basic, time_col="time", target_col="target", mode="unsupported_mode")
-
-
-def test_init_unsupported_backend_error():
-    """Test that UnsupportedBackendError is raised for unsupported DataFrame types."""
-    invalid_df = {"unsupported": "type"}  # Example of an invalid DataFrame type
-    with pytest.raises(UnsupportedBackendError, match="Unsupported DataFrame type"):
-        TimeFrame(invalid_df, time_col="time", target_col="target")
-
-
-def test_init_verbose_logging(df_basic, capsys):
-    """Test that verbose mode prints any output."""
-    # Initialize TimeFrame with verbose=True
-    TimeFrame(df_basic, time_col="time", target_col="target", verbose=True)
-
-    # Capture the printed output
-    captured = capsys.readouterr()
-
-    # Check that something was printed
-    assert captured.out.strip(), "Expected output was not printed in verbose mode."
-
-    # ========================= Invalid mode =========================
-
-
-def test_verbose_logging_tf_validate_dataframe(df_basic, capsys):
-    """Test that verbose mode outputs a message during DataFrame validation."""
-    # Initialize TimeFrame with verbose=True
-    tf = TimeFrame(df_basic, time_col="time", target_col="target", verbose=True)
-
-    # Call validate_dataframe to trigger the verbose message
-    tf.validate_dataframe(tf.df)
-
-    # Capture the printed output
-    captured = capsys.readouterr()
+def test_timeframe_init_invalid_time_col():
+    """Test TimeFrame initialization with invalid time column."""
+    df = pd.DataFrame({"wrong_col": [1, 2, 3], "target": [10, 20, 30]})
+    with pytest.raises(ValueError, match="Column 'time' does not exist"):
+        TimeFrame(df, time_col="time", target_col="target")
 
-    # Check if any output was produced
-    assert len(captured.out.strip()) > 0, "Expected verbose output, but got none."
 
+def test_timeframe_init_invalid_target_col(synthetic_df):
+    """Test TimeFrame initialization with invalid target column."""
+    with pytest.raises(ValueError, match="Column 'wrong_target' does not exist"):
+        TimeFrame(synthetic_df, time_col="time", target_col="wrong_target")
 
-# ========================= Invalid mode =========================
 
+def test_timeframe_init_empty_df():
+    """Test TimeFrame initialization with empty DataFrame."""
+    df = pd.DataFrame()
+    with pytest.raises(ValueError, match="Empty DataFrame provided"):
+        TimeFrame(df, time_col="time", target_col="target")
 
-def test_mode_warning(df_basic):
-    """Test that multi-target mode is accepted."""
-    # Test with multi-target mode
-    tf = TimeFrame(df_basic, time_col="time", target_col="target", mode=MODE_MULTI_TARGET)
-    assert tf.mode == MODE_MULTI_TARGET
 
-    # Test invalid mode raises error
+def test_timeframe_init_invalid_mode(synthetic_df):
+    """Test TimeFrame initialization with invalid mode."""
     with pytest.raises(ValueError, match="Invalid mode"):
-        TimeFrame(df_basic, time_col="time", target_col="target", mode="custom_mode")
-
+        TimeFrame(synthetic_df, time_col="time", target_col="feature_1", mode="invalid_mode")
 
-# ========================= Columns =========================
-
-
-def test_init_missing_columns(df_basic):
-    """Test initialization with missing columns."""
-    with pytest.raises(ValueError, match="Column 'nonexistent' does not exist"):
-        TimeFrame(df_basic, time_col="nonexistent", target_col="target")
-
-
-def test_rejects_nulls(df_nulls):
-    """Test rejection of null values."""
-    with pytest.raises(ValueError, match="Missing values detected"):
-        TimeFrame(df_nulls, time_col="time", target_col="target")
 
+def test_timeframe_init_multi_target(synthetic_df):
+    """Test TimeFrame initialization in multi-target mode."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1", mode=MODE_MULTI_TARGET)
+    assert tf.mode == MODE_MULTI_TARGET
 
-def test_rejects_nans(df_nans):
-    """Test rejection of NaN values."""
-    with pytest.raises(ValueError, match="Missing values detected"):
-        TimeFrame(df_nans, time_col="time", target_col="target")
 
+def test_timeframe_init_descending_sort(synthetic_df):
+    """Test TimeFrame initialization with descending sort."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1", ascending=False)
+    assert tf.ascending is False
 
-@pytest.mark.parametrize("ascending", [True, False])
-def test_sort_dataframe_time_delegation(df_basic, ascending):
-    """Test that TimeFrame.sort_dataframe_time delegates to the core utility."""
-    # Initialize TimeFrame with the basic DataFrame
-    tf = TimeFrame(df_basic, time_col="time", target_col="target")
 
-    # Mock the method within the class
-    with mock.patch("temporalscope.core.temporal_data_loader.TimeFrame.sort_dataframe_time") as mock_sort:
-        # Call the method under test
-        tf.sort_dataframe_time(tf.df, ascending=ascending)
+def test_timeframe_init_no_sort(synthetic_df):
+    """Test TimeFrame initialization without sorting."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1", sort=False)
+    # Original order should be preserved
+    # Convert both to pandas for comparison if needed
+    df1 = tf.df.to_pandas() if hasattr(tf.df, "to_pandas") else tf.df
+    df2 = synthetic_df.to_pandas() if hasattr(synthetic_df, "to_pandas") else synthetic_df
+    pd.testing.assert_frame_equal(df1, df2)
 
-        # Ensure the mocked method was called with the expected arguments
-        mock_sort.assert_called_once_with(tf.df, ascending=ascending)
 
+def test_timeframe_init_invalid_parameter_types(synthetic_df):
+    """Test TimeFrame initialization with invalid parameter types."""
+    # Test invalid time_col type
+    with pytest.raises(TypeError, match="`time_col` must be a string"):
+        TimeFrame(synthetic_df, time_col=123, target_col="feature_1")
 
-@pytest.mark.parametrize("target_backend", ["pandas", "polars"])
-def test_backend_conversion(df_basic, target_backend):
-    """Test backend conversion."""
-    tf = TimeFrame(df_basic, time_col="time", target_col="target", dataframe_backend=target_backend)
-    assert tf.backend == target_backend
+    # Test invalid target_col type
+    with pytest.raises(TypeError, match="`target_col` must be a string"):
+        TimeFrame(synthetic_df, time_col="time", target_col=123)
 
+    # Test invalid sort type
+    with pytest.raises(TypeError, match="`sort` must be a boolean"):
+        TimeFrame(synthetic_df, time_col="time", target_col="feature_1", sort="yes")
 
-# ========================= Update DataFrame =========================
+    # Test invalid ascending type
+    with pytest.raises(TypeError, match="`ascending` must be a boolean"):
+        TimeFrame(synthetic_df, time_col="time", target_col="feature_1", ascending="yes")
 
+    # Test invalid verbose type
+    with pytest.raises(TypeError, match="`verbose` must be a boolean"):
+        TimeFrame(synthetic_df, time_col="time", target_col="feature_1", verbose="yes")
 
-def convert_to_pandas(df):
-    """Convert a DataFrame to Pandas, handling backend-specific cases."""
-    if hasattr(df, "to_pandas"):
-        return df.to_pandas()  # Works for modin, dask, etc.
-    elif isinstance(df, pd.DataFrame):  # Native Pandas DataFrame
-        return df
-    elif hasattr(df, "compute"):  # For Dask DataFrames
-        return df.compute()
-    elif hasattr(df, "to_dataframe"):  # For PyArrow tables
-        return df.to_dataframe()
-    else:
-        raise ValueError(f"Unsupported DataFrame type: {type(df)}")
+    # Test invalid id_col type
+    with pytest.raises(TypeError, match="`id_col` must be a string or None"):
+        TimeFrame(synthetic_df, time_col="time", target_col="feature_1", id_col=123)
 
 
-@pytest.mark.parametrize("backend", ["pandas", "modin", "pyarrow", "polars", "dask"])
-def test_update_dataframe_basic(backend):
-    """Test basic functionality of update_dataframe across backends."""
-    # Skip the test if the backend is "modin"
-    if backend == "modin":
-        pytest.skip("Skipping modin backend due to compatibility issues.")
+# ========================= Tests for TimeFrame validation =========================
 
-    # Step 1: Generate the initial DataFrame
-    df_basic = generate_synthetic_time_series(backend=backend, num_samples=5, num_features=3)
 
-    # Step 2: Initialize TimeFrame
-    tf = TimeFrame(df_basic, time_col="time", target_col="target")
+def test_timeframe_validate_numeric_time(synthetic_df):
+    """Test TimeFrame validation with numeric time column."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1")
+    tf.validate_dataframe(synthetic_df)  # Should pass
 
-    # Step 3: Generate new data to update
-    new_data = generate_synthetic_time_series(backend=backend, num_samples=5, num_features=3)
 
-    # Step 4: Call update_dataframe
-    tf.update_dataframe(new_data)
+def test_timeframe_validate_datetime_time():
+    """Test TimeFrame validation with datetime time column."""
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3), "target": [1, 2, 3]})
+    tf = TimeFrame(df, time_col="time", target_col="target")
+    tf.validate_dataframe(df)  # Should pass
 
-    # Step 5: Convert both DataFrames to Pandas for comparison
-    df_tf_updated = convert_to_pandas(tf._df)
-    df_new_data = convert_to_pandas(new_data)
 
-    # Step 6: Use Pandas' equality check
-    pd.testing.assert_frame_equal(
-        df_tf_updated.reset_index(drop=True),  # Drop index for consistent comparison
-        df_new_data.reset_index(drop=True),
-        check_dtype=False,  # Allow dtype differences for cross-backend compatibility
+def test_timeframe_validate_non_numeric_features():
+    """Test TimeFrame validation with non-numeric feature columns."""
+    df = pd.DataFrame(
+        {
+            "time": [1, 2, 3],
+            "target": [1, 2, 3],
+            "category": ["A", "B", "C"],  # Non-numeric column
+        }
     )
+    with pytest.raises(ValueError, match="must be numeric"):
+        TimeFrame(df, time_col="time", target_col="target")
 
 
-# ========================= Metadata Property =========================
-
-
-def test_metadata_property(df_basic):
-    """Test the functionality of the metadata property."""
-    # Initialize TimeFrame
-    tf = TimeFrame(df_basic, time_col="time", target_col="target")
-
-    # Ensure metadata is initially an empty dictionary
-    assert isinstance(tf.metadata, dict), "Metadata should be initialized as a dictionary."
-    assert not tf.metadata, "Metadata should be empty upon initialization."
-
-    # Add custom metadata
-    tf.metadata["description"] = "Test dataset for sales forecasting"
-    tf.metadata["model_details"] = {"type": "LSTM", "framework": "TensorFlow"}
-
-    # Access and verify custom metadata
-    assert (
-        tf.metadata["description"] == "Test dataset for sales forecasting"
-    ), "Metadata description does not match expected value."
-    assert tf.metadata["model_details"]["type"] == "LSTM", "Metadata model type does not match expected value."
-    assert (
-        tf.metadata["model_details"]["framework"] == "TensorFlow"
-    ), "Metadata framework does not match expected value."
-
-    # Ensure metadata updates persist
-    tf.metadata["extra_info"] = {"notes": "This is a unit test"}
-    assert "extra_info" in tf.metadata, "Metadata did not persist after adding new key."
-    assert (
-        tf.metadata["extra_info"]["notes"] == "This is a unit test"
-    ), "Metadata extra_info notes value does not match expected."
-
-
-# ========================= Direct Parameter Validation Tests =========================
+# ========================= Tests for TimeFrame sorting =========================
 
 
-def test_validate_parameters_direct_invalid_time_col(df_basic):
-    """Test _validate_parameters directly with invalid time_col."""
-    # Attempt to create an invalid TimeFrame instance with non-string time_col
-    with pytest.raises(TypeError, match="`time_col` must be a string"):
-        TimeFrame(  # No need to assign to `tf`
-            df_basic,
-            time_col=123,  # Invalid
-            target_col="target",
-            dataframe_backend=None,
-            sort=True,
-            ascending=True,
-            time_col_conversion=None,
-            enforce_temporal_uniqueness=False,
-            id_col=None,
-            verbose=False,
-        )
-
-
-def test_validate_parameters_direct_invalid_target_col(df_basic):
-    """Test invalid target_col during TimeFrame initialization."""
-    with pytest.raises(TypeError, match="`target_col` must be a string"):
-        TimeFrame(
-            df_basic,
-            time_col="time",
-            target_col=456,  # Invalid target_col
-        )
-
-
-def test_validate_parameters_direct_invalid_backend(df_basic):
-    """Test _validate_parameters directly with invalid dataframe_backend."""
-    tf = TimeFrame(df_basic, time_col="time", target_col="target")  # Create instance first
-    tf._backend = 123  # Directly set invalid backend
-    with pytest.raises(TypeError, match="`dataframe_backend` must be a string or None"):
-        tf._validate_parameters()
+def test_timeframe_sort_numeric_time():
+    """Test TimeFrame sorting with numeric time column."""
+    df = pd.DataFrame({"time": [3, 1, 2], "target": [30, 10, 20]})
+    tf = TimeFrame(df, time_col="time", target_col="target")
+    assert list(tf.df["time"]) == [1, 2, 3]
+    assert list(tf.df["target"]) == [10, 20, 30]
 
 
-def test_validate_parameters_direct_invalid_sort(df_basic):
-    """Test _validate_parameters directly with invalid sort."""
-    tf = TimeFrame(df_basic, time_col="time", target_col="target")  # Create instance first
-    tf._sort = "true"  # Assign invalid value to the instance variable
-    with pytest.raises(TypeError, match="`sort` must be a boolean"):
-        tf._validate_parameters()
+def test_timeframe_sort_datetime_time():
+    """Test TimeFrame sorting with datetime time column."""
+    dates = pd.date_range("2023-01-01", periods=3)
+    df = pd.DataFrame({"time": [dates[2], dates[0], dates[1]], "target": [30, 10, 20]})
+    tf = TimeFrame(df, time_col="time", target_col="target")
+    # Convert to pandas if needed and reset index names for comparison
+    time_series = tf.df["time"].to_pandas() if hasattr(tf.df["time"], "to_pandas") else tf.df["time"]
+    sorted_dates = pd.DatetimeIndex(time_series)
+    sorted_dates.name = None  # Reset name for comparison
+    pd.testing.assert_index_equal(sorted_dates, dates)
+    assert list(tf.df["target"]) == [10, 20, 30]
 
 
-def test_validate_parameters_direct_invalid_ascending(df_basic):
-    """Test _validate_parameters directly with invalid ascending."""
-    tf = TimeFrame(df_basic, time_col="time", target_col="target")  # Create instance first
-    tf._ascending = "false"  # Assign invalid value to the instance variable
-    with pytest.raises(TypeError, match="`ascending` must be a boolean"):
-        tf._validate_parameters()
+def test_timeframe_sort_descending():
+    """Test TimeFrame descending sort."""
+    df = pd.DataFrame({"time": [1, 2, 3], "target": [10, 20, 30]})
+    tf = TimeFrame(df, time_col="time", target_col="target", ascending=False)
+    assert list(tf.df["time"]) == [3, 2, 1]
+    assert list(tf.df["target"]) == [30, 20, 10]
 
 
-def test_validate_parameters_direct_invalid_verbose(df_basic):
-    """Test _validate_parameters directly with invalid verbose."""
-    tf = TimeFrame(df_basic, time_col="time", target_col="target")  # Create instance first
-    tf._verbose = "yes"  # Assign invalid value to the instance variable
-    with pytest.raises(TypeError, match="`verbose` must be a boolean"):
-        tf._validate_parameters()
+# ========================= Tests for TimeFrame conversion =========================
 
 
-def test_validate_parameters_direct_invalid_id_col(df_basic):
-    """Test _validate_parameters directly with invalid id_col."""
-    tf = TimeFrame(df_basic, time_col="time", target_col="target")  # Create instance first
-    tf._id_col = 123  # Assign invalid value to the instance variable
-    with pytest.raises(TypeError, match="`id_col` must be a string or None"):
-        tf._validate_parameters()
+def test_timeframe_convert_to_numeric():
+    """Test TimeFrame time column conversion to numeric."""
+    df = pd.DataFrame({"time": pd.date_range("2023-01-01", periods=3), "target": [1, 2, 3]})
+    tf = TimeFrame(df, time_col="time", target_col="target", time_col_conversion="numeric")
+    assert pd.api.types.is_float_dtype(tf.df["time"].dtype)
 
 
-# ========================= Setup Tests =========================
+def test_timeframe_convert_to_datetime():
+    """Test TimeFrame time column conversion to datetime."""
+    df = pd.DataFrame({"time": [1672531200, 1672617600, 1672704000], "target": [1, 2, 3]})
+    tf = TimeFrame(df, time_col="time", target_col="target", time_col_conversion="datetime")
+    assert pd.api.types.is_datetime64_dtype(tf.df["time"].dtype)
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_setup_enforce_temporal_uniquenessing_invalid_time_column(backend):
-    """Test setup with invalid time column when enforce_temporal_uniqueness is True."""
-    df = pd.DataFrame({"time": ["a", "b", "c"], "target": [1, 2, 3]})
-    df = convert_to_backend(df, backend)
-    with pytest.raises(ValueError, match=r".*neither numeric nor datetime.*"):
-        TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=True)
+def test_timeframe_convert_invalid_type(synthetic_df):
+    """Test TimeFrame conversion with invalid type."""
+    with pytest.raises(ValueError, match="Invalid `time_col_conversion`"):
+        TimeFrame(synthetic_df, time_col="time", target_col="feature_1", time_col_conversion="invalid")
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_setup_validate_columns_missing_columns(backend):
-    """Test setup with missing columns required for validation."""
-    df = pd.DataFrame({"other": [1, 2, 3]})
-    df = convert_to_backend(df, backend)
+# ========================= Tests for TimeFrame temporal validation =========================
 
-    # Test missing time column
-    with pytest.raises(ValueError, match=r"Column .* does not exist"):
-        TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=True)
 
-    # Test missing id column when enforcing temporal uniqueness
-    df_with_time = pd.DataFrame({"time": [1, 2, 3], "target": [1, 2, 3]})
-    df_with_time = convert_to_backend(df_with_time, backend)
-    with pytest.raises(ValueError, match=r"Column .* does not exist"):
-        TimeFrame(
-            df_with_time,
-            time_col="time",
-            target_col="target",
-            enforce_temporal_uniqueness=True,
-            id_col="id",  # Missing id_col
-        )
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_setup_enforce_temporal_uniquenessing_empty_dataframe(backend):
-    """Test setup with empty DataFrame when enforce_temporal_uniqueness is True."""
-    df = pd.DataFrame(columns=["time", "target"])
-    df = convert_to_backend(df, backend)
-    with pytest.raises(ValueError, match="Empty DataFrame provided."):
+def test_timeframe_temporal_uniqueness():
+    """Test TimeFrame temporal uniqueness validation."""
+    df = pd.DataFrame({"time": [1, 2, 2, 3], "target": [10, 20, 30, 40]})  # Duplicate at t=2
+    with pytest.raises(TimeColumnError, match="Duplicate timestamps found"):
         TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=True)
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_setup_enforce_temporal_uniquenessing_duplicates(backend):
-    """Test setup with duplicate timestamps when enforce_temporal_uniqueness is True."""
+def test_timeframe_temporal_uniqueness_by_group():
+    """Test TimeFrame temporal uniqueness validation within groups."""
     df = pd.DataFrame(
         {
-            "time": [1, 1, 2, 3],  # Duplicate timestamp
+            "id": [1, 1, 2, 2],
+            "time": [1, 2, 1, 2],  # Same times ok for different ids
             "target": [10, 20, 30, 40],
         }
     )
-    df = convert_to_backend(df, backend)
-    # Update regex to match 'None' or empty string
-    with pytest.raises(ValueError, match=r"Duplicate timestamps in id_col '.*' column 'time'."):
-        TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=True)
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_setup_enforce_temporal_uniqueness_group_validation(backend):
-    """Test setup with group-based validation ensuring duplicate timestamps raise ValueError."""
-    # Create a DataFrame with duplicates in each group
-    # Group 1 has time=[1,1], Group 2 has time=[2,2]
-    df = pd.DataFrame(
-        {
-            "time": [1, 1, 2, 2],
-            "group": [1, 1, 2, 2],
-            "target": [20, 10, 40, 30],
-        }
-    )
-    df = convert_to_backend(df, backend)
+    tf = TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=True, id_col="id")
+    assert len(tf.df) == 4  # All rows preserved
 
-    # Expecting a ValueError due to duplicate timestamps in 'group' column 'time'
-    with pytest.raises(ValueError, match=r"Duplicate timestamps in id_col 'group' column 'time'."):
-        TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=True, id_col="group")
 
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_setup_enforce_temporal_uniquenessing_valid_data(backend):
-    """Test setup with valid temporal ordering."""
+def test_timeframe_temporal_uniqueness_violation_in_group():
+    """Test TimeFrame temporal uniqueness violation within a group."""
     df = pd.DataFrame(
         {
-            "time": [1, 2, 3, 4],  # Strictly increasing
+            "id": [1, 1, 2, 2],
+            "time": [1, 1, 2, 3],  # Duplicate for id=1
             "target": [10, 20, 30, 40],
         }
     )
-    df = convert_to_backend(df, backend)
-    # Should pass - timestamps are strictly increasing
-    tf = TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=True)
-    assert tf._enforce_temporal_uniqueness is True
+    with pytest.raises(TimeColumnError, match="Duplicate timestamps found within groups"):
+        TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=True, id_col="id")
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_setup_print_statements_numeric_conversion(backend, capsys):
-    """Test that the numeric conversion print statement is triggered."""
-    df = pd.DataFrame(
-        {
-            "time": pd.date_range(start="2023-01-01", periods=3, freq="D"),  # Valid datetime
-            "target": [10, 20, 30],
-        }
-    )
-    df = convert_to_backend(df, backend)
+# ========================= Tests for TimeFrame update =========================
 
-    # Initialize TimeFrame with verbose=True to enable printing
-    tf = TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=False, verbose=True)
-    tf.setup(df, time_col_conversion="numeric")  # Trigger numeric conversion
 
-    # Capture printed output
-    captured = capsys.readouterr()
-    expected_message = "Converted column 'time' to numeric (Unix timestamp)."
-    assert expected_message in captured.out
+def test_timeframe_update_dataframe(synthetic_df):
+    """Test TimeFrame DataFrame update."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1")
+    new_df = generate_synthetic_time_series(backend="pandas", num_samples=5, num_features=2, drop_time=False)
+    tf.update_dataframe(new_df)
+    pd.testing.assert_frame_equal(tf.df, new_df)
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_setup_print_statements_datetime_conversion(backend, capsys):
-    """Test that the datetime conversion print statement is triggered."""
-    df = pd.DataFrame(
-        {
-            "time": [1672531200, 1672617600, 1672704000],  # Unix timestamps
-            "target": [10, 20, 30],
-        }
-    )
-    df = convert_to_backend(df, backend)
+def test_timeframe_update_invalid_df(synthetic_df):
+    """Test TimeFrame update with invalid DataFrame."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1")
+    invalid_df = pd.DataFrame({"wrong_col": [1, 2, 3]})
+    with pytest.raises(ValueError, match="Column 'time' does not exist"):
+        tf.update_dataframe(invalid_df)
 
-    # Initialize TimeFrame with verbose=True to enable printing
-    tf = TimeFrame(df, time_col="time", target_col="target", enforce_temporal_uniqueness=False, verbose=True)
-    tf.setup(df, time_col_conversion="datetime")  # Trigger datetime conversion
 
-    # Capture printed output
-    captured = capsys.readouterr()
-    expected_message = "Converted column 'time' to datetime."
-    assert expected_message in captured.out
+def test_timeframe_update_empty_df(synthetic_df):
+    """Test TimeFrame update with empty DataFrame."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1")
+    empty_df = pd.DataFrame()
+    with pytest.raises(ValueError, match="Empty DataFrame provided"):
+        tf.update_dataframe(empty_df)
 
 
-# ========================= Valid Numeric Conversion =========================
+def test_timeframe_update_with_verbose(synthetic_df):
+    """Test TimeFrame update with verbose mode."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1", verbose=True)
+    new_df = generate_synthetic_time_series(backend="pandas", num_samples=5, num_features=2, drop_time=False)
+    tf.update_dataframe(new_df)  # Should print success message
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_time_col_conversion_to_numeric_valid_datetime(backend):
-    """Test numeric conversion of a valid datetime column."""
-    # Step 1: Create the initial DataFrame in Pandas
-    df = pd.DataFrame(
-        {
-            "time": pd.date_range(start="2023-01-01", periods=3, freq="D"),
-            "target": [10, 20, 30],
-        }
-    )
+# ========================= Tests for TimeFrame metadata =========================
 
-    # Step 2: Convert to the specified backend
-    df = convert_to_backend(df, backend)
 
-    # Step 3: Initialize the TimeFrame
-    tf = TimeFrame(df, time_col="time", target_col="target")
+def test_timeframe_metadata(synthetic_df):
+    """Test TimeFrame metadata storage."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1")
+    tf.metadata["description"] = "Test dataset"
+    tf.metadata["model_details"] = {"type": "LSTM"}
+    assert tf.metadata["description"] == "Test dataset"
+    assert tf.metadata["model_details"]["type"] == "LSTM"
 
-    # Step 4: Perform the setup with conversion to numeric
-    converted_df = tf.setup(df, time_col_conversion="numeric")
 
-    # Step 5: Convert back to Pandas for validation
-    pandas_df = convert_to_backend(converted_df, backend="pandas")
+def test_timeframe_metadata_empty(synthetic_df):
+    """Test TimeFrame metadata starts empty."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1")
+    assert tf.metadata == {}
 
-    # Step 6: Validate that the time column is numeric
-    assert pd.api.types.is_numeric_dtype(pandas_df["time"]), "Time column was not converted to numeric."
 
+def test_timeframe_setup_empty_df():
+    """Test TimeFrame setup with empty DataFrame."""
+    df = pd.DataFrame()
+    tf = TimeFrame(pd.DataFrame({"time": [1], "target": [1]}), time_col="time", target_col="target")
+    with pytest.raises(ValueError, match="Empty DataFrame provided"):
+        tf.setup(df)
 
-# ========================= Invalid Numeric Conversion =========================
 
+def test_timeframe_setup_missing_columns():
+    """Test TimeFrame setup with missing required columns."""
+    df = pd.DataFrame({"wrong_col": [1, 2, 3]})
+    tf = TimeFrame(pd.DataFrame({"time": [1], "target": [1]}), time_col="time", target_col="target")
+    with pytest.raises(ValueError, match="Column 'time' does not exist"):
+        tf.setup(df)
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_time_col_conversion_to_numeric_invalid_non_datetime(backend):
-    """Test numeric conversion skips already numeric columns gracefully."""
-    # Step 1: Create a DataFrame with numeric columns
-    df = pd.DataFrame(
-        {
-            "time": [1672531200, 1672617600, 1672704000],  # Already numeric
-            "target": [10, 20, 30],
-        }
-    )
-
-    # Step 2: Convert the DataFrame to the specified backend
-    df = convert_to_backend(df, backend)
 
-    # Step 3: Initialize TimeFrame and run the setup
+def test_timeframe_metadata_access():
+    """Test TimeFrame metadata access and modification."""
+    df = pd.DataFrame({"time": [1, 2, 3], "target": [10, 20, 30]})
     tf = TimeFrame(df, time_col="time", target_col="target")
-    converted_df = tf.df
 
-    # Step 4: Convert back to Pandas for comparison using `convert_to_backend`
-    converted_df_native = convert_to_backend(converted_df, "pandas")
-    original_df_native = convert_to_backend(df, "pandas")
+    # Test metadata access
+    assert isinstance(tf.metadata, dict)
 
-    # Step 5: Explicitly handle Modin backend if necessary
-    if backend == "modin":
-        converted_df_native = converted_df_native._to_pandas()
-        original_df_native = original_df_native._to_pandas()
+    # Test metadata modification
+    tf.metadata["test_key"] = "test_value"
+    assert tf.metadata["test_key"] == "test_value"
 
-    # Step 6: Assert that the numeric column remains unchanged
-    pd.testing.assert_frame_equal(converted_df_native, original_df_native)
+    # Test nested metadata
+    tf.metadata["nested"] = {"key": "value"}
+    assert tf.metadata["nested"]["key"] == "value"
 
 
-# ========================= Already Numeric Input =========================
+def test_timeframe_validate_dataframe_empty():
+    """Test validate_dataframe with empty DataFrame."""
+    df = pd.DataFrame()
+    tf = TimeFrame(pd.DataFrame({"time": [1], "target": [1]}), time_col="time", target_col="target")
+    with pytest.raises(ValueError, match="Empty DataFrame provided"):
+        tf.validate_dataframe(df)
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_time_col_conversion_to_numeric_already_numeric(backend):
-    """Test behavior with a column that is already numeric."""
-    # Step 1: Create the DataFrame in Pandas
-    df = pd.DataFrame(
-        {
-            "time": [1672531200, 1672617600, 1672704000],  # Unix timestamps
-            "target": [10, 20, 30],
-        }
-    )
-    # Step 2: Convert to backend
-    df = convert_to_backend(df, backend)
-    # Step 3: Validate that no change is made
-    tf = TimeFrame(df, time_col="time", target_col="target")
-    converted_df = tf.setup(df, time_col_conversion="numeric")
-    # Step 4: Convert back to Pandas for validation
-    converted_df = convert_to_backend(converted_df, backend="pandas")
-    # Step 5: Validate that the time column is still numeric
-    assert pd.api.types.is_numeric_dtype(converted_df["time"]), "Time column was not numeric."
+def test_timeframe_validate_dataframe_missing_time_col():
+    """Test validate_dataframe with missing time column."""
+    df = pd.DataFrame({"wrong_col": [1, 2, 3], "target": [10, 20, 30]})
+    tf = TimeFrame(pd.DataFrame({"time": [1], "target": [1]}), time_col="time", target_col="target")
+    with pytest.raises(ValueError, match="Column 'time' does not exist"):
+        tf.validate_dataframe(df)
 
 
-# ========================= Mixed Valid and Invalid Values =========================
+def test_timeframe_validate_dataframe_missing_target_col():
+    """Test validate_dataframe with missing target column."""
+    df = pd.DataFrame({"time": [1, 2, 3], "wrong_col": [10, 20, 30]})
+    tf = TimeFrame(pd.DataFrame({"time": [1], "target": [1]}), time_col="time", target_col="target")
+    with pytest.raises(ValueError, match="Column 'target' does not exist"):
+        tf.validate_dataframe(df)
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_time_col_conversion_to_numeric_mixed_invalid(backend):
-    """Test numeric conversion with a column containing mixed valid and invalid datetime values."""
-    # Step 1: Create a DataFrame with mixed valid and invalid datetime values
-    df = pd.DataFrame(
-        {
-            "time": ["2023-01-01", "2023-01-02", "invalid_date"],  # Mixed valid and invalid
-            "target": [10, 20, 30],
-        }
-    )
+def test_timeframe_validate_dataframe_with_verbose():
+    """Test validate_dataframe with verbose mode."""
+    df = pd.DataFrame({"time": [1, 2, 3], "target": [10, 20, 30]})
+    tf = TimeFrame(df, time_col="time", target_col="target", verbose=True)
+    tf.validate_dataframe(df)  # Should print validation message
 
-    # Step 2: Convert the DataFrame to the specified backend
-    df = convert_to_backend(df, backend)
 
-    # Step 3: Assert that initializing TimeFrame raises a ValueError
-    with pytest.raises(ValueError, match="Column 'time' is neither numeric nor datetime."):
-        TimeFrame(df, time_col="time", target_col="target")
+def test_timeframe_setup_with_verbose(synthetic_df):
+    """Test TimeFrame setup with verbose mode."""
+    tf = TimeFrame(synthetic_df, time_col="time", target_col="feature_1", verbose=True)
+    # Test both conversion and temporal validation messages
+    tf.setup(synthetic_df, time_col_conversion="numeric", enforce_temporal_uniqueness=True)
diff --git a/test/unit/datasets/test_dataset_validator.py b/test/unit/datasets/test_dataset_validator.py
index 127c22a..6287639 100644
--- a/test/unit/datasets/test_dataset_validator.py
+++ b/test/unit/datasets/test_dataset_validator.py
@@ -50,8 +50,9 @@ from typing import Any, Callable, Dict, Generator, Tuple
 import narwhals as nw
 import pandas as pd
 import pytest
+from narwhals.typing import FrameT
 
-from temporalscope.core.core_utils import SupportedTemporalDataFrame, get_temporalscope_backends
+from temporalscope.core.core_utils import NARWHALS_BACKENDS
 from temporalscope.datasets.dataset_validator import DatasetValidator, ValidationResult
 from temporalscope.datasets.synthetic_data_generator import generate_synthetic_time_series
 
@@ -59,7 +60,7 @@ from temporalscope.datasets.synthetic_data_generator import generate_synthetic_t
 DataConfigType = Callable[..., Dict[str, Any]]
 
 
-@pytest.fixture(params=get_temporalscope_backends())
+@pytest.fixture(params=NARWHALS_BACKENDS)
 def backend(request) -> str:
     """Fixture providing all supported backends for testing."""
     return request.param
@@ -84,7 +85,7 @@ def data_config(backend: str) -> DataConfigType:
 
 
 @pytest.fixture
-def sample_df(data_config: DataConfigType) -> Generator[Tuple[SupportedTemporalDataFrame, str], None, None]:
+def sample_df(data_config: DataConfigType) -> Generator[Tuple[FrameT, str], None, None]:
     """Generate sample DataFrame for testing."""
     config = data_config()
     # Remove time_col and target_col from config
@@ -165,9 +166,7 @@ def test_sample_size_validation(validator: DatasetValidator, data_config: DataCo
     assert result.details["num_samples"] == 100
 
 
-def test_feature_count_validation(
-    validator: DatasetValidator, sample_df: Tuple[SupportedTemporalDataFrame, str]
-) -> None:
+def test_feature_count_validation(validator: DatasetValidator, sample_df: Tuple[FrameT, str]) -> None:
     """Test feature count validation."""
     df, _ = sample_df
     result = validator._check_feature_count(df)
@@ -176,9 +175,7 @@ def test_feature_count_validation(
     assert result.details["num_features"] == 4
 
 
-def test_feature_ratio_validation(
-    validator: DatasetValidator, sample_df: Tuple[SupportedTemporalDataFrame, str]
-) -> None:
+def test_feature_ratio_validation(validator: DatasetValidator, sample_df: Tuple[FrameT, str]) -> None:
     """Test feature-to-sample ratio validation."""
     df, _ = sample_df
     result = validator._check_feature_ratio(df)
@@ -187,9 +184,7 @@ def test_feature_ratio_validation(
     assert result.details["ratio"] == 4 / 100  # 4 features, 100 samples
 
 
-def test_numerical_uniqueness_validation(
-    validator: DatasetValidator, sample_df: Tuple[SupportedTemporalDataFrame, str]
-) -> None:
+def test_numerical_uniqueness_validation(validator: DatasetValidator, sample_df: Tuple[FrameT, str]) -> None:
     """Test numerical feature uniqueness validation."""
     df, _ = sample_df
     result = validator._check_feature_variability(df)
@@ -197,9 +192,7 @@ def test_numerical_uniqueness_validation(
     assert "numeric_feature" in result.details
 
 
-def test_class_balance_validation(
-    validator: DatasetValidator, sample_df: Tuple[SupportedTemporalDataFrame, str]
-) -> None:
+def test_class_balance_validation(validator: DatasetValidator, sample_df: Tuple[FrameT, str]) -> None:
     """Test class balance validation."""
     df, target_col = sample_df
     result = validator._check_class_balance(df, target_col)
@@ -207,7 +200,7 @@ def test_class_balance_validation(
     assert_validation_details(result, ["class_counts"])
 
 
-def test_validate_all(validator: DatasetValidator, sample_df: Tuple[SupportedTemporalDataFrame, str]) -> None:
+def test_validate_all(validator: DatasetValidator, sample_df: Tuple[FrameT, str]) -> None:
     """Test running all validation checks."""
     df, target_col = sample_df
     results = validator.transform(df, target_col=target_col)
@@ -215,7 +208,7 @@ def test_validate_all(validator: DatasetValidator, sample_df: Tuple[SupportedTem
     assert set(results.keys()) >= {"sample_size", "feature_count", "feature_ratio"}
 
 
-def test_selective_validation(validator: DatasetValidator, sample_df: Tuple[SupportedTemporalDataFrame, str]) -> None:
+def test_selective_validation(validator: DatasetValidator, sample_df: Tuple[FrameT, str]) -> None:
     """Test running only selected validation checks."""
     df, _ = sample_df
     validator.checks_to_run = {"sample_size", "feature_count"}
@@ -238,9 +231,7 @@ def test_invalid_checks() -> None:
         DatasetValidator(time_col="time", target_col="target", checks_to_run=["invalid_check"])
 
 
-def test_print_report(
-    validator: DatasetValidator, sample_df: Tuple[SupportedTemporalDataFrame, str], capsys: Any
-) -> None:
+def test_print_report(validator: DatasetValidator, sample_df: Tuple[FrameT, str], capsys: Any) -> None:
     """Test validation report printing."""
     df, target_col = sample_df
     results = validator.transform(df, target_col=target_col)
@@ -269,13 +260,11 @@ def test_edge_cases(validator: DatasetValidator) -> None:
     assert not results["feature_variability"].passed
 
 
-def test_fit_transform_equivalence(
-    validator: DatasetValidator, sample_df: Tuple[SupportedTemporalDataFrame, str]
-) -> None:
+def test_fit_transform_equivalence(validator: DatasetValidator, sample_df: Tuple[FrameT, str]) -> None:
     """Test fit_transform equivalence to separate fit and transform."""
     df, target_col = sample_df
 
-    # Compare fit_trƒansform vs separate fit/transform
+    # Compare fit_transform vs separate fit/transform
     result1 = validator.fit_transform(df, target_col=target_col)
     result2 = validator.fit(df).transform(df, target_col=target_col)
 
@@ -287,14 +276,8 @@ def test_fit_transform_equivalence(
         assert result1[key].details == result2[key].details
 
 
-def test_narwhals_integration(sample_df: Tuple[SupportedTemporalDataFrame, str]) -> None:
-    """Test integration with Narwhals operations.
-
-    This test verifies that:
-    1. We can use narwhals operations (select, col) on the input DataFrame
-    2. The validator can process a narwhalified DataFrame
-    3. The validator returns results in the expected format
-    """
+def test_narwhals_integration(sample_df: Tuple[FrameT, str]) -> None:
+    """Test integration with Narwhals operations."""
     df, target_col = sample_df
 
     # Test with narwhalified DataFrame
@@ -348,13 +331,7 @@ def test_validation_error_paths(validator: DatasetValidator, data_config: DataCo
 
 
 def test_validation_edge_cases_extended(validator: DatasetValidator, data_config: DataConfigType) -> None:
-    """Test additional edge cases in validation.
-
-    Tests edge cases around:
-    - Feature variability with null/missing values
-    - Class balance validation with/without target column
-    - Feature validation with invalid column types
-    """
+    """Test additional edge cases in validation."""
     # Test feature variability with null values
     config = data_config(with_nulls=True)
     null_df = generate_synthetic_time_series(**config)
@@ -379,23 +356,13 @@ def test_validation_edge_cases_extended(validator: DatasetValidator, data_config
     invalid_df = generate_synthetic_time_series(**config)
     results = validator.transform(invalid_df)
     assert not results["feature_count"].passed
-    # Update assertion to match actual message
     assert "dataset has 0 features" in results["feature_count"].message.lower()
 
 
-def test_validation_report_formatting_andf_null_handling(
+def test_validation_report_formatting_and_null_handling(
     validator: DatasetValidator, data_config: DataConfigType, capsys: Any
 ) -> None:
-    """Test validation report formatting with null/empty values and edge cases.
-
-    Tests:
-    1. Report formatting with empty details
-    2. Report formatting with null messages
-    3. Report formatting with completely null results
-    4. Report formatting with failed checks and no details
-    5. Class balance handling with null/empty target columns
-    6. Feature variability handling with insufficient unique values
-    """
+    """Test validation report formatting with null/empty values and edge cases."""
     # Create validator with stricter thresholds to force failures
     strict_validator = DatasetValidator(
         time_col="time",
@@ -441,28 +408,18 @@ def test_validation_report_formatting_andf_null_handling(
 
 
 def test_invalid_dataframe_validation(validator: DatasetValidator) -> None:
-    """Test validation of invalid DataFrames through fit().
-
-    Tests that fit() properly validates DataFrame type before transform()
-    can use narwhals operations on it.
-    """
+    """Test validation of invalid DataFrames through fit()."""
 
     class InvalidDF:
         def __init__(self):
             self.columns = ["col1"]
 
-    with pytest.raises(TypeError, match="must be a valid temporal DataFrame"):
+    with pytest.raises(TypeError, match="Input must be convertible to a Narwhals DataFrame"):
         validator.fit(InvalidDF())
 
 
 def test_feature_ratio_edge_cases(validator: DatasetValidator, data_config: DataConfigType) -> None:
-    """Test feature ratio validation edge cases.
-
-    Tests:
-    - Zero samples
-    - No features
-    - High feature-to-sample ratio
-    """
+    """Test feature ratio validation edge cases."""
     # Test with zero samples
     zero_samples_df = generate_synthetic_time_series(**data_config(num_samples=1))
     results = validator.transform(zero_samples_df)
@@ -508,13 +465,7 @@ def test_validation_report_formatting(validator: DatasetValidator) -> None:
 
 
 def test_check_method_error_paths(validator: DatasetValidator, data_config: DataConfigType) -> None:
-    """Test error paths in check methods.
-
-    Tests:
-    1. Empty DataFrame handling in _check_sample_size
-    2. PyArrow scalar conversion in _check_sample_size
-    3. collect() and to_list() paths in _check_feature_count
-    """
+    """Test error paths in check methods."""
 
     # Test empty DataFrame in _check_sample_size
     @nw.narwhalify
@@ -554,7 +505,7 @@ def test_ensure_narwhals_df_error_path(validator: DatasetValidator) -> None:
             self.columns = ["col1"]
             self.select = None  # Has select but it's None
 
-    with pytest.raises(TypeError, match="must be a valid temporal DataFrame"):
+    with pytest.raises(TypeError, match="Input must be convertible to a Narwhals DataFrame"):
         validator.fit(InvalidDF())
 
 
@@ -627,12 +578,7 @@ def test_check_class_balance_error_paths(validator: DatasetValidator) -> None:
 
 
 def test_transform_warning_paths(validator: DatasetValidator) -> None:
-    """Test transform() warning paths.
-
-    Tests both warning paths in transform():
-    1. Non-critical failures (UserWarning)
-    2. Critical failures (RuntimeWarning)
-    """
+    """Test transform() warning paths."""
 
     @nw.narwhalify
     def create_df():
@@ -650,15 +596,7 @@ def test_transform_warning_paths(validator: DatasetValidator) -> None:
 
 
 def test_comprehensive_error_paths(validator: DatasetValidator) -> None:
-    """Test all error paths systematically.
-
-    Creates a DataFrame that triggers multiple validation failures to test:
-    1. No features case
-    2. Sample size issues
-    3. Invalid ratios
-    4. Missing target
-    5. Warning paths
-    """
+    """Test all error paths systematically."""
 
     @nw.narwhalify
     def create_problematic_df():
@@ -690,17 +628,7 @@ def test_comprehensive_error_paths(validator: DatasetValidator) -> None:
 
 
 def test_dataframe_method_failures(validator: DatasetValidator) -> None:
-    """Test validation when DataFrame methods fail.
-
-    Tests handling of DataFrame method failures:
-    1. select() returns None
-    2. collect() returns None
-    3. to_list() returns None
-    4. as_py() returns None
-
-    These represent error conditions that could occur when DataFrame methods
-    fail to return expected values.
-    """
+    """Test validation when DataFrame methods fail."""
 
     class SelectFailureDF:
         def __init__(self):
diff --git a/test/unit/datasets/test_datasets.py b/test/unit/datasets/test_datasets.py
index 27b3a2d..9b1ae09 100644
--- a/test/unit/datasets/test_datasets.py
+++ b/test/unit/datasets/test_datasets.py
@@ -15,25 +15,15 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.
-# See the NOTICE file for additional information regarding copyright ownership.
-# The ASF licenses this file under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License. You may obtain a copy of the License at
-# http://www.apache.org/licenses/LICENSE-2.0
-
 import pandas as pd
 import pytest
 
-from temporalscope.core.core_utils import (
-    TEMPORALSCOPE_CORE_BACKEND_TYPES,
-    is_valid_temporal_backend,
-)
-from temporalscope.core.exceptions import UnsupportedBackendError
+from temporalscope.core.core_utils import TEST_BACKENDS
 from temporalscope.datasets.datasets import DatasetLoader
 
 # Constants
 DEFAULT_DATASET_NAME = "macrodata"
-VALID_BACKENDS = list(TEMPORALSCOPE_CORE_BACKEND_TYPES.keys())
+VALID_BACKENDS = TEST_BACKENDS
 INVALID_BACKEND = "unsupported_backend"
 
 
@@ -45,7 +35,7 @@ def dataset_loader():
 
 @pytest.fixture(params=VALID_BACKENDS)
 def backend(request):
-    """Parametrized fixture for all supported backends in TEMPORALSCOPE_CORE_BACKEND_TYPES."""
+    """Parametrized fixture for all supported backends."""
     return request.param
 
 
@@ -80,14 +70,12 @@ def test_load_macrodata_none_data(mocker):
 # ========================= Backend Validation Tests =========================
 @pytest.mark.parametrize("backend", VALID_BACKENDS)
 def test_valid_backends_with_load_data(dataset_loader, backend):
-    """Test loading data and converting to each backend specified in TEMPORALSCOPE_CORE_BACKEND_TYPES."""
-    is_valid_temporal_backend(backend)  # Ensure the backend is supported
+    """Test loading data and converting to each backend."""
     data = dataset_loader.load_data(backend=backend)
-    expected_type = TEMPORALSCOPE_CORE_BACKEND_TYPES[backend]
-    assert isinstance(data, expected_type), f"Data is not of type {expected_type} for backend '{backend}'"
+    assert data is not None, f"Data is None for backend '{backend}'"
 
 
 def test_invalid_backend_raises_error(dataset_loader):
-    """Test that using an invalid backend raises an UnsupportedBackendError."""
-    with pytest.raises(UnsupportedBackendError, match="is not supported"):
+    """Test that using an invalid backend raises a ValueError."""
+    with pytest.raises(ValueError, match="is not supported"):
         dataset_loader.load_data(backend=INVALID_BACKEND)
diff --git a/test/unit/datasets/test_synthetic_data_generator.py b/test/unit/datasets/test_synthetic_data_generator.py
index cf320dd..d72d49d 100644
--- a/test/unit/datasets/test_synthetic_data_generator.py
+++ b/test/unit/datasets/test_synthetic_data_generator.py
@@ -15,70 +15,57 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.
-# See the NOTICE file for additional information regarding copyright ownership.
-# The ASF licenses this file under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+"""TemporalScope/test/unit/datasets/test_synthetic_data_generator.py
+
+This module contains unit tests for the synthetic data generator, ensuring it works
+correctly with Narwhals and different DataFrame backends.
+"""
 
 from datetime import datetime
-from typing import Any, TypeVar
+from typing import Any
 
+import narwhals as nw
 import numpy as np
 import pandas as pd
-import polars as pl
-import pyarrow as pa
 import pytest
+from narwhals.typing import FrameT
+from narwhals.utils import Implementation
 
-from temporalscope.core.core_utils import MODE_SINGLE_TARGET, TEMPORALSCOPE_CORE_BACKEND_TYPES
-from temporalscope.core.exceptions import UnsupportedBackendError
+from temporalscope.core.core_utils import MODE_SINGLE_TARGET
 from temporalscope.datasets.synthetic_data_generator import generate_synthetic_time_series
 
 # Constants
-VALID_BACKENDS = [backend for backend in TEMPORALSCOPE_CORE_BACKEND_TYPES if backend != "dask"]
+VALID_BACKENDS = [impl.name.lower() for impl in Implementation]
 INVALID_BACKEND = "unsupported_backend"
 
-# Type variables for better type handling
-T = TypeVar("T")
 
+@nw.narwhalify(eager_only=True)
+def get_shape(df: FrameT) -> tuple[int, int]:
+    """Get shape of DataFrame using Narwhals operations."""
+    # For empty DataFrames, use len() directly
+    if len(df.columns) == 0:
+        return (0, 0)
 
-def is_dask_df(obj: Any) -> bool:
-    """Check if object is a Dask DataFrame."""
-    # Check both the module name and string representation
-    return type(obj).__module__.startswith("dask.") or isinstance(obj, object) and "Dask DataFrame" in str(obj)
+    # For non-empty DataFrames, use len() for row count
+    row_count = len(df.select([nw.col(df.columns[0])]))
+    col_count = len(df.columns)
 
+    return (row_count, col_count)
 
-def get_shape(df: Any) -> tuple[int, int]:
-    """Get shape of DataFrame, handling different backends including Dask."""
-    if is_dask_df(df):
-        # Compute the shape components separately for Dask
-        nrows = int(df.shape[0].compute())  # type: ignore
-        ncols = df.shape[1]  # This is already an int
-        return (nrows, ncols)
-    return df.shape
 
+@nw.narwhalify(eager_only=True)
+def get_row_value(df: FrameT, col_name: str, row_idx: int = 0) -> Any:
+    """Get a value from a DataFrame using Narwhals operations."""
+    # Get the first value from the specified column at the specified row
+    value = df.select([nw.col(col_name).alias("value")]).item(row=row_idx, column=0)
 
-def get_row_value(df: Any, col_name: str, row_idx: int = 0) -> Any:
-    """Safely get a value from a DataFrame, handling different backends."""
-    if is_dask_df(df):
-        df = df.compute()  # type: ignore
+    return value
 
-    if isinstance(df, pl.DataFrame):
-        return df.select(pl.col(col_name)).row(row_idx)[0]
-    elif isinstance(df, pa.Table):
-        return df.column(col_name)[row_idx].as_py()  # type: ignore
-    else:
-        return df.iloc[row_idx][col_name]
 
-
-def get_column_names(df: Any) -> set[str]:
-    """Get column names from DataFrame, handling different backends."""
-    if isinstance(df, pa.Table):
-        return set(df.schema.names)  # type: ignore
-    elif is_dask_df(df):
-        return set(df.columns)
-    else:
-        return set(df.columns)
+@nw.narwhalify(eager_only=True)
+def get_column_names(df: FrameT) -> set[str]:
+    """Get column names from DataFrame using Narwhals operations."""
+    return set(df.columns)
 
 
 # ========================= Basic Data Generation Tests =========================
@@ -90,7 +77,7 @@ def get_column_names(df: Any) -> set[str]:
 def test_generate_synthetic_time_series_basic(
     backend: str, num_samples: int, num_features: int, with_nulls: bool, with_nans: bool
 ) -> None:
-    """Test basic functionality of `generate_synthetic_time_series` across backends and configurations."""
+    """Test basic functionality of generate_synthetic_time_series across backends."""
     df = generate_synthetic_time_series(
         backend=backend,
         num_samples=num_samples,
@@ -100,7 +87,7 @@ def test_generate_synthetic_time_series_basic(
         mode=MODE_SINGLE_TARGET,
     )
 
-    # Get shape and compute for Dask
+    # Get shape
     shape = get_shape(df)
 
     # Validate number of samples and features
@@ -108,41 +95,13 @@ def test_generate_synthetic_time_series_basic(
         assert shape[0] == 0, f"Expected empty DataFrame, got {shape[0]} rows"
     else:
         assert shape[0] == num_samples, f"Expected {num_samples} rows, got {shape[0]}"
-
-        # For Dask, compute to finalize DataFrame creation
-        if is_dask_df(df):
-            df = df.compute()  # type: ignore
-
-        # Check feature columns based on backend
-        feature_columns: list[str] = []
-        if isinstance(df, pa.Table):
-            feature_columns = [str(name) for name in df.schema.names]  # type: ignore
-            feature_columns = [name for name in feature_columns if "feature_" in name]
-        else:
-            feature_columns = [str(col) for col in df.columns]
-            feature_columns = [col for col in feature_columns if "feature_" in col]
-
-        assert (
-            len(feature_columns) == num_features
-        ), f"Expected {num_features} feature columns, got {len(feature_columns)}"
-
-    # Verify DataFrame backend type
-    expected_type: Any = TEMPORALSCOPE_CORE_BACKEND_TYPES[backend]
-    if is_dask_df(df):
-        assert type(df).__module__.startswith("dask."), "Expected Dask DataFrame"
-    else:
-        assert isinstance(df, expected_type), f"Expected {expected_type} for backend '{backend}'"  # type: ignore[arg-type]
+        feature_columns = [col for col in df.columns if "feature_" in col]
+        assert len(feature_columns) == num_features, f"Expected {num_features} feature columns"
 
     # Test target column type for single-step mode
     if num_samples > 0:
         target_val = get_row_value(df, "target")
-        if isinstance(df, pa.Table):
-            target_type = df.schema.field("target").type  # type: ignore
-            assert pa.types.is_floating(target_type), "Expected scalar target value in PyArrow"
-        elif isinstance(df, pl.DataFrame):
-            assert isinstance(target_val, (float, int)), "Expected scalar target value in Polars"
-        else:
-            assert np.isscalar(target_val), "Expected scalar target value in Pandas-based backend"
+        assert isinstance(target_val, (float, np.floating)), "Expected numeric target value"
 
 
 # ========================= Time Column Generation Tests =========================
@@ -151,7 +110,7 @@ def test_generate_synthetic_time_series_basic(
 @pytest.mark.parametrize("backend", VALID_BACKENDS)
 @pytest.mark.parametrize("time_col_numeric", [True, False])
 def test_time_column_generation(backend: str, time_col_numeric: bool) -> None:
-    """Test the `time` column generation as numeric or timestamp based on `time_col_numeric`."""
+    """Test time column generation as numeric or timestamp."""
     df = generate_synthetic_time_series(
         backend=backend,
         num_samples=100,
@@ -163,44 +122,59 @@ def test_time_column_generation(backend: str, time_col_numeric: bool) -> None:
     # Get first time value
     time_val = get_row_value(df, "time")
 
-    # Check time column based on type
+    # Check time column type
     if time_col_numeric:
-        if isinstance(df, pl.DataFrame):
-            assert isinstance(time_val, (float, int)), "Expected Polars numeric column"
-        elif isinstance(df, pa.Table):
-            time_type = df.schema.field("time").type  # type: ignore
-            assert pa.types.is_floating(time_type), "Expected PyArrow float column"
-        else:
-            assert isinstance(time_val, (np.float64, float)), "Expected numeric column"
-    elif isinstance(df, pl.DataFrame):
-        assert isinstance(time_val, datetime), "Expected Polars datetime column"
-    elif isinstance(df, pa.Table):
-        time_type = df.schema.field("time").type  # type: ignore
-        assert isinstance(time_type, pa.TimestampType), "Expected PyArrow timestamp column"
+        assert isinstance(time_val, (float, np.floating)), "Expected numeric time column"
     else:
-        assert isinstance(time_val, (pd.Timestamp, datetime)), "Expected timestamp column"
+        assert isinstance(time_val, (pd.Timestamp, datetime)), "Expected datetime time column"
 
 
 # ========================= Error Handling Tests =========================
 
 
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_invalid_backend(backend: str) -> None:
+def test_invalid_backend() -> None:
     """Test error handling for unsupported backends."""
-    with pytest.raises(UnsupportedBackendError, match="is not supported"):
-        generate_synthetic_time_series(backend=INVALID_BACKEND, num_samples=100, num_features=5)
+    with pytest.raises(ValueError, match="is not supported by Narwhals"):
+        generate_synthetic_time_series(backend=INVALID_BACKEND)
 
 
 @pytest.mark.parametrize("backend", VALID_BACKENDS)
 def test_unsupported_mode(backend: str) -> None:
-    """Test that an unsupported mode raises the appropriate error."""
+    """Test that an unsupported mode raises ValueError."""
     with pytest.raises(ValueError, match="Unsupported mode: multi_target"):
-        generate_synthetic_time_series(
-            backend=backend,
-            num_samples=100,
-            num_features=5,
-            mode="multi_target",
-        )
+        generate_synthetic_time_series(backend=backend, mode="multi_target")
+
+
+# ========================= Narwhals-Specific Tests =========================
+
+
+def test_narwhals_backend_conversion() -> None:
+    """Test that generated data can be converted between backends."""
+    # Test pandas backend (default path)
+    df_pandas = generate_synthetic_time_series(backend="pandas")
+    assert isinstance(df_pandas, pd.DataFrame)
+
+    # Test non-pandas backend (covers line 149)
+    df_polars = generate_synthetic_time_series(backend="polars")
+    df_polars_native = nw.from_native(df_polars).to_native()  # Convert back to native polars
+    assert "polars" in str(type(df_polars_native)).lower()
+
+    # Verify data consistency across backends
+    assert set(df_polars.columns) == set(df_pandas.columns)
+    assert len(df_polars.columns) == len(df_pandas.columns)
+
+
+def test_narwhals_lazy_evaluation() -> None:
+    """Test lazy evaluation handling."""
+    # Generate data with dask backend (which is inherently lazy)
+    df = generate_synthetic_time_series(backend="dask")
+
+    # Verify we can chain operations without immediate execution
+    result = nw.from_native(df).select([nw.col("target").mean().alias("mean"), nw.col("target").sum().alias("sum")])
+
+    # The result should be a DataFrame but not yet computed
+    assert "mean" in result.columns
+    assert "sum" in result.columns
 
 
 # ========================= Additional Edge Case Tests =========================
@@ -208,210 +182,82 @@ def test_unsupported_mode(backend: str) -> None:
 
 @pytest.mark.parametrize("backend", VALID_BACKENDS)
 def test_generate_synthetic_time_series_empty_data(backend: str) -> None:
-    """Test generating synthetic data with zero samples to verify empty dataset handling."""
-    df = generate_synthetic_time_series(backend=backend, num_samples=0, num_features=3)
+    """Test generating empty datasets."""
+    df = generate_synthetic_time_series(backend=backend, num_samples=0)
     shape = get_shape(df)
     assert shape[0] == 0, f"Expected empty DataFrame, got {shape[0]} rows"
 
 
+@nw.narwhalify(eager_only=True)
+def check_nulls_nans(df: FrameT, col: str) -> int:
+    """Check for nulls/nans in a column using Narwhals operations."""
+    return df.select([nw.col(col).is_null().sum().cast(nw.Int64()).alias("null_count")]).item()
+
+
 @pytest.mark.parametrize("backend", VALID_BACKENDS)
 def test_generate_synthetic_time_series_with_nulls_and_nans(backend: str) -> None:
-    """Test generating synthetic data with both nulls and NaNs introduced to feature columns."""
+    """Test null and NaN handling."""
     df = generate_synthetic_time_series(
         backend=backend, num_samples=100, num_features=5, with_nulls=True, with_nans=True
     )
 
-    # Get all feature values
-    if isinstance(df, pa.Table):
-        feature_field = df.schema.field("feature_1")  # type: ignore
-        assert feature_field is not None, "Expected feature_1 column in PyArrow backend"
-    elif isinstance(df, pl.DataFrame):
-        # Get null count and ensure it's greater than 0
-        has_nulls = df.select(pl.col("feature_1").is_null()).sum().item() > 0  # type: ignore
-        assert has_nulls, "Expected None or NaN in Polars backend"
-    else:
-        # Check any row has null/nan
-        if hasattr(df["feature_1"], "compute"):  # For dask
-            feature_vals = pd.Series(df["feature_1"].compute())
-        else:  # For pandas and modin
-            feature_vals = pd.Series(df["feature_1"])
-        assert feature_vals.isna().any(), "Expected at least one None or NaN value"
-
-
-@pytest.mark.parametrize("backend", VALID_BACKENDS)
-def test_generate_synthetic_time_series_defaults_only_backend(backend: str) -> None:
-    """Test function call with only the backend parameter to confirm default value handling."""
-    df = generate_synthetic_time_series(backend=backend)
-    shape = get_shape(df)
-
-    # Check if the DataFrame matches the default values (100 samples, 3 features)
-    assert shape[0] == 100, f"Expected 100 rows, got {shape[0]}"
-
-    # For Dask, compute to finalize DataFrame creation
-    if is_dask_df(df):
-        df = df.compute()  # type: ignore
-
-    feature_columns: list[str] = []
-    if isinstance(df, pa.Table):
-        feature_columns = [str(name) for name in df.schema.names]  # type: ignore
-        feature_columns = [name for name in feature_columns if "feature_" in name]
-    else:
-        feature_columns = [str(col) for col in df.columns]
-        feature_columns = [col for col in feature_columns if "feature_" in col]
-
-    assert len(feature_columns) == 3, "Expected 3 feature columns"
-
-
-def test_generate_synthetic_time_series_negative_values() -> None:
-    """Test that generating synthetic data with negative `num_samples` or `num_features` raises a ValueError."""
-    with pytest.raises(ValueError, match="`num_samples` and `num_features` must be non-negative."):
-        generate_synthetic_time_series(
-            backend="pandas",
-            num_samples=-10,
-            num_features=5,
-        )
-    with pytest.raises(ValueError, match="`num_samples` and `num_features` must be non-negative."):
-        generate_synthetic_time_series(backend="pandas", num_samples=100, num_features=-3)
+    # Check for nulls/nans in feature columns
+    feature_cols = [col for col in df.columns if col.startswith("feature_")]
+    for col in feature_cols:
+        null_count = check_nulls_nans(df, col)
+        assert null_count > 0, f"Expected nulls/nans in {col}"
 
 
 @pytest.mark.parametrize("backend", VALID_BACKENDS)
 @pytest.mark.parametrize("drop_time", [True, False])
 def test_generate_synthetic_time_series_drop_time(backend: str, drop_time: bool) -> None:
-    """Test that drop_time parameter correctly handles time column inclusion/exclusion."""
-    num_samples = 3
+    """Test time column inclusion/exclusion."""
     df = generate_synthetic_time_series(
         backend=backend,
-        num_samples=num_samples,
-        num_features=2,
-        drop_time=drop_time,
-        time_col_numeric=True,  # Use numeric time for easier verification
-    )
-
-    # Get column names
-    columns = get_column_names(df)
-
-    # Test the dictionary unpacking operation directly
-    time_dict = {"time": np.arange(num_samples, dtype=np.float64)} if not drop_time else {}
-    expected_cols = {
-        **time_dict,  # Test the same unpacking operation as in the implementation
-        "target": "value",  # Placeholder value
-        "feature_1": "value",
-        "feature_2": "value",
-    }.keys()
-
-    # Verify column names match expected set from dictionary unpacking
-    assert columns == set(expected_cols), "Column names don't match expected set from dictionary unpacking"
-
-    # Verify time column values when present
-    if not drop_time:
-        time_val = get_row_value(df, "time")
-        assert isinstance(time_val, (np.float64, float)), "time column should be numeric"
-        assert time_val == 0.0, "time column should start at 0"
-
-    # Verify target column values
-    target_val = get_row_value(df, "target")
-    assert isinstance(target_val, (np.float64, float)), "target column should be numeric"
-    assert 0.0 <= target_val <= 1.0, "target values should be between 0 and 1"
-
-
-def test_generate_synthetic_time_series_all_paths():
-    """Test all code paths in synthetic data generation."""
-    # Test with time column included
-    df1 = generate_synthetic_time_series(
-        backend="pandas",
         num_samples=3,
         num_features=2,
+        drop_time=drop_time,
         time_col_numeric=True,
-        drop_time=False,  # Include time column
-    )
-    assert "time" in df1.columns
-    assert df1["time"].dtype == np.float64
-    assert len(df1.columns) == 4  # time, target, feature_1, feature_2
-
-    # Test with time column dropped
-    df2 = generate_synthetic_time_series(
-        backend="pandas",
-        num_samples=3,
-        num_features=2,
-        drop_time=True,  # Drop time column
     )
-    assert "time" not in df2.columns
-    assert len(df2.columns) == 3  # target, feature_1, feature_2
-
-    # Verify feature columns in both cases
-    for df in [df1, df2]:
-        for i in range(2):
-            col = f"feature_{i+1}"
-            assert col in df.columns
-            assert all(0 <= x <= 1 for x in df[col])
-
-
-def test_generate_synthetic_time_series_feature_loop():
-    """Test feature column generation loop."""
-    # Test with exactly one feature to force loop execution
-    df = generate_synthetic_time_series(backend="pandas", num_samples=1, num_features=1, drop_time=True)
 
-    # Verify feature column exists and has correct values
-    assert "feature_1" in df.columns
-    assert len(df["feature_1"]) == 1
-    assert 0 <= df["feature_1"].iloc[0] <= 1
-
-    # Test with zero features to cover loop initialization
-    df_empty = generate_synthetic_time_series(backend="pandas", num_samples=1, num_features=0, drop_time=True)
-    assert not any(col.startswith("feature_") for col in df_empty.columns)
-
-
-def test_generate_synthetic_time_series_feature_value():
-    """Test feature value generation."""
-    df = generate_synthetic_time_series(backend="pandas", num_samples=1, num_features=1, drop_time=True)
-
-    # Verify feature value is generated correctly
-    feature_val = df["feature_1"].values[0]
-    assert isinstance(feature_val, float)
-    assert 0 <= feature_val <= 1
-
-
-def test_generate_synthetic_time_series_dask():
-    """Test that Dask DataFrames are properly handled."""
-    # Skip if dask is not in TEMPORALSCOPE_CORE_BACKEND_TYPES
-    if "dask" not in TEMPORALSCOPE_CORE_BACKEND_TYPES:
-        pytest.skip("Dask backend not available")
-
-    df = generate_synthetic_time_series(backend="dask", num_samples=1, num_features=1, drop_time=True)
-
-    # Verify it's a Dask DataFrame
-    assert is_dask_df(df), "Expected Dask DataFrame"
-
-    # Get a value to trigger computation
-    value = get_row_value(df, "feature_1")
-    assert isinstance(value, float)
+    # Check time column presence
+    if drop_time:
+        assert "time" not in df.columns
+    else:
+        assert "time" in df.columns
+        time_val = get_row_value(df, "time")
+        assert isinstance(time_val, (float, np.floating))
 
 
 @pytest.mark.parametrize("backend", VALID_BACKENDS)
 def test_generate_synthetic_time_series_percentage_validation(backend: str) -> None:
-    """Test validation of null and nan percentage parameters."""
+    """Test null/nan percentage validation."""
     with pytest.raises(ValueError, match="null_percentage must be between 0.0 and 1.0"):
-        generate_synthetic_time_series(
-            backend=backend, num_samples=100, num_features=5, with_nulls=True, null_percentage=1.5
-        )
+        generate_synthetic_time_series(backend=backend, null_percentage=1.5)
 
     with pytest.raises(ValueError, match="nan_percentage must be between 0.0 and 1.0"):
-        generate_synthetic_time_series(
-            backend=backend, num_samples=100, num_features=5, with_nans=True, nan_percentage=-0.1
-        )
+        generate_synthetic_time_series(backend=backend, nan_percentage=-0.1)
 
 
 @pytest.mark.parametrize("backend", VALID_BACKENDS)
 def test_generate_synthetic_time_series_single_row(backend: str) -> None:
-    """Test handling of nulls and nans with single row datasets."""
-    # Test nulls take precedence
+    """Test single row dataset handling."""
+    # Test with nulls (should take precedence)
     df = generate_synthetic_time_series(backend=backend, num_samples=1, num_features=2, with_nulls=True, with_nans=True)
     feature_val = get_row_value(df, "feature_1")
-    assert pd.isna(feature_val), "Expected null/nan value for single row with both nulls and nans"
+    assert pd.isna(feature_val), "Expected null/nan value"
 
-    # Test nans only
-    df = generate_synthetic_time_series(
+    # Test with nans only (covers line 51)
+    df_nans = generate_synthetic_time_series(
         backend=backend, num_samples=1, num_features=2, with_nulls=False, with_nans=True
     )
-    feature_val = get_row_value(df, "feature_1")
-    assert pd.isna(feature_val), "Expected null/nan value for single row with nans only"
+    feature_val_nan = get_row_value(df_nans, "feature_1")
+    assert pd.isna(feature_val_nan), "Expected nan value"
+
+
+@pytest.mark.parametrize("backend", VALID_BACKENDS)
+@pytest.mark.parametrize("num_samples, num_features", [(-1, 3), (100, -2)])
+def test_generate_synthetic_time_series_negative_values(backend: str, num_samples: int, num_features: int) -> None:
+    """Test that negative values for samples or features raise ValueError."""
+    with pytest.raises(ValueError, match="`num_samples` and `num_features` must be non-negative"):
+        generate_synthetic_time_series(backend=backend, num_samples=num_samples, num_features=num_features)
diff --git a/test/unit/partition/test_padding_functional.py b/test/unit/partition/test_padding_functional.py
index b2e5ad4..fd497f2 100644
--- a/test/unit/partition/test_padding_functional.py
+++ b/test/unit/partition/test_padding_functional.py
@@ -42,11 +42,13 @@ from typing import Any, Callable, Dict
 
 import narwhals as nw
 import pytest
+from narwhals.typing import FrameT
 
 from temporalscope.core.core_utils import (
-    SupportedTemporalDataFrame,
-    get_temporalscope_backends,
-    is_lazy_evaluation,
+    get_narwhals_backends as get_temporalscope_backends,
+)
+from temporalscope.core.core_utils import (
+    is_dataframe_empty as is_lazy_evaluation,
 )
 from temporalscope.datasets.synthetic_data_generator import generate_synthetic_time_series
 from temporalscope.partition.single_target.padding.functional import mean_fill_pad
@@ -79,14 +81,14 @@ def data_config(backend: str) -> Callable[..., Dict[str, Any]]:
 
 
 @pytest.fixture
-def sample_df(data_config: Callable[..., Dict[str, Any]]) -> SupportedTemporalDataFrame:
+def sample_df(data_config: Callable[..., Dict[str, Any]]) -> FrameT:
     """Generate sample DataFrame for testing."""
     config = data_config()
     return generate_synthetic_time_series(**config)
 
 
 @nw.narwhalify
-def assert_padding_length(df: SupportedTemporalDataFrame, target_len: int) -> None:
+def assert_padding_length(df: FrameT, target_len: int) -> None:
     """Verify padding length using Narwhals operations."""
     count_result = df.select([nw.col(df.columns[0]).count().cast(nw.Int64).alias("count")])
 
@@ -104,7 +106,7 @@ def assert_padding_length(df: SupportedTemporalDataFrame, target_len: int) -> No
 
 
 @nw.narwhalify
-def assert_mean_values(df: SupportedTemporalDataFrame, original_df: SupportedTemporalDataFrame) -> None:
+def assert_mean_values(df: FrameT, original_df: FrameT) -> None:
     """Verify mean values in padding rows match original column means."""
     for col in df.columns:
         mean_expr = nw.col(col).mean().cast(nw.Float64).alias("mean")
@@ -126,7 +128,7 @@ def assert_mean_values(df: SupportedTemporalDataFrame, original_df: SupportedTem
         assert abs(original_mean - padded_mean) < 1e-6, f"Mean mismatch for column {col}"
 
 
-def test_mean_fill_pad_basic(sample_df: SupportedTemporalDataFrame) -> None:
+def test_mean_fill_pad_basic(sample_df: FrameT) -> None:
     """Test basic mean-fill padding functionality."""
     df = nw.from_native(sample_df)
     target_len = 5
@@ -162,7 +164,7 @@ def test_mean_fill_pad_nan_data(data_config: Callable[..., Dict[str, Any]]) -> N
         mean_fill_pad(df, target_len=5)
 
 
-def test_mean_fill_pad_invalid_target_len(sample_df: SupportedTemporalDataFrame) -> None:
+def test_mean_fill_pad_invalid_target_len(sample_df: FrameT) -> None:
     """Test invalid target length handling."""
     df = nw.from_native(sample_df)
 
@@ -170,7 +172,7 @@ def test_mean_fill_pad_invalid_target_len(sample_df: SupportedTemporalDataFrame)
         mean_fill_pad(df, target_len=2)
 
 
-def test_mean_fill_pad_invalid_padding(sample_df: SupportedTemporalDataFrame) -> None:
+def test_mean_fill_pad_invalid_padding(sample_df: FrameT) -> None:
     """Test invalid padding direction handling."""
     df = nw.from_native(sample_df)
 
diff --git a/test/unit/target_shifters/test_single_step.py b/test/unit/target_shifters/test_single_step.py
index 7c4ef23..1942c52 100644
--- a/test/unit/target_shifters/test_single_step.py
+++ b/test/unit/target_shifters/test_single_step.py
@@ -51,12 +51,9 @@ import narwhals as nw
 import numpy as np
 import pandas as pd
 import pytest
+from narwhals.typing import FrameT
 
-from temporalscope.core.core_utils import (
-    MODE_SINGLE_TARGET,
-    SupportedTemporalDataFrame,
-    get_temporalscope_backends,
-)
+from temporalscope.core.core_utils import MODE_SINGLE_TARGET, NARWHALS_BACKENDS
 from temporalscope.core.temporal_data_loader import TimeFrame
 from temporalscope.datasets.synthetic_data_generator import generate_synthetic_time_series
 from temporalscope.target_shifters.single_step import SingleStepTargetShifter
@@ -65,7 +62,7 @@ from temporalscope.target_shifters.single_step import SingleStepTargetShifter
 DataConfigType = Callable[..., Dict[str, Any]]
 
 
-@pytest.fixture(params=get_temporalscope_backends())
+@pytest.fixture(params=NARWHALS_BACKENDS)
 def backend(request) -> str:
     """Fixture providing all supported backends for testing."""
     return request.param
@@ -92,7 +89,7 @@ def data_config(backend: str) -> DataConfigType:
 
 
 @pytest.fixture
-def sample_df(data_config: DataConfigType) -> Generator[Tuple[SupportedTemporalDataFrame, str], None, None]:
+def sample_df(data_config: DataConfigType) -> Generator[Tuple[FrameT, str], None, None]:
     """Generate sample DataFrame for testing."""
     config = data_config()
     df = generate_synthetic_time_series(**config)
@@ -100,7 +97,7 @@ def sample_df(data_config: DataConfigType) -> Generator[Tuple[SupportedTemporalD
 
 
 @pytest.fixture
-def sample_timeframe(sample_df: Tuple[SupportedTemporalDataFrame, str]) -> TimeFrame:
+def sample_timeframe(sample_df: Tuple[FrameT, str]) -> TimeFrame:
     """Create TimeFrame instance for testing."""
     df, target_col = sample_df
     return TimeFrame(df=df, time_col="time", target_col=target_col)
@@ -122,7 +119,7 @@ def _get_scalar_value(result, column: str) -> int:
 
 # Assertion Helpers
 @nw.narwhalify
-def assert_shifted_columns(df: SupportedTemporalDataFrame, target_col: str, n_lags: int, drop_target: bool) -> None:
+def assert_shifted_columns(df: FrameT, target_col: str, n_lags: int, drop_target: bool) -> None:
     """Verify shifted columns using Narwhals operations."""
     # Check shifted column exists
     shifted_col = f"{target_col}_shift_{n_lags}"
@@ -136,7 +133,7 @@ def assert_shifted_columns(df: SupportedTemporalDataFrame, target_col: str, n_la
 
 
 @nw.narwhalify
-def assert_row_reduction(df: SupportedTemporalDataFrame, original_df: SupportedTemporalDataFrame, n_lags: int) -> None:
+def assert_row_reduction(df: FrameT, original_df: FrameT, n_lags: int) -> None:
     """Verify row count reduction using Narwhals operations."""
     # Get row counts using Narwhals with proper type casting
     count_expr = nw.col(df.columns[0]).count().cast(nw.Int64).alias("count")
@@ -151,7 +148,7 @@ def assert_row_reduction(df: SupportedTemporalDataFrame, original_df: SupportedT
     ), f"Expected {original_count - n_lags} rows, got {transformed_count}"
 
 
-def test_transform_dataframe(sample_df: Tuple[SupportedTemporalDataFrame, str]) -> None:
+def test_transform_dataframe(sample_df: Tuple[FrameT, str]) -> None:
     """Test transformation of raw DataFrame."""
     df, target_col = sample_df
     shifter = SingleStepTargetShifter(target_col=target_col, n_lags=1)
@@ -176,7 +173,6 @@ def test_transform_timeframe(sample_timeframe: TimeFrame) -> None:
 
     # Verify metadata preservation
     assert transformed_tf.mode == sample_timeframe.mode
-    assert transformed_tf.backend == sample_timeframe.backend
     assert transformed_tf.ascending == sample_timeframe.ascending
 
     # Verify transformation
@@ -184,7 +180,7 @@ def test_transform_timeframe(sample_timeframe: TimeFrame) -> None:
     assert_row_reduction(transformed_tf.df, sample_timeframe.df, 1)
 
 
-def test_verbose_output(sample_df: Tuple[SupportedTemporalDataFrame, str], capfd: Any) -> None:
+def test_verbose_output(sample_df: Tuple[FrameT, str], capfd: Any) -> None:
     """Test verbose mode output."""
     df, target_col = sample_df
     shifter = SingleStepTargetShifter(target_col=target_col, n_lags=1, verbose=True)
@@ -198,7 +194,7 @@ def test_verbose_output(sample_df: Tuple[SupportedTemporalDataFrame, str], capfd
     assert "Rows after:" in captured.out
 
 
-def test_multiple_lags(sample_df: Tuple[SupportedTemporalDataFrame, str]) -> None:
+def test_multiple_lags(sample_df: Tuple[FrameT, str]) -> None:
     """Test transformation with different lag values."""
     df, target_col = sample_df
     n_lags = 3
@@ -231,7 +227,7 @@ def test_target_column_inference(sample_timeframe: TimeFrame) -> None:
     assert shifter.target_col == sample_timeframe._target_col
 
 
-def test_drop_target_option(sample_df: Tuple[SupportedTemporalDataFrame, str]) -> None:
+def test_drop_target_option(sample_df: Tuple[FrameT, str]) -> None:
     """Test drop_target parameter behavior."""
     df, target_col = sample_df
 
@@ -246,7 +242,7 @@ def test_drop_target_option(sample_df: Tuple[SupportedTemporalDataFrame, str]) -
     assert_shifted_columns(transformed_keep, target_col, 1, False)
 
 
-def test_fit_transform_equivalence(sample_df: Tuple[SupportedTemporalDataFrame, str]) -> None:
+def test_fit_transform_equivalence(sample_df: Tuple[FrameT, str]) -> None:
     """Test fit_transform equivalence to separate fit and transform."""
     df, target_col = sample_df
     shifter1 = SingleStepTargetShifter(target_col=target_col)
@@ -256,7 +252,7 @@ def test_fit_transform_equivalence(sample_df: Tuple[SupportedTemporalDataFrame,
     result1 = shifter1.fit_transform(df)
     result2 = shifter2.fit(df).transform(df)
 
-    # Convert both to pandas for comparn
+    # Convert both to pandas for comparison
     if hasattr(result1, "compute"):  # For Dask DataFrame
         result1_pd = result1.compute()
     elif hasattr(result1, "collect"):  # For LazyFrame
@@ -305,7 +301,7 @@ def test_missing_target_column() -> None:
     df = pd.DataFrame({"feature": range(5)})  # DataFrame without target column
     shifter = SingleStepTargetShifter(target_col="target")
 
-    with pytest.raises(ValueError, match="target_col must be set before transform"):
+    with pytest.raises(ValueError, match="Column 'target' does not exist in DataFrame"):
         shifter.fit_transform(df)