semvec/pypi-packages huggingface dataset:
Topsis-Dishav-102217004 1.0.0
A Python package to implement the TOPSIS method.
Requires Python: >=3.6

# Topsis-Dishav-102217004

A Python package to implement the **TOPSIS** (Technique for Order of Preference by Similarity to Ideal Solution) method, a multi-criteria decision-making approach.

---

## Overview

The **TOPSIS** method is used for ranking alternatives based on their closeness to the ideal solution. It is widely used in decision-making scenarios where multiple criteria are considered.

This package simplifies the process of implementing the TOPSIS method by providing a command-line interface and an easy-to-use Python API.

---

## Features

- Handles multi-criteria decision-making problems.
- Works with weighted criteria and both beneficial (`+`) and non-beneficial (`-`) impacts.
- Outputs a ranked list of alternatives with TOPSIS scores.

---

## Installation

Install the package directly from PyPI:

```bash
pip install Topsis-Dishav-102217004
```

## Usage

Command Line Usage

```bash
topsis <InputFile> <Weights> <Impacts> <ResultFile>
```

Python API Usage

```bash
from Topsis_Dishav_102217004 import topsis
```
megaprofiler 1.0.0
megaprofiler is a highly customizable and extensible data profiling library designed to help data scientists and engineers understand their datasets before performing analysis or building models.
Requires Python: >=3.6

[![Downloads](https://img.shields.io/pypi/dm/megaprofiler)](https://pypi.org/project/megaprofiler/)

`MegaProfiler` is an easy-to-use, highly customizable Python library designed for profiling and analyzing datasets. It provides deep insights into your data's structure, distributions, missing values, anomalies, and more. With built-in support for data validation, anomaly detection, and data drift tracking, it's the perfect tool for data scientists and engineers looking to automate exploratory data analysis (EDA) and quality checks for large datasets.

While other libraries like `pandas-profiling` exist, **MegaProfiler** stands out for its extensibility, scalability, and integration with data validation and anomaly detection, making it ideal for data preprocessing and ETL pipelines.

---

## Key Features

- **Automatic Data Summaries**:
  - Automatically generate statistical summaries, distributions, unique values, missing values, and data types for each column.
  
- **Anomaly Detection**:
  - Flag unusual distributions, outliers, or inconsistent data using z-score, IQR, or machine learning techniques (e.g., Isolation Forest).
  
- **Data Validation**:
  - Set custom validation rules (e.g., no missing values in specific columns, data type constraints) and receive alerts for rule violations.
  
- **Custom Reports**:
  - Generate configurable reports in various formats (e.g., HTML, PDF), with customizable thresholds for anomalies.
  
- **Data Drift Detection**:
  - Track changes in data distributions over time to detect shifts in data quality or content, useful for continuous monitoring of data pipelines.

- **Multicollinearity and Correlation Analysis**:
  - Perform advanced correlation analysis and detect multicollinearity with Variance Inflation Factor (VIF).

- **Time Series Analysis**:
  - Decompose and analyze time series data to identify trends, seasonality, and residuals.

---

## Benefits

`MegaProfiler` is an invaluable tool for:
- **Data Scientists and Engineers**: It automates exploratory data analysis, saving valuable time and reducing manual inspection of large datasets.
- **ETL Pipelines**: Easily detect issues such as missing data, outliers, or data drift, and ensure the quality of data moving through your pipeline.
- **Data Quality Assurance**: Validate the integrity of your data before model training or analysis, minimizing the risk of poor model performance due to flawed data.

---

## Installation

You can install `MegaProfiler` using pip:

```bash
pip install megaprofiler
abhishek-topsis 0.0.1
This Package will help you to calculate TOPSIS Score for multiple criteria...
Requires Python: >=3.6

# TOPSIS Implementation in Python...

---

## Overview
This Python script implements the **TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method for multi-criteria decision-making. It calculates the performance score and rank for given data based on user-defined weights and impacts.

---

## Usage

Run the script with the following command:

```bash
python topsis.py <inputFileName> <weights> <impacts> <resultFileName>
```

- `<inputFileName>`: Path to the CSV file containing the dataset.
- `<weights>`: Comma-separated weights for the criteria (e.g., `1,2,3`).
- `<impacts>`: Comma-separated impacts for the criteria (`+` for benefit, `-` for cost).
- `<resultFileName>`: Name of the output file where results will be saved.

---

## Input File Requirements
1. Must be a `.csv` file with at least **3 columns**.
2. The first column can contain non-numeric values (e.g., IDs).
3. Columns from the 2nd to the last must contain **numeric values only**.

---

## Features
- **Validation**:
  - Ensures correct number of inputs.
  - Validates weights, impacts, and column counts.
  - Handles missing or invalid files.
- **Error Handling**:
  - Displays user-friendly messages for issues like file not found, non-numeric data, or mismatched input parameters.
- **Output**:
  - Adds two new columns: `Topsis Score` and `Rank`.
  - Saves the results in the specified output file.

---

## Dependencies
- Python 3.x
- Required libraries: `pandas`, `math`, `sys`

Install dependencies using:
```bash
pip install pandas
```

---

## License
This project is open-source and free to use under the MIT License.
hien-logger 0.0.3.1
A colorful logger for Python's applications.
Requires Python: >=3.8

# Colorful logger

<div align="center">

![Downloads](https://img.shields.io/pypi/dm/hien_logger) ![GitHub CI](https://github.com/hienhayho/hien_logger/actions/workflows/install_package.yml/badge.svg) ![License](https://img.shields.io/badge/license-MIT-green)

</div>

## :wrench: I. Installation

**1.** You can easily install it from pypi:

```bash
pip install hien_logger
```

**2.** You can also install it from source:

```bash
pip install git+https://github.com/hienhayho/hien_logger.git
```

- Or

```bash
git clone https://github.com/hienhayho/hien_logger.git

cd hien_logger/

pip install -v .

# Optional: Test if installed successfully.
pip install pytest
pytest
```

## :tada: II. Usage

- You can use it as a console logger:

```python
from hien_logger import get_formatted_logger

logger = get_formatted_logger(name="hien_logger")

logger.info("Hello World")
```

- Or you can log it to a file:

```python
from hien_logger import get_formatted_logger

logger = get_formatted_logger(
    name="hien_logger",
    file_path="test.log"
)

logger.info("Hello World")
```

- You can include date of this file log with `include_date=True`. Then the date will be included in file log name.

```python
from hien_logger import get_formatted_logger

logger = get_formatted_logger(
    name="hien_logger",
    file_path="test.log",
    include_date=True
)

logger.info("Hello World")
```

- Log all to one global file:

```python
from hien_logger import get_formatted_logger

logger = get_formatted_logger(
    name="hien_logger",
    global_file_log=True
)

logger.info("Hello World")
```

- You can set datetime to your timezone:

```python
from hien_logger import setup_timezone

setup_timezone("Asia/Ho_Chi_Minh")
```

## :book: III. LICENSE

`hien_logger` is under [MIT LICENSE.](./LICENSE)
mcpx 0.1.1
Add your description here
Requires Python: >=3.11

# mcpx

mcpx is an extension for the [mcp python sdk](https://github.com/modelcontextprotocol/python-sdk) that includes extra functionality.

This package is aimed at client developers.

## Features

- Docker client transport

## Installation

Install mcpx as a python package via pypi:

```bash
uv add mcpx
```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

mcpx is licensed under the MIT License.