semvec/pypi-packages huggingface dataset:
advaita 0.1.0
Philosophy expressed as code. Quotes from Advaita Vedanta & Acharya Prashant.
Requires Python: >=3.8

# Advaita

> Philosophy expressed as code. Minimal. Sharp. Clear.

`advaita` is a minimalist Python library that brings the clarity of Advaita Vedanta and the teachings of Acharya Prashant into your terminal.

## Installation

```bash
pip install advaita
```

## Usage

### In Python

```python
import advaita

# Experience the silence
quote = advaita.silence()
print(quote)

# Seek clarity
print(advaita.clarity())
```

### CLI

```bash
advaita
```

## Philosophy

Advaita (non-duality) suggests that the observer and the observed are one. This library is a reminder of that simplicity. No dependencies. No complexity. Just truth.
checkpl 0.1.1
Ergonomic inline validation for Polars DataFrames with pipeable assertions and user-friendly wrappers around native Polars boolean expressions.
Requires Python: >=3.12
Keywords: polars, validation, data-quality, dataframe

# checkpl

Ergonomic inline validation for Polars DataFrames.

## Installation

```bash
pip install checkpl
```

## Quick Start

```python
import polars as pl
from checkpl import verify, is_uniq

df = pl.DataFrame({
    "id": [1, 2, 3],
    "city": ["NYC", "LA", "NYC"],
    "year": [2020, 2020, 2021]
})

# Validate with Polars expressions
df.pipe(verify(pl.col("year") >= 2020))

# Validate uniqueness
df.pipe(verify(is_uniq("id")))

# Composite key uniqueness
df.pipe(verify(is_uniq("city", "year")))

# Chain validations
(
    df
    .pipe(verify(pl.col("year") >= 2020))
    .pipe(verify(is_uniq("id")))
    .filter(pl.col("city") == "NYC")
)
```

## Features

- **Pipeable**: Integrates with Polars' `.pipe()` for fluent data pipelines
- **Native expressions**: Use any Polars boolean expression directly
- **Predicates**: Built-in checks like `is_uniq()` for common validations
- **Clear errors**: `CheckError` with descriptive messages and failure counts
- **LazyFrame support**: Works with both `DataFrame` and `LazyFrame`

## API

### `verify(check)`

Validate a DataFrame using a Polars expression or predicate.

```python
# With Polars expression
df.pipe(verify(pl.col("price") > 0))

# With predicate
df.pipe(verify(is_uniq("id")))
```

Raises `CheckError` if validation fails, returns DataFrame unchanged if valid.

### `is_uniq(*cols)`

Check that column(s) have no duplicate values.

```python
df.pipe(verify(is_uniq("id")))           # Single column
df.pipe(verify(is_uniq("id", "year")))   # Composite key
```

## Error Handling

```python
from checkpl import CheckError

try:
    df.pipe(verify(is_uniq("id")))
except CheckError as e:
    print(f"Check '{e.check_name}' failed: {e}")
```

## License

MIT