Metadata-Version: 2.4
Name: check-the-data
Version: 0.1.0
Summary: Quick descriptive statistics for Polars DataFrames exported to Excel
License: MIT
Project-URL: Homepage, https://github.com/pradeepmav/check_the_data
Project-URL: Repository, https://github.com/pradeepmav/check_the_data
Project-URL: Issues, https://github.com/pradeepmav/check_the_data/issues
Keywords: polars,data-analysis,descriptive-statistics,excel,data-science
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: polars>=0.20.0
Requires-Dist: pandas>=1.5.0
Requires-Dist: xlsxwriter>=3.0.0
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: flake8; extra == "dev"
Dynamic: license-file

# check-the-data

Quick descriptive statistics for [Polars](https://pola.rs/) DataFrames — exported to Excel in one call.

Built for Machine Learning / Data Science workflows where you need a rapid, comprehensive statistical profile of every column: data type, missing values, distinct values, min/max, mean, median, mode, and missing percentage.

## Features

- Works with large files (> 200 GB) via Polars
- One-shot Excel output with polished table formatting
- Automatic fallback to Pandas / CSV if Excel write fails
- Handles numeric and non-numeric columns safely

## Installation

```bash
pip install check-the-data
```

Or install from source:

```bash
git clone https://github.com/pradeepmav/check_the_data.git
cd check_the_data
pip install -e .
```

## Usage

```python
import polars as pl
import check_the_data

# Load your data
df = pl.read_csv("your_data.csv")

# Generate descriptive statistics
result = check_the_data.fact_check_of_the_data(
    dataframe=df,
    output_dir="./reports",
    workbook_name="data_profile"
)
print(result)
```

## Requirements

- Python >= 3.9
- polars >= 0.20.0
- pandas >= 1.5.0 (fallback)
- xlsxwriter >= 3.0.0

## License

MIT
