Metadata-Version: 2.4
Name: cleanframe-data
Version: 0.0.5
Summary: Automated Data Cleaning for Faster Analytics
Project-URL: Homepage, https://github.com/TejasAnalyst/cleanframe
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.5.0
Requires-Dist: numpy>=1.20.0
Dynamic: license-file

# cleanframe-data 🪄

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue.svg)](https://www.python.org/)
[![PyPI version](https://img.shields.io/pypi/v/cleanframe-data.svg)](https://pypi.org/project/cleanframe-data/)

**Automated Data Cleaning for Faster Analytics**

`cleanframe-data` is a lightweight, fast, and intuitive Python library designed to automate dataset diagnostics and cleaning. It helps data analysts, scientists, and beginners clean messy datasets, handle missing values, drop low-quality columns, and cap outliers in just one line of code.

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## 🚀 Features

* **One-Line Auto-Clean:** Drop duplicates, remove low-quality columns, impute missing values, and handle outliers instantly using `cf.auto_clean(df)`.
* **Advanced Outlier Handling:** Automatically detects and caps extreme numerical outliers using the Interquartile Range (IQR) method.
* **Smart Column Dropping:** Drops columns automatically if their missing data percentage crosses your defined threshold.
* **Dataset Diagnostics:** Get a quick, comprehensive report of data types, missing values, and percentages.
* **Modern Pandas Ready:** Built from the ground up to support modern Pandas (2.0+) Copy-on-Write behaviors without annoying warnings.

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## 🛠️ Installation

You can install the official stable release directly from PyPI:

```bash
pip install cleanframe-data
