dataclense 0.1.0

An automated data-cleaning package that handles missing values, duplicates, and formatting issues.

CleanPy - Automated Data Cleaning

CleanPy is an automated data-cleaning package that helps users handle missing values, remove duplicates, fix formatting issues, and standardize datasets effortlessly.

Features

Handle Missing Values: Fill or remove missing values using different strategies.

Remove Duplicates: Detect and eliminate duplicate rows.

Fix Data Formatting: Standardize text, dates, and column names.

Auto-detect Cleaning Strategies: Get suggested fixes for common data issues.

Support for Multiple Formats: Works with CSV, Excel, JSON, Parquet, and more.

Command-line Interface (CLI): Clean data directly from the terminal.

Installation

You can install CleanPy from PyPI using:

pip install cleanpy

Usage

Command-line Interface (CLI)

To clean a dataset from the command line:

cleanpy data.csv --auto-clean -o cleaned_data.csv

Available CLI options:

cleanpy input_file [options]

Options: --auto-clean Apply recommended cleaning strategies automatically. --fill-missing [strategy] Fill missing values (mean, median, mode, etc.). --drop-duplicates Remove duplicate rows. --fix-datatypes Automatically fix data types. --normalize-text Normalize text columns. --format-dates Convert dates to ISO format. --standardize-columns Standardize column names. -o, --output [file] Save cleaned data to a file.

Python API

You can also use CleanPy in your Python scripts:

from cleanpy.core import CleanDF

df = CleanDF("data.csv") df.auto_clean() df.save("cleaned_data.csv")

Supported File Formats

CSV (.csv)

Excel (.xls, .xlsx)

JSON (.json)

Pickle (.pkl)

Parquet (.parquet)

Contributing

We welcome contributions! Feel free to fork the repository, make changes, and submit a pull request.

License

This project is licensed under the MIT License.