datacleaners 0.3.0

A package for cleaning data before machine learning.

dataCleaner

dataCleaner is a Python package designed to help data scientists clean and preprocess datasets effectively before feeding them into machine learning models. The package provides a comprehensive suite of tools for handling missing data, removing duplicate rows, normalizing numerical columns, and removing outliers.

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Features

• Handle Missing Data: Automatically handle missing values using mean, median, or by dropping rows.
• Drop Duplicate Rows: Identify and remove duplicate rows in your dataset.
• Normalize Numerical Columns: Scale numerical columns to a range of 0 to 1 using Min-Max normalization.
• Remove Outliers: Remove outliers using Z-score or IQR (Interquartile Range) methods.
• Get Cleaned Data: Retrieve the cleaned DataFrame after applying the cleaning methods.

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Installation

Install the package from PyPI using pip:

pip install dataCleaner

cleaner = DataCleaner(df)
cleaner.handle_missing_data()
cleaner.drop_duplicates()
cleaner.normalize_columns()
print(cleaner.get_cleaned_data())