nullpy 0.0.2

Automated data cleaning tool

📦 NullPy

An Intelligent, Data-Aware Pandas DataFrame Cleaner 🚀

NullPy is a Python library for automatic, intelligent, and target-aware cleaning of pandas DataFrames. It handles missing values, outliers, and predictive imputations using ML models when needed. No more repetitive manual cleaning — NullPy decides the best strategy automatically.

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✨ Features

• 🧹 Automatic Missing Value Handling

  • Detects missing values.
  • Decides best imputation strategy (mean, median, mode, predictive).
  • Can train ML models (RandomForest, LinearRegression, LogisticRegression) for predictive imputation.

• 📊 Outlier Detection & Handling

  • Detects outliers using IQR method.
  • Handles them via clip, drop, or predictive imputation.

• 🎯 Target-Aware Cleaning

  • Uses correlation & chi-squared tests to decide when predictive cleaning is useful.

• ⚡ Highly Customizable

  • Parameters for imputation strategy, outlier strategy, verbosity, and difference reporting.

• 🖥️ Beautiful Console Output (powered by Rich)

  • Colorful progress bars.
  • Summary cleaning reports.
  • Difference reports (before vs after cleaning).

• 🔮 Demo Reports Included

  • Quick one-call demo for showing cleaning in action.

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🚀 Installation

pip install pandas numpy scikit-learn rich

(Or clone the repo and drop nullpy.py into your project.)

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⚡ Quick Usage

1️⃣ Basic Cleaning

import pandas as pd
from nullpy import SmartDFCleaner

# Example Data
data = {
    'Age': [25, 30, None, 22, 35, 45, 28, 33, None, 50, 150],
    'Income': [50000, 60000, 58000, None, 72000, 68000, 52000, 61000, 59000, 75000, 80000],
    'Gender': ['Male', 'Female', 'Male', 'Female', 'Male', None, 'Female', 'Male', 'Male', 'Female', 'Male'],
    'City': ['NY', 'LA', 'NY', 'SF', 'LA', 'NY', 'SF', 'LA', 'NY', 'SF', 'LA'],
    'Purchased': [0, 1, 1, 0, 1, 1, 0, 1, None, 1, 0]
}
df = pd.DataFrame(data)

# Run cleaner
cleaner = SmartDFCleaner(target_column="Purchased", show_difference=True)
cleaned_df = cleaner.fit_transform(df)

print(cleaned_df)

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2️⃣ One-Call Full Demo (with auto + predictive cleaning reports)

from nullpy import SmartDFCleaner

newdf = SmartDFCleaner().clean_it(df, target_column="Purchased")

This will:

• Show original data.
• Show auto-cleaned DataFrame.
• Show predictive-cleaned DataFrame.
• Print summary reports + null counts.

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⚙️ Parameters

Parameter	Type	Default	Description
target_column	str	None	Target variable for correlation/predictive imputation
impute_strategy	str	auto	auto, mean, median, mode, predictive
outlier_strategy	str	auto	auto, clip, drop, predictive
verbose	bool	True	Print logs and progress
show_difference	bool	False	Show before/after difference report

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📊 Example Console Output

> Identified 2 numerical and 2 categorical features.
> 'Age' has high missing data (18.0%). Using simple imputation.
> Applied Median Imputation to column 'Age'.
> Clipped 1 outliers in column 'Age'.
> Cleaning process completed successfully!

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🛠️ Methods

• fit_transform(df) → Returns cleaned DataFrame.
• demo_report(df, target_column, ...) → Runs auto + predictive cleaning demo.
• clean_it(df, target_column, ...) → One-call shortcut for full demo + final cleaned DF.

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📌 Roadmap

• 🔜 Add support for time-series cleaning.
• 🔜 Add advanced outlier detection (Isolation Forest, Z-score).
• 🔜 Export cleaning logs to JSON/CSV.

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👨‍💻 Author

Made with ❤️ and ☕ by Foresty (India 🇮🇳)

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