dfcleanerpro 0.2.3

Simple DataFrame cleaning toolkit

dfcleanerpro

A lightweight and efficient DataFrame preprocessing library designed for modern data workflows.

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Installation

pip install dfcleanerpro

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Key Capabilities

• Automated data cleaning pipeline
• Intelligent handling of missing values
• Standardized column name formatting
• Duplicate record elimination
• Removal of low-information columns
• String normalization for text fields
• Simple and intuitive API design

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Getting Started

Basic Usage

import pandas as pd
from dfcleanerpro import DataCleaner

df = pd.read_csv("data.csv")

cleaned_df = DataCleaner(df).run_all()

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Example Transformation

data = {
    "Name ": ["Alice", "Bob", "Bob", "None"],
    "Age": [25, None, 25, 30],
    "City": [" Chennai", "Delhi ", "Delhi ", None],
    "Constant": [1,1,1,1]
}

df = pd.DataFrame(data)

cleaned = DataCleaner(df).run_all()
print(cleaned)

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What It Handles

Task	Description
Missing Values	Replaces nulls using smart strategies
Column Formatting	Converts names to clean snake_case
Duplicate Rows	Identifies and removes duplicates
Constant Columns	Drops columns with no variance
String Cleanup	Removes unwanted whitespace

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Design Philosophy

Data preprocessing is a repetitive but critical step in any data workflow. This library focuses on:

• Simplicity over complexity
• Clean and readable transformations
• Reusability across projects

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Built With

• Python
• Pandas
• NumPy

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Use Cases

• Data Engineering pipelines
• Data Science preprocessing
• Exploratory Data Analysis (EDA)
• Machine Learning data preparation

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Future Enhancements

• CLI support for CSV processing
• Data validation rules
• Outlier detection utilities
• Data profiling reports
• Integration with big data tools

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Contributions

Contributions, issues, and feature requests are welcome!

License

MIT License