dfcleanerpro 0.2.6

Simple DataFrame cleaning toolkit

dfcleanerpro

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

---

Installation

pip install dfcleanerpro

---

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

---

Getting Started

Basic Usage

import pandas as pd
from dfcleanerpro import DataCleaner

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

cleaned_df = DataCleaner(df).run_all()

---

Example Transformation

data = {
    "Name ": ["Nishanth", "Arun", "Arun", "Azar", "None"],
    "Age": [27, None, 26, 27, 30],
    "City": [" Bangalore", "Chennai", "Chennai", None, "Mumbai"],
    "Constant": [1,1,1,1,1]
}

df = pd.DataFrame(data)

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

---

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

---

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

---

Built With

• Python
• Pandas
• NumPy

---

Use Cases

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

---

Future Enhancements

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

---

Contributions

Contributions, issues, and feature requests are welcome!

License

MIT License