sanex 0.1.1

A data cleaning library for Pandas and Polars DataFrames with a simple, chainable API.

Sanex

Sanex is a powerful and intuitive data cleaning library for Python, designed to work seamlessly with both pandas and polars DataFrames. With a fluent, chainable API, Sanex makes the process of cleaning and preparing your data not just easy, but enjoyable.

---

🚀 Key Features

• Fluent, Chainable API: Clean your data in a single, readable chain of commands.
• Dual Backend Support: Works effortlessly with both pandas and polars DataFrames.
• Comprehensive Cleaning Functions: From column name standardization to outlier handling, Sanex has you covered.
• Extensible: Easily add your own cleaning functions to the pipeline.
• Lightweight and Performant: Designed to be fast and efficient.

---

📦 Installation

Install Sanex easily with pip:

pip install sanex

---

⚡ Quick Start

Here's a quick example of how to use Sanex to clean a DataFrame:

import pandas as pd
from sanex import Sanex

# Create a sample DataFrame
data = {
    'First Name': [' John ', 'Jane', '  Peter', 'JOHN'],
    'Last Name': ['Smith', 'Doe', 'Jones', 'Smith'],
    'Age': [28, 34, 22, 28],
    'Salary': [70000, 80000, 65000, 70000],
    'is_active': ['True', 'False', 'true', 'TRUE']
}
df = pd.DataFrame(data)

# Clean the data with Sanex
clean_df = (
    Sanex(df)
    .clean_column_names()
    .remove_whitespace()
    .remove_duplicates()
    .standardize_booleans()
    .to_df()
)

print(clean_df)

---

📖 API Reference

The Sanex class provides a variety of methods for data cleaning. All methods (except to_df) are chainable, returning the Sanex instance.

Initialization

• Sanex(df): Initializes the cleaner with a pandas or polars DataFrame.

Column Name Cleaning

• .clean_column_names(case='snake'): Cleans and standardizes all column names to a specified case.

  • case (str): The target case. Options: 'snake', 'camel', 'pascal', 'kebab', 'title', 'lower', 'screaming_snake'.

• .snakecase(): Converts column names to snake_case.

• .camelcase(): Converts column names to camelCase.

• .pascalcase(): Converts column names to PascalCase.

• .kebabcase(): Converts column names to kebab-case.

• .titlecase(): Converts column names to Title Case.

• .lowercase(): Converts column names to lowercase.

• .screaming_snakecase(): Converts column names to SCREAMING_SNAKE_CASE.

Data Deduplication

• .remove_duplicates(): Removes duplicate rows from the DataFrame.

Missing Data Handling

• .fill_missing(value=0, subset=None): Fills missing values.

  • value: The value to fill missing entries with.
  • subset (list): A list of columns to fill. Defaults to all columns.

• .drop_missing(how='any', thresh=None, subset=None, axis='rows'): Drops rows or columns with missing values.

  • how (str): 'any' or 'all'.
  • thresh (int): The number of non-NA values required to keep a row/column.
  • subset (list): Columns to consider.
  • axis (str): 'rows' or 'columns'.

Whitespace and Text Manipulation

• .remove_whitespace(): Removes leading and trailing whitespace from all string columns.
• .replace_text(to_replace, value, subset=None): Replaces text in string columns.
  • to_replace (str): The text to find.
  • value (str): The text to replace with.
  • subset (list): Columns to apply the replacement to.

Column Management

• .drop_single_value_columns(): Drops columns that have only one unique value.

Outlier Handling

• .handle_outliers(method='iqr', factor=1.5, subset=None): A general method that can be configured to cap or remove outliers.
• .cap_outliers(method='iqr', factor=1.5, subset=None): Caps outliers at a specified threshold.
• .remove_outliers(method='iqr', factor=1.5, subset=None): Removes rows containing outliers.
  • method (str): 'iqr' (Interquartile Range) or 'zscore'.
  • factor (float): The multiplier for the chosen method to determine the outlier threshold.
  • subset (list): Columns to process. Defaults to all numeric columns.

Data Standardization

• .standardize_booleans(true_values=None, false_values=None, subset=None): Converts boolean-like values into actual booleans.
  • true_values (list): A list of strings to be considered True.
  • false_values (list): A list of strings to be considered False.
  • subset (list): Columns to standardize.

Final Output

• .to_df(): Returns the cleaned pandas or polars DataFrame.

---

🤝 Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for bugs, feature requests, or suggestions.

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

---

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.