my-eda-helper 0.1.2

A helper package for Exploratory Data Analysis (EDA)

My EDA Helper - Boost Your Exploratory Data Analysis Process! 🚀

EDA Helper is a Python package designed to streamline your Exploratory Data Analysis (EDA) process. It provides a collection of helper functions to quickly analyze, visualize, and summarize datasets. Whether you're working with numeric, categorical, or datetime data, this package has you covered!

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Credits 🙏

This package is inspired by the brilliant work of @MisbahullahSheriff. The original EDA helper functions were created by him, and I have extended and organized them for easier use. Additional functions and improvements have been added by me (@shemanto27).

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Installation 📦

You can install the package via pip:

pip install my_eda_helper

For Google Colab users, install it directly in your notebook:

!pip install my_eda_helper

Usage 🛠️

1. Import the Package

import my_eda_helper as eda

2. High-Level Analysis

Missing Data

Find Missing Values:

missing_data = eda.missing_info(df)
print(missing_data)

Plot Missing Data:

eda.plot_missing_info(df)

Correlation Analysis

Numeric Features (Pearson/Spearman):

eda.correlation_heatmap(df)

Categorical Features (Cramer's V):

eda.cramersV_heatmap(df)

Pair Plots

eda.pair_plots(df)

3. Detailed Analysis

Numeric Features

Summary:

eda.num_summary(df, "Age")

Univariate Plots:

eda.num_univar_plots(df, "Fare")

Bivariate Plots:

eda.num_bivar_plots(df, "Age", "Fare")

Categorical Features

Summary:

eda.cat_summary(df, "Sex")

Univariate Plots:

eda.cat_univar_plots(df, "Embarked")

Bivariate Plots:

eda.num_cat_bivar_plots(df, "Fare", "Sex")

Hypothesis Testing

Numeric vs Numeric:

eda.num_num_hyp_testing(df, "Age", "Fare")

Numeric vs Categorical:

eda.num_cat_hyp_testing(df, "Fare", "Sex")

Categorical vs Categorical:

eda.hyp_cat_cat(df, "Sex", "Survived")

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Contributing 🤝

Contributions are welcome! If you have ideas for new features, improvements, or bug fixes, please feel free to:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/YourFeatureName
   

3. Commit your changes:

   git commit -m 'Add some feature'
   

4. Push to the branch:

   git push origin feature/YourFeatureName
   

5. Open a pull request.

Please ensure your code follows the project's style and includes appropriate tests.

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License 📄

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

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Support 💬

If you have any questions, suggestions, or issues, please open an issue on the GitHub repository.

Happy EDA! 🎉