explorica 1.0.1

A Python framework for streamlined Exploratory Data Analysis (EDA).

Explorica - A Flexible Framework for Exploratory Data Analysis🌱

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Meta

Explorica is a modular and extensible Python framework for exploratory data analysis (EDA).
It provides ready-to-use components for data preprocessing, feature engineering, statistical analysis, visualization and report automation, allowing analysts and data scientists to focus on insights instead of boilerplate code.

Designed for data analysts and data scientists who want to streamline their EDA workflow.

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Table of Contents

• Main Features
• Installation
• Documentation
• Development Setup
• License

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Main Features

• One-liner Visualizations - Generate ready-to-use plots for numeric and categorical features with a single function call.
• Beyond Pearson - Advanced dependency detection using non-obvious metrics.
• Automated EDA Reports - Run a full EDA pipeline and generate comprehensive PDF or HTML reports with a single script.

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Installation

The source code is currently hosted on GitHub at: https://github.com/LaplaceDevil/explorica.

Binary installers for the latest released version are available at the Python Package Index (PyPI).

# PyPi
pip install explorica

Alternatively, for development or to get the latest code:

# or from github
pip install git+https://github.com/LaplaceDevil/explorica.git

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Documentation

The official documentation is hosted on GitHub Pages - always up-to-date with the latest release.

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Development setup

Explorica uses pylint, flake8 and black for lint and pytest for unit and integration tests. For building documentation, it uses sphinx, numpydoc, and doctests.

To set up the development environment:

git clone https://github.com/LaplaceDevil/explorica
cd explorica

# Basic dev setup
python -m venv .venv
source .venv/bin/activate   # Linux / Mac
# .venv\Scripts\activate    # Windows
pip install -e ".[dev]"

# If you also want to work with documentation
pip install -e ".[dev,docs]"

Contributing

We welcome contributions of all kinds, including:

• Bug fixes - clear and reproducible.
• New features - e.g., new visualizations, metrics, or reports.
• Documentation and examples - improving docs, tutorials, or demos.
• Code improvements and tests - refactoring, optimization, or additional tests.

Any pull request that follows coding standards and passes tests will be reviewed and merged. We encourage contributors to propose creative ideas and enhancements!

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License

MIT License