nf-datalib 0.1.0

A Python library for data manipulation and analysis

DataLib Project

DataLib is a Python library designed to simplify data manipulation and analysis in various projects. It provides features catering to a wide range of users, from beginners exploring the basics of data processing to experts seeking advanced tools for statistical analysis and machine learning models.

Installation

You can install DataLib using pip:

pip install datalib

Features

Data Manipulation

• Load and process CSV files (read, write, filters).
• Data transformations (normalization, handling missing values).

Statistical Computations

• Mean, median, mode, standard deviation, correlation.
• Basic statistical tests (t-test, chi-square test).

Data Visualization

• Generate simple graphs (bar charts, histograms, scatter plots).
• Support for advanced visualizations like correlation matrices.

Advanced Analysis

• Linear and polynomial regression models.
• Supervised classification algorithms (k-NN, decision trees).
• Unsupervised methods (k-means, principal component analysis).

Usage

from datalib.data_manipulation import normalize_column
from datalib.visualization import plot_histogram
import pandas as pd

# Load and normalize data
data = pd.DataFrame({"values": [1, 2, 3, 4, 5]})
normalized = normalize_column(data, "values", method="minmax")

# Create visualization
fig = plot_histogram(data, "values")
fig.savefig("histogram.png")

Development

Setting up the development environment

1. Clone the repository:

git clone https://github.com/NaderFerjani/datalib.git
cd datalib

2. Install development dependencies:

pip install -e ".[test,doc]"

Running tests

pytest tests/

Building documentation

cd docs
python -m sphinx -b html . _build/html

Versioning

DataLib follows Semantic Versioning. Version numbers follow the format MAJOR.MINOR.PATCH:

• MAJOR version for incompatible API changes
• MINOR version for new functionality in a backward compatible manner
• PATCH version for backward compatible bug fixes

Creating a new release

1. Update version:

python scripts/release.py [major|minor|patch]

2. Review and commit changes
3. Push to GitHub with tags
4. The GitHub Actions workflow will automatically publish to PyPI

License

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

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Author

• Nader Ferjani - GitHub
• Email: [e-mail]

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Project Goals

The main objective is to develop a professional packaging system for the DataLib library, enabling:

1. Easy and intuitive installation through package managers like pip.
2. Distribution on platforms like PyPI (Python Package Index).
3. Integrated, clear, and accessible documentation.

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Work Plan

1. Project Structure

• Organize the source code in a modular format (e.g., src/ directory).
• Define essential files like setup.py, pyproject.toml, or setup.cfg.

2. Dependencies and Compatibility

• Identify and include necessary dependencies (e.g., numpy, pandas, matplotlib, scikit-learn).
• Ensure compatibility with recent Python versions.

3. Documentation

• Write a detailed README.md or README.rst outlining the library's usage and features.
• Add concrete usage examples.
• Generate technical documentation using tools like Sphinx.

4. Testing

• Write unit tests for main functions using pytest.
• Integrate CI/CD workflows (e.g., GitHub Actions) to validate changes.

5. Publication

• Prepare and publish the library on PyPI.
• Regularly update the version following semantic versioning (SemVer).

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Deliverables

• A functional library distributable via pip.
• Online documentation (e.g., hosted on Read the Docs).
• Automated tests and code quality monitoring.

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Evaluation Criteria

• Packaging Quality: Ease of installation and compatibility.
• Documentation Clarity: Completeness and ease of understanding.
• Functionality and Robustness: Reliability of the library's tools.
• Test Coverage: Quality and extent of automated testing.

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DataLib aims to become a reliable and user-friendly library for data enthusiasts and professionals alike, enhancing the Python data ecosystem.