Polars Data Analysis Environment

A Docker-based development environment for data analysis using Polars, Jupyter Lab, and Python 3.11.

Prerequisites

• Docker installed on your system
• Git (optional, for cloning the repository)

Quick Start

1. Build the Docker image:

   ./01-docker_build.sh
   # or
   docker build -t polars-data-analysis .

2. Run the container:

   ./02-docker_run.sh
   # or
   docker run -p 8888:8888 -v ${PWD}:/app polars-data-analysis

3. Open your web browser and navigate to the URL shown in the terminal output (usually starts with http://127.0.0.1:8888/...)

   Default token: yourtoken

Installed Packages

Core Data Science

• Polars
• NumPy
• Pandas
• Matplotlib
• Seaborn
• scikit-learn

Deep Learning

• TensorFlow
• PyTorch
• Transformers
• Hugging Face Hub

Machine Learning

• XGBoost
• LightGBM
• CatBoost
• Optuna
• AlphaPy

Development Tools

• Jupyter Lab
• Jupyter Black
• IPyWidgets
• MLflow
• Weights & Biases
• FastAPI
• Streamlit
• Dash

Features

• Python 3.11 base environment
• Jupyter Lab server accessible via browser
• Volume mounting for persistent notebook storage
• Pre-installed data science and ML packages
• Polars for high-performance data manipulation

Development

The project uses volume mounting (-v ${PWD}:/app) to sync the local directory with the container's /app directory. Any changes made to notebooks or other files will persist on your local machine.

CI Testing

This project includes GitHub Actions workflows for continuous integration testing. The CI process:

• Builds a lightweight Docker image with essential dependencies
• Runs basic package import tests
• Verifies the development environment integrity

License

This project is open-source and available under the MIT License.