anquev/datascience-container repository overview

⁠Data Science DevContainer

A lightweight development environment for data science projects using uv for dependency management and ruff for linting.

⁠Quick Start

⁠Prerequisites

• Docker⁠
• VS Code⁠
• Remote - Containers extension⁠

⁠Starting the DevContainer

1. Clone this repository
2. Open the project folder in VS Code
3. When prompted, click "Reopen in Container" or press F1 and select "Remote-Containers: Reopen in Container"
4. Wait for the container to build (this may take a few minutes the first time)

⁠Working with the Environment

• The Python virtual environment is automatically activated in terminal sessions
• All dependencies from pyproject.toml are pre-installed
• Jupyter notebooks can be created and run directly in VS Code

⁠Project Structure

By default, the following structure is created:

.
├── .devcontainer/     # Container configuration
├── data/              # Store your datasets here
├── notebooks/         # Jupyter notebooks
├── src/               # Source code
└── pyproject.toml     # Project dependencies

⁠Managing Dependencies

Add new dependencies to pyproject.toml and run:

uv pip install -e .

⁠Running Python Scripts

python your_script.py

⁠Features

• Python 3.12 environment
• Data science packages (numpy, pandas, polars, scikit-learn, duckdb)
• Visualization tools (matplotlib, seaborn)
• Jupyter notebooks support
• Fast dependency management with uv
• Code linting with ruff

⁠Troubleshooting

If you encounter issues with missing packages:

1. Verify you're using the correct Python interpreter:

   which python
   

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   Should point to /home/vscode/.venv/bin/python

2. Reinstall dependencies:

   uv pip install -e .
   

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3. Check VS Code Python interpreter settings (F1 → "Python: Select Interpreter")

Enjoy coding 😎