PyData NYC 2024

Contents

• Notebooks:
  • polars/Idiomatic Polars.ipynb : "Slides" for Polars
• Environment Setup: Instructions for running these notebooks in various environments (locally, Codespaces, and Google Colab).

Running the Jupyter Notebooks

You can run the notebooks in several ways, depending on your preferences and setup:

1. Running Locally

To run the notebooks locally, you will need to create a virtual environment and install the necessary dependencies.

1. Clone the repository:

   git clone [email protected]:mattharrison/pydata_nyc_2024.git
   cd pydata_nyc_2024

2. Set up the environment using uv:

   Mac/Linux:

   $ curl -LsSf https://astral.sh/uv/install.sh | sh

   Windows:

   > powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

   Then run:

   uv sync
   

   The above commands will create a virtual environment, activate it, and install dependencies.

3. Start Jupyter Notebook:

   uv run jupyter notebook

2. Running on GitHub Codespaces

GitHub Codespaces allows you to run this project entirely in the cloud without needing to set up a local environment.

1. Open the GitHub repository in your browser.
2. Click the Code button and select Open with Codespaces.
3. After the Codespace launches, wait for it to install the environment.
4. Click on the notebook and select the local kernel.

The project is pre-configured to install the necessary dependencies when the Codespace is first created.

3. Running on Google Colab

You can also run the notebooks on Google Colab, which provides free GPU/TPU resources for faster computations.

1. Open the repository on GitHub.
2. Navigate to the desired Jupyter notebook file (ending in .ipynb).
3. Update the domain from github.com to githubtocolab.com
4. Once in Colab, you may need to run the first cell to install any required dependencies.

Dependencies

To see the complete list of dependencies, please check the pyproject.toml file.

Suggested Reading

To deepen your understanding of Python for data analysis, I recommend the following books:

• Learning Python for Data: This book provides a comprehensive introduction to Python, with a focus on its applications in data analysis. It covers Python fundamentals, essential libraries, and practical examples to help you get started with data-driven projects.

• Effective Polars: A guide to mastering data manipulation and analysis with Polars.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions, improvements, or additional notebooks to add.

License

This project is licensed under the MIT License.

Contact

If you have any questions, feel free to reach out or open an issue on the repository.