https://github.com/aeturrell/python4ds
Python for Data Science. This repository hosts the code behind the online book that teaches you how to use Python for data science.
Science Score: 49.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.5%) to scientific vocabulary
Keywords
Repository
Python for Data Science. This repository hosts the code behind the online book that teaches you how to use Python for data science.
Basic Info
- Host: GitHub
- Owner: aeturrell
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://aeturrell.github.io/python4DS
- Size: 4.93 MB
Statistics
- Stars: 147
- Watchers: 2
- Forks: 37
- Open Issues: 3
- Releases: 4
Topics
Metadata Files
README.md
Python for Data Science
This is the repo for Python for Data Science.
This README is for developers and contributors. If you're here to read the book, head over to https://aeturrell.github.io/python4DS.
Contributing
We are very keen to encourage contributors! You can contribute by raising issues with the book or by creating pull requests directly. If you are creating a pull request you will need to install the development environment locally and check the book builds after you've made your changes.
Note that we aim to closely follow the content of R for Data Science (2e).
Before making a pull request you should test that the pre-commit checks pass, including that there are no outputs included, and that the book builds. See below for instructions on how to do these locally.
When you make a pull request, pre-commit and build will run automatically, and fail if there are errors. They are in .github/workflows/tests.yml.
Installing the development environment locally
You will need installations of Python 3.10 and uv. uv can be used to install certain distributions of Python through the uv python install 3.10 command but you can use other Python installations.
Clone this repository.
To install the development environment, run uv sync from the project root. This should create a .venv/ directory with the Python4DS environment in it. You can check that the environment has been installed by running uv run python -V in the project root directory.
Building the book
The book is compiled from source markdown and Jupyter notebook files jupyter-book package.
To build the book, run
bash
uv run jupyter-book build .
Once this command is run, you should be able to look at the HTML files for the book locally on your computer. They will be in _build. The project is configured to stop the build if any errors are encountered. This is a frequent occurrence! You'll need to look at the logs to work out what might have gone wrong.
Uploading the book
Automatic uploads of the book
This repo is configured such that new versions automatically build and upload the book to the website. The GitHub Action that does this is in .github/workflows/release.yml.
Uploading the built book manually
You shouldn't need to upload the book if you are a regular contributor. There are times when you might need to as an admin, but normally the book will be updated automatically upon release of a new version.
See here for how to upload revised HTML files, but the key command is
bash
uv run ghp-import -n -p -f _build/html
Code hygiene
This book uses pre-commit to strip output from notebooks, lint, format, and check for large files added by mistake.
To perform the pre-commit checks, use
bash
uv run pre-commit run --all-files
on your staged files. Ensure pre-commit reports all tests as having passed before committing.
Publishing a new version
Open a new branch with the version name, eg
v1.0.4Change the version in
pyproject.toml(you can runuv run version_bumper.py, which has script-level dependencies)Commit the change with a new version label (eg
v1.0.4) as the commit messageGo to GitHub. Assuming the tests pass, merge into main.
The book should automatically build in GitHub actions, and be pushed to the website. A new release will also be created automatically. A new Zenodo entry is also automatically created.
Running and developing in a Docker container
There is a Dockerfile associated with this project. Pre-reqs To use it:
- Pre-reqs: docker installed, VS Code installed, VS Code docker and Remote Explorer extensions installed.
- Build the image from the file. Right click on the file in VS Code and select "Build Image".
- On the Docker tab of VS Code, right-click on the
python4DS:latestimage and select 'Run Interactive'. - On the Docker tab again, right-click on the running
python4DS:latestcontainer and click "Attach Visual Studio Code". - Do any development as required (see the instructions above)
If you wish to copy any files (eg the built HTML files) back to your local machine to check them, use
bash
docker cp CONTAINER:app/_build/html/ temp_dir/
Owner
- Login: aeturrell
- Kind: user
- Website: www.aeturrell.com
- Repositories: 9
- Profile: https://github.com/aeturrell
GitHub Events
Total
- Create event: 8
- Release event: 2
- Issues event: 8
- Watch event: 19
- Delete event: 3
- Issue comment event: 9
- Push event: 23
- Pull request review event: 3
- Pull request review comment event: 2
- Pull request event: 15
- Fork event: 6
Last Year
- Create event: 8
- Release event: 2
- Issues event: 8
- Watch event: 19
- Delete event: 3
- Issue comment event: 9
- Push event: 23
- Pull request review event: 3
- Pull request review comment event: 2
- Pull request event: 15
- Fork event: 6
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 6
- Total pull requests: 9
- Average time to close issues: 3 months
- Average time to close pull requests: 18 days
- Total issue authors: 3
- Total pull request authors: 3
- Average comments per issue: 0.83
- Average comments per pull request: 0.0
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 5
- Pull requests: 9
- Average time to close issues: about 2 months
- Average time to close pull requests: 18 days
- Issue authors: 3
- Pull request authors: 3
- Average comments per issue: 0.8
- Average comments per pull request: 0.0
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- aeturrell (5)
- ProfAvery (2)
- hotshotberad (1)
Pull Request Authors
- aeturrell (7)
- TheJolman (1)
- ProfAvery (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- continuumio/miniconda3 4.10.3-alpine build
- graphviz *
- ibis-framework *
- openpyxl *
- pandas-profiling *
- pyarrow *
- skimpy *
- sqlmodel *
- watermark *