Python client for NBA statistics located at nba.com
nbapy - stats.nba.com API for python
A python facing API for
stats.nba.com is notorious for being extremely unreliable. Please report any issues you find.
All data is returned as a pandas dataframe (check out the starter docs if you're new to pandas). For example:
from nbapy import game import pandas as pd game_id = '0021900017' # taken from 'https://stats.nba.com/game/0021900017/' stats = pd.DataFrame(game.BoxScore(game_id).players_stats())
If you want to cache results so you don't have to reach the api every time, you can use requests-cache
from nbapy import game import pandas as pd import requests_cache requests_cache.install_cache('nbapy_cache') game_id = '0021900017' stats = pd.DataFrame(game.BoxScore(game_id).players_stats())
An ongoing process, but check out the jupyter notebook docs, or feel free to poke around the codebase.
To install from pypi:
$ python -m pip install nbapy
- Download from source (git clone, zipped package)
- Run from the root directory:
$ python -m pip install .
1. Fork the repository and create a feature/bug fix branch
2. Install development requirements
$ python -m pip install -e . ".[dev]"
3. Hack away
- black for formatting
- google docstrings
- flake8 for linting
- mypy for static typing analysis
- conventional commits for commit style.
Optional (but recommended)
nbapy has a pre-commit file that you can install to automatically enforce these conventions prior to committing via a git hook.
$ pre-commit install
You can also use
$ pre-commit run -a to run the checks manually.
For commit messages, I recommend using commtizen. It is automatically installed in the dev dependencies, so to commit, you just run
cz c and follow the prompts.
4. Create some tests
5. Make sure everything looks good
$ pytest --cov*
$ pre-commit run -a (if you didn't install the pre-commit git hook)
* note the first time you run this, it may take a few minutes. However, the requests will cache, and subsequent runs should be much faster.
6. Submit a pull request
Other ways to contribute involve submitting any issues or adding some documentation!
- Finish Jupyter Notebook documentation
This is orginally based off of https://github.com/seemethere/nba_py so a lot of the work was done by those guys. My goal with this project is to clean up the code, add some proper documentation, and keep it up to date.
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