nbapy 1.1.6

Python client for NBA statistics located at nba.com

nbapy - stats.nba.com API for python

Summary

A python facing API for stats.nba.com

Warning stats.nba.com is notorious for being extremely unreliable. Please report any issues you find.

Usage

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())

Documentation

An ongoing process, but check out the jupyter notebook docs, or feel free to poke around the codebase.

Installation

To install from pypi:

$ pip install nbapy

Else:

• Download from source (git clone, zipped package)
• Run from the root directory:

$ pip install .

Contributing

1. Fork the repository and create a feature/bug fix branch

2. Install development requirements

$ pip install -e . ".[dev]"

3. Hack away

Coding conventions

• black for formatting
• google docstrings
• flake8 for linting
• mypy for static typing analysis

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.

To install: $ pre-commit install

You can also use $ pre-commit run -a to run the checks manually.

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!

To-Do

• Finish Jupyter Notebook documentation