nba_api Documentation

The nba_api package provides a comprehensive suite of tools and endpoints to access NBA.com data. You can use the library to access official stats (via stats.nba.com), live data, and static information like player and team lists.

Key areas of the documentation include:

• Library Core: Debugging tools and HTTP utilities.
• Stats API: Extensive endpoints for player/team performance, shot charts, and league-wide statistics.
• Static Data: Access to player and team metadata.
• Tools: Utilities for endpoint analysis and documentation generation.

The analyze_endpoints_and_create_files.py script can be used to analyze all endpoints and automatically generate .py files, endpoint documentation, and parameter documentation.

Warning: This script is sensitive to changes in the NBA API and may break if major changes occur in the underlying API structure.

When troubleshooting or debugging the generation process, it is recommended to enable DEBUG and DEBUG_STORAGE settings.

Install the nba_api package using pip. The package requires Python 3.10+ and the requests and numpy packages. If you want to work with Pandas DataFrames, you must also install pandas.

pip install nba_api

Set DEBUG_STORAGE to True to save every request to a text file. Files are saved in a debug_storage folder, with filenames generated by joining parameters and using an MD5 hash.

Note: This feature requires DEBUG to be set to True to function.

Set the PROXY variable to a string containing your proxy address to route all requests through that proxy.

PROXY = '127.0.0.1:80'

Use analyze_endpoint(endpoint, pause=1) to conduct a series of tests on a specific endpoint to map out all its parameters. The analysis includes:

1. Required Parameters Test
2. Minimal Requirement Tests
3. Nullable Parameters Test
4. Invalid Values Test

The resulting information is returned as a dictionary.

Every stats endpoint instance contains one or more DataSet objects (e.g., available_seasons, common_player_info). You can retrieve the data from a specific DataSet using the following methods:

• get_json(): Returns data as a JSON string.
• get_dict(): Returns data as a Python dictionary.
• get_data_frame(): Returns the data set as a pandas DataFrame (requires pandas to be installed).