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        "description": "[![Downloads](https://pepy.tech/badge/py-ball)](https://pepy.tech/project/py-ball)\n\n# py_ball\nPython API wrapper for stats.nba.com with a focus on NBA and WNBA applications\n\n## Introduction\n\nThe motivation for this stems from [nba_py](https://github.com/seemethere/nba_py) by [seemethere](https://github.com/seemethere) and [nbastatsR](https://github.com/abresler/nbastatR) by [abresler](https://github.com/abresler). The work towards a Python API wrapper in `nba_py` is a great start, but the documentation of the [stats.nba.com](https://stats.nba.com) API is lacking. `nbastatsR` is an extremely valuable resource for the R community, and this work hopes to extend the breadth and depth of that package. In my research, I have also come across the recent effort of [nba_api](https://github.com/swar/nba_api) by [swar](https://github.com/swar). This looks similar to `nba_py` and I am hoping to collaborate.\n\n## Goals\n\nIf successful, `py_ball` should accomplish the following:\n- By working with the community, improve the quality of documentation for [stats.nba.com](https://stats.nba.com).\n- Further enable the dissemination of basketball statistics to increase the understanding of the sport and encourage the practice of basketball analytics.\n- Produce introductory analyses leveraging NBA and WNBA data to reduce the barrier of entry to basketball analytics through demonstration.\n- Focus on the WNBA in an effort to stress inclusivity and contribute to women's basketball analytics.\n\n## Documentation\n\nWhile `nba_api` improves greatly upon the documentation of the [stats.nba.com](https://stats.nba.com) API in `nba_py`, `py_ball` strives to take documentation further through the following:\n- Fully documented code, including function, class, and script docstrings.\n- Extend endpoint and parameter documentation to include feature definitions.\n\n### [Current Documentation](https://github.com/basketballrelativity/py_ball/wiki)\n\nClasses:\n\nThe functionality of the classes within the package are documented in both the docstrings and [this site](https://basketballrelativity.github.io/py_ball/_build/html/index.html). The endpoints, parameters, and tables are documented in the Wiki (linked below):\n\n- [BoxScore](https://github.com/basketballrelativity/py_ball/wiki/BoxScore)\n- [Draft](https://github.com/basketballrelativity/py_ball/wiki/Draft)\n- [Image](https://github.com/basketballrelativity/py_ball/wiki/Image)\n- [LeaderBoard](https://github.com/basketballrelativity/py_ball/wiki/LeaderBoard)\n- [League](https://github.com/basketballrelativity/py_ball/wiki/League)\n- [LeagueDash](https://github.com/basketballrelativity/py_ball/wiki/LeagueDash)\n- [PlayByPlay](https://github.com/basketballrelativity/py_ball/wiki/PlayByPlay)\n- [Player](https://github.com/basketballrelativity/py_ball/wiki/Player)\n- [Salaries](https://github.com/basketballrelativity/py_ball/wiki/Salaries) (using [Hoopshype](https://hoopshype.com/))\n- [ScoreBoard](https://github.com/basketballrelativity/py_ball/wiki/ScoreBoard)\n- [Shots](https://github.com/basketballrelativity/py_ball/wiki/Shots)\n- [Team](https://github.com/basketballrelativity/py_ball/wiki/Team)\n- [WinProbability](https://github.com/basketballrelativity/py_ball/wiki/WinProbability)\n\n## Development\n\n1. ~~Initially map [stats.nba.com](https://stats.nba.com) API and fully document code.~~\n2. ~~Refactor code to generate a more consistent structure across classes.~~\n3. ~~Document endpoints and parameters with definitions.~~ (See Wiki [here](https://github.com/basketballrelativity/py_ball/wiki))\n4. Research other basketball-related APIs to map.\n5. ~~Write unit tests for the package.~~\n6. ~~Begin introductory basketball analytics analyses.~~\n    - ~~Franchise History~~ ([here!](https://github.com/basketballrelativity/franchise_history))\n    - ~~Draft Combine Player Sheet~~ ([here!](https://github.com/basketballrelativity/draft_combine))\n    - ~~Live NBA/WNBA scoreboard~~ ([here!](https://github.com/basketballrelativity/scoreboard))\n    - ~~Shot Probability Model~~ ([here!](https://github.com/basketballrelativity/shot_probability))\n    - ~~Location Data Exploration~~ ([here!](https://github.com/basketballrelativity/location_data))\n    - ~~Assist Networks~~ ([here!](https://github.com/basketballrelativity/assist_networks))\n    - ~Win Probability Model~ ([here!](https://github.com/basketballrelativity/py_ball/wiki/WinProbability))\n\n## Installation\n\nThe package is built for Python 3 and leverages the packages in the `requirements.txt` file. `py_ball` can be installed via pip (more info [here](https://pypi.org/project/py-ball/)):\n```\npip install py_ball\n```\n\n## Usage\n\nThe [stats.nba.com](https://stats.nba.com) API requires a request header for all API calls. A good discussion on this, including steps to obtain a proper request header, can be found [here](https://stackoverflow.com/questions/46781563/how-to-obtain-a-json-response-from-the-stats-nba-com-api). With a request header in `HEADER`, the example below demonstrates usage of the package to pull franchise history for the WNBA:\n\n```\nfrom py_ball import league, image\n\nleague_id = '10' #WNBA\nfranchises = league.League(headers=HEADERS,\n                           endpoint='franchisehistory',\n                           league_id=league_id)\n```\n\nEach class, with the exception of the `Headshot` and `Logo` classes, has a `data` attribute. This is a dictionary containing table names as keys and a list of dictionaries of table data as values. The `Headshot` and `Logo` classes have an `image` attribute that is a PNG object.\n\n## Contact\n\nFollow along for updates or reach out on Twitter [@py_ball_](https://twitter.com/py_ball_)!",
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