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License: GPL v3 Maintenance PyPI version Downloads Build Status codecov

This package is no longer maintained as of 2021/01/30. Any outstanding issues or new ones will not be fixed

nba_scraper

This is a package written in Python to scrape the NBA's api and produce the play by play of games either in a csv file or a pandas dataframe. This package has two main functions scrape_game which scrapes an individual game or a list of specific games, and scrape_season which scrapes an entire season of regular season games.

The scraper goes back to the 1999-2000 season and will pull the play by play along with who was on the court at the time of each play. Some other various statistics may be calculated as well.

As of version 1.0.8 the scraper will now scrape WNBA games as well as NBA games. Just call wnba_scrape_game instead of scrape_game. The parameters and usage is exactly the same as scrape_game function. As of right now I know it goes back to the 2005 season maybe further just haven't tested. Be warned it is much slower than the nba scraper due to the extra api calls needed to pull in player names that are readily available in the NBA api itself.

Installation

To install this package just type this at the command line:

pip install nba_scraper

Usage

scrape_game

The default data format is a pandas dataframe you can change this to csv with the data_format parameter. The default file path is the users home directory you can change this with the data_dir parameter

import nba_scraper.nba_scraper as ns

# if you want to return a dataframe
# you can pass the function a list of strings or integers
# all nba game ids have two leading zeros but you can omit these
# to make it easier to create lists of game ids as I add them on
nba_df = ns.scrape_game([21800001, 21800002])

# if you want a csv if you don't pass a file path the default is home
# directory
ns.scrape_game([21800001, 21800002], data_format='csv', data_dir='file/path')

scrape_season

The data_format and data_dir key words are used the excat same way as scrape_game. Instead of game ids though, you would pass the season you want scraped to the function. This season is a four digit year that must be an integer.

import nba_scraper.nba_scraper as ns

#scrape a season
nba_df = ns.scrape_season(2019)

# if you want a csv if you don't pass a file path the default is home
# directory
ns.scrape_season(2019, data_format='csv', data_dir='file/path')

scrape_date_range

This allows you to scrape all regular season games in the date range passed to the function. As of right now it will not scrape playoff games. Date format must be passed in the format YYYY-MM-DD.

import nba_scraper.nba_scraper as ns

#scrape a season
nba_df = ns.scrape_date_range('2019-01-01', 2019-01-03')

# if you want a csv if you don't pass a file path the default is home
# directory
ns.scrape_date_range('2019-01-01', 2019-01-03', data_format='csv', data_dir='file/path')

Contact

If you have any troubles or bugs please open an issue/bug report. If you have any improvements/suggestions please submit a pull request. If it falls outside those two areas please feel free to email me at matt@barloweanalytics.com.

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A scraper to scrape the NBA API and compile a play by play file

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