This is a package written in Python to scraper the NBA's api and produced 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 specifice games, and scrape_season which scrapes an entire season of regular
season games.
As of right now the package only scrapes as far back as the 2016 season. Plans are in place to be able to scraper older seasons, however these older seasons won't have x/y locations for all events as the more recent seasons do.
To install this package just type this at the command line:
pip install nba_scraper
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')
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')
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')
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.