Skip to main content

Python utility to easily scrape NBA stats

Project description

# nba_scrape

An easy-to-use Python utility to scrape professional basketball data off using Selenium and BeautifulSoup.

## Installation:

pip install nba_scrape

## Usage:

from nba_scrape import NBA

#### Example:

>>> league = NBA()

>>> lebron = league.get_player(‘lebron james’)

>>> lebron.get_stat(‘pts’, ‘2016-17’, playoffs=True)

>>> 32.8

>>> lebron.get_stats([‘pts’, ‘reb’, ‘ast’, ‘ts%’], ‘2015-18’, mode=’playoffs’)

>>> {‘2015-16’: (26.3, 9.5, 7.6, 0.585), ‘2016-17’: (32.8, 9.1, 7.8, 0.649), ‘2017-18’: (34, 9.1, 9, 0.619)}

#### Get an instance of the NBA class:

league = NBA()

#### Get a player:

player = league.get_player(player_name)


player = league.get_player_by_id(id_number)

#### Get a single stat:

player.get_stat(stat_name, season)

#### Get multiple stats (formatted as a dict with tuples as items):

player.get_stats([stat1, stat2, stat3], season_range, mode=mode)

(Possible modes are ‘season’, ‘playoffs’, or ‘both’; ‘season’ is the default.)

## Current functionality:

  1. Compile a list of all NBA players and their IDs when initializing the NBA class.
  2. Easily load all regular season and playoff stats off a player’s career page to a SQLite database. Only the player’s name is required as input.
  3. Retrieve all traditional stats and select advanced ones (such as True Shooting Percentage) via database queries; only the requested stats and seasons are required as input.
  4. Browser-agnostic; uses the best available browser or raises an error if no supported browser is available.
  5. Test suite to ensure correct statistics are returned.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for nba_scrape, version 0.56
Filename, size File type Python version Upload date Hashes
Filename, size nba_scrape-0.56.tar.gz (9.4 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page