Metadata-Version: 1.1
Name: nba_scrape
Version: 0.55
Summary: Python utility to easily scrape NBA stats
Home-page: https://github.com/hqadeer/nba_scrape.git
Author: Hamza Qadeer
Author-email: hamza.qadeer@berkeley.edu
License: UNKNOWN
Description: # nba_scrape
        
        An easy-to-use Python utility to scrape professional basketball data off stats.nba.com 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)`
        
        OR
        
        `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.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
