As a huge fan of both basketball and BeautifulSoup 4 (currently in alpha), I
decided to rewrite an earlier module I'd been using to scrape games from ESPN.
In order to use this package, you will need lxml, mock, and bs4
installed.
I've found it parses pages and data pretty fast — around a second to parse a game, rearrange the data into a tuple, and then spit it back out. On average, most games normally consist of 400 to 460 individual plays (timeouts and interruptions are counted as an Offical Play).
The tuple returned consists of the away team, home team, and a list of dictionaries (each one represents an individual play in the game). You can always read the source code to find out more.
Also, the library does have numerous unit tests that you can check out.
Using the datetime module.
>>> import datetime
>>> from espn import get_games
>>> yesterday = datetime.date.today() - datetime.timedelta(1)
>>> for game in get_games(yesterday, iterable=True):
... print game
Alternatively you can just use a string in YYYYMMDD format.
>>> yesterday_string = "20110330"
>>> for game in get_games(yesterday_string, iterable=True):
... print game
You don't have to use the iterable=True argument — a list will be passed
back to you.
>>> april_fools_last_year = "20100401"
>>> games = get_games(april_fools_last_year)
You can also scrape NCAA Men's Basketball games by passing in a
league='ncb' argument.
>>> march_1 = '20110301'
>>> for ncb_game in get_games(march_1, league='ncb', iterable=True):
... print ncb_game
The daterange function can also come in handy for generating days between two
specific dates.
>>> import datetime
>>> from espn import daterange, get_games
>>> yesterday = datetime.date.today() - datetime.timedelta(1)
>>> week_ago = yesterday - datetime.timedelta(7)
>>> for day in daterange(week_ago, yesterday):
... for game in get_games(day):
... print game