{ "info": { "author": "Devin Kelly", "author_email": "dwwkelly@fastmail.fm", "bugtrack_url": null, "classifiers": [], "description": "fantasy_basketball\n==================\n\nThis package will fetch NBA stas from basketball-reference.com, parse \nthe statistics into pandas dataframes, then visualize the statistics.\n\nInstallation\n============\n\nThis package can be installed with either pip,::\n\n $ pip install Fantasy_Basketball\n\nOr with directly from the source code::\n\n $ git checkout https://github.com/dwwkelly/fantasy_basketball\n $ cd fantasy_basketball\n $ python setup.py install\n\n\nDependencies\n============\n\n * Click\n * numpy\n * matplotlib\n * pandas\n * jinja2\n * pycurl',\n * beautifulsoup4\n * lxml\n \nUsage\n=====\n\nA library and a user application are provided, you can use\nthe user application like this::\n\n $ FB_Manager download --year 2013 --teams --draft\n $ FB_Manager process --year 2013 --teams\n $ FB_Manager plot --year 2013\n\nData Storage\n============\n\nThe fantasy_basketball library creates several directories::\n\n ~/.fantasy_basketball/plots\n ~/.fantasy_basketball/processed_data\n ~/.fantasy_basketball/raw_data\n\nEach directory contains directories that are either the data type or\nthe year for the data, *e.g.*::\n\n ~/.fantasy_basketball/processed_data/2013\n ~/.fantasy_basketball/raw_data/teams/2013\n\nThe raw data is all HTML files, the processed data is pickle files\nthat contain pandas dataframes, the plots directory contains either\neps images or png images.\n\nYou can import the dataframes yourself for your own analysis with ipython::\n\n In [1]: import pandas as pd\n\n In [2]: import os\n\n In [3]: data_dir = os.path.expanduser('~/.fantasy_basketball/processed_data/2013/team_data.pkl')\n\n In [4]: df = pd.read_pickle(data_dir)\n\n In [5]: df.shape\n Out[5]: (347, 55)\n\n\n\nTODO\n====\n\n* Config file.\n* Generate HTML from data.\n* Download and process more statistics.\n* Deal with infer_types 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