def dump_mlb_data(outfile, start_date=None, end_date=None, max_count=None, use_random=False, datatype='batting'):
"""
Dump MLB statistical data to a file.
:param str outfile: name of file to become pickled pandas datafile
:param str start_date: don't include games from before this date when dumping data
:param str end_date: don't include games from after this date when dumping data
:param int max_count: maximum # of rows to dump
:param bool use_random: whether to select rows at random (if False, choose most recent)
:return:
"""
print 'Dump MLB data for', datatype
print 'loading data...'
all_bsbr_logs = load_gamelogs(datatype=datatype)
unindexed_dfs = []
print 'reindexing data...'
pbar = progressbar.ProgressBar(widgets=[progressbar.Percentage(), ' ', progressbar.Bar(), ' ', progressbar.ETA()])
for player_id, dataframe in pbar(all_bsbr_logs.items()):
uidf = dataframe.reset_index()
# Add player ID as a column to the dataframe for future joining purposes!
uidf['player_id'] = pandas.Series(data=player_id, index=uidf.index)
unindexed_dfs.append(uidf)
all_game_rows = pandas.concat(unindexed_dfs, ignore_index=True)
# Filter by date
if start_date is not None:
all_game_rows = all_game_rows[all_game_rows['Date'] > start_date]
if end_date is not None:
all_game_rows = all_game_rows[all_game_rows['Date'] < end_date]
# Don't use relief pitchers in our dataset
if datatype == 'pitching':
print 'restricting to starting pitchers only...'
all_game_rows = all_game_rows[all_game_rows['player_id'].apply(brefid_is_starting_pitcher)]
# Sample filtered data
if max_count and max_count < len(all_game_rows):
print 'sampling %d rows...' % max_count
if use_random:
kept_indices = random.sample(all_game_rows.index, max_count)
selected = all_game_rows.iloc[kept_indices]
else:
all_game_rows.sort("Date")
selected = all_game_rows.tail(max_count)
else:
selected = all_game_rows
print 'saving...'
pandas.to_pickle(selected, outfile)
print 'Done!'
return selected
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