def partition_by_team(self):
# get the unique teams in this current data frame
teams = self.historical_data["standardHomeTeamName"].unique()
teams = numpy_sort(teams)
games_by_team = {team: [] for team in teams}
# for each team setup an empty collection
for team in teams:
by_home_team = self.historical_data[
self.historical_data["standardHomeTeamName"].isin([team])]
by_away_team = self.historical_data[
self.historical_data["standardAwayTeamName"].isin([team])]
by_team = pd.concat([by_home_team, by_away_team])
games_by_team[team] = by_team
return games_by_team
def idealfourths(data, axis=None):
"""This function returns an estimate of the lower and upper quartiles of the data along
the given axis, as computed with the ideal fourths. This function was taken
from scipy.stats.mstat_extra.py (http://projects.scipy.org/scipy/browser/trunk/scipy/stats/mstats_extras.py?rev=6392)
"""
def _idf(data):
x = data.compressed()
n = len(x)
if n < 3:
return [numpy_nan,numpy_nan]
(j,h) = divmod(n/4. + 5/12.,1)
qlo = (1-h)*x[j-1] + h*x[j]
k = n - j
qup = (1-h)*x[k] + h*x[k-1]
return [qlo, qup]
data = numpy_sort(data, axis=axis).view(MaskedArray)
if (axis is None):
return _idf(data)
else:
return apply_along_axis(_idf, axis, data)
def idealfourths(data, axis=None):
"""This function returns an estimate of the lower and upper quartiles of the data along
the given axis, as computed with the ideal fourths. This function was taken
from scipy.stats.mstat_extra.py (http://projects.scipy.org/scipy/browser/trunk/scipy/stats/mstats_extras.py?rev=6392)
"""
def _idf(data):
x = data.compressed()
n = len(x)
if n < 3:
return [numpy_nan, numpy_nan]
(j, h) = divmod(n / 4. + 5 / 12., 1)
qlo = (1 - h) * x[j - 1] + h * x[j]
k = n - j
qup = (1 - h) * x[k] + h * x[k - 1]
return [qlo, qup]
data = numpy_sort(data, axis=axis).view(MaskedArray)
if (axis is None):
return _idf(data)
else:
return apply_along_axis(_idf, axis, data)