def get_team_combo_corsi(season, team, games, n_players=2):
"""
Gets combo Corsi for team for specified games
:param season: int, the season
:param team: int or str, team
:param games: int or iterable of int, games
:param n_players: int. E.g. 1 gives you player TOI, 2 gives you 2-player group TOI, 3 makes 3-player groups, etc
:return: dataframe
"""
if helpers.check_number(games):
games = [games]
teamid = team_info.team_as_id(team)
corsi = teams.get_team_pbp(season, team)
corsi = corsi.assign(_Secs=corsi.Time) \
.merge(pd.DataFrame({'Game': games}), how='inner', on='Game') \
.pipe(manip.filter_for_five_on_five) \
.pipe(manip.filter_for_corsi) \
[['Game', 'Time', 'Team', '_Secs']] \
.pipe(onice.add_onice_players_to_df, focus_team=team, season=season, gamecol='Game')
cols_to_drop = ['Opp{0:d}'.format(i) for i in range(1, 7)] + ['{0:s}6'.format(team_info.team_as_str(team))]
corsi = corsi.drop(cols_to_drop, axis=1) \
.pipe(helpers.melt_helper, id_vars=['Game', 'Time', 'Team'], var_name='P', value_name='PlayerID') \
.drop('P', axis=1)
corsi2 = None
for i in range(n_players):
corsitemp = corsi.rename(columns={'PlayerID': 'PlayerID' + str(i+1)})
if corsi2 is None:
corsi2 = corsitemp
else:
corsi2 = corsi2.merge(corsitemp, how='inner', on=['Game', 'Time', 'Team'])
# Assign CF and CA
teamid = team_info.team_as_id(team)
corsi2.loc[:, 'CF'] = corsi2.Team.apply(lambda x: 1 if x == teamid else 0)
corsi2.loc[:, 'CA'] = corsi2.Team.apply(lambda x: 0 if x == teamid else 1)
corsi2 = corsi2.drop({'Game', 'Time', 'Team'}, axis=1)
# Group by players and count
groupcols = ['PlayerID' + str(i+1) for i in range(n_players)]
grouped = corsi2 \
.groupby(groupcols, as_index=False) \
.sum() \
.rename(columns={'Time': 'Secs'})
# Convert to all columns
allcombos = manip.convert_to_all_combos(grouped, 0, *groupcols)
return allcombos
def _combo_corsi_from_hrcodes(homedf=None, roaddf=None, corsidf=None, cfca=None, *hrcodes):
"""
Joins the homedf and roaddf as specified by hrcodes.
:param homedf: home team df (e.g. for TOI)
:param roaddf: road team df (e.g. for TOI)
:param corsidf: a dataframe with Time and HomeCorsi (1 or -1), one row per event
:param hrcodes: to limit exploding joins, specify strings containing 'H' and 'R' and 'A', each of length player_n
For example, if player_n=3, specify 'HHH' to only get home team player combos.
If this is left unspecified, will do all combos, which can be problematic when player_n > 3.
'R' for road, 'H' for home, 'A' for all (both)
:return: joined df, grouped and summed by player combos
"""
alldf = pd.concat([homedf, roaddf])
dflst = []
for hrcode in hrcodes:
dfs_to_join = []
for i in range(len(hrcode)):
if hrcode[i].upper() == 'H':
dfs_to_join.append(homedf)
elif hrcode[i].lower() == 'R':
dfs_to_join.append(roaddf)
else:
dfs_to_join.append(alldf)
gamedf = None
for i, df in enumerate(dfs_to_join):
if gamedf is None:
gamedf = df
else:
# Drop duplicates so, e.g. if you have 2 shots in a second, final df registers that twice, not 4x
gamedf = gamedf.merge(df.drop_duplicates(), how='inner', on='Time', suffixes=['', str(i + 1)])
gamedf = gamedf.rename(columns={'PlayerID': 'PlayerID1', 'Team': 'Team1'}) \
.merge(corsidf, how='inner', on='Time') \
.drop('Time', axis=1)
if cfca is None:
gamedf.loc[gamedf.Team1 == 'R', 'HomeCorsi'] = gamedf.loc[gamedf.Team1 == 'R', 'HomeCorsi'] * -1
gamedf = gamedf.groupby([col for col in gamedf.columns if col != 'HomeCorsi'], as_index=False).sum()
# One last to-do: make sure I have all possible pairs of players covered
combocols = tuple([('PlayerID' + str(x), 'Team' + str(x)) for x in range(1, len(hrcodes[0]) + 1)])
allcombos = manip.convert_to_all_combos(gamedf, 0, *combocols)
dflst.append(allcombos)
return pd.concat(dflst)
def insert_missing_team_games(df, **kwargs):
"""
:param df: dataframe, 5v5 player log or part of it
:param kwargs: relevant ones are 'team' and 'add_missing_games'
:return: dataframe with added rows
"""
if 'add_missing_games' in kwargs and 'team' in kwargs and kwargs['add_missing_games'] is True:
_, enddate = get_startdate_enddate_from_kwargs(**kwargs)
df2 = manip.convert_to_all_combos(df, np.NaN, ('Season', 'Game'), 'PlayerID')
df2 = schedules.attach_game_dates_to_dateframe(df2).sort_values('Date')
# Don't use the team kwarg here but this will obviously be messy if we bring in multiple teams' games
# And get_and_filter_5v5_log does filter for team up above
return df2
return df
def _combo_secs_from_hrcodes(homedf=None, roaddf=None, *hrcodes):
"""
Joins the homedf and roaddf as specified by hrcodes.
:param homedf: home team df (e.g. for TOI)
:param roaddf: road team df (e.g. for TOI)
:param hrcodes: to limit exploding joins, specify strings containing 'H' and 'R' and 'A', each of length player_n
For example, if player_n=3, specify 'HHH' to only get home team player combos.
If this is left unspecified, will do all combos, which can be problematic when player_n > 3.
'R' for road, 'H' for home, 'A' for all (both)
:return: joined df, grouped and summed
"""
alldf = pd.concat([homedf, roaddf])
dflst = []
for hrcode in hrcodes:
dfs_to_join = []
for i in range(len(hrcode)):
if hrcode[i].upper() == 'H':
dfs_to_join.append(homedf)
elif hrcode[i].lower() == 'R':
dfs_to_join.append(roaddf)
else:
dfs_to_join.append(alldf)
gamedf = None
for i, df in enumerate(dfs_to_join):
if gamedf is None:
gamedf = df
else:
gamedf = gamedf.merge(df, how='inner', on='Time', suffixes=['', str(i + 1)])
gamedf = gamedf.rename(columns={'PlayerID': 'PlayerID1', 'Team': 'Team1'}) \
.assign(Secs=1) \
.drop('Time', axis=1)
gamedf = gamedf.groupby([col for col in gamedf.columns if col != 'Secs'], as_index=False).count()
# One last to-do: make sure I have all possible pairs of players covered
combocols = tuple([('PlayerID' + str(x), 'Team' + str(x)) for x in range(1, len(hrcodes[0]) + 1)])
allcombos = manip.convert_to_all_combos(gamedf, 0, *combocols)
allcombos.loc[:, 'Min'] = allcombos.Secs / 60
dflst.append(allcombos)
return pd.concat(dflst)
def get_team_combo_toi(season, team, games, n_players=2):
"""
Gets 5v5 combo TOI for team for specified games
:param season: int, the season
:param team: int or str, team
:param games: int or iterable of int, games
:param n_players: int. E.g. 1 gives you player TOI, 2 gives you 2-player group TOI, 3 makes 3-player groups, etc
:return: dataframe
"""
if helpers.check_number(games):
games = [games]
toi = teams.get_team_toi(season, team) \
.merge(pd.DataFrame({'Game': games}), how='inner', on='Game') \
.pipe(manip.filter_for_five_on_five) \
[['Game', 'Time', 'Team1', 'Team2', 'Team3', 'Team4', 'Team5']] \
.pipe(helpers.melt_helper, id_vars=['Game', 'Time'], var_name='P', value_name='PlayerID') \
.drop('P', axis=1)
toi2 = None
for i in range(n_players):
toitemp = toi.rename(columns={'PlayerID': 'PlayerID' + str(i+1)})
if toi2 is None:
toi2 = toitemp
else:
toi2 = toi2.merge(toitemp, how='inner', on=['Game', 'Time'])
# Group by players and count
groupcols = ['PlayerID' + str(i+1) for i in range(n_players)]
grouped = toi2.drop('Game', axis=1) \
.groupby(groupcols, as_index=False) \
.count() \
.rename(columns={'Time': 'Secs'})
# Convert to all columns
allcombos = manip.convert_to_all_combos(grouped, 0, *groupcols)
return allcombos