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import pandas as pd
def create_player_exposures(df: pd.DataFrame, player_columns: list, entrants: list = None):
player_frame = pd.DataFrame()
if entrants is not None:
overall_players = pd.Series(list(df[df['BaseName'].isin(entrants)][player_columns].values.flatten())).value_counts()
else:
overall_players = pd.Series(list(df[player_columns].values.flatten())).value_counts()
top_1per_players = pd.Series(list(df[df['percentile_finish'] <= 0.01][player_columns].values.flatten())).value_counts()
top_5per_players = pd.Series(list(df[df['percentile_finish'] <= 0.05][player_columns].values.flatten())).value_counts()
top_10per_players = pd.Series(list(df[df['percentile_finish'] <= 0.10][player_columns].values.flatten())).value_counts()
top_20per_players = pd.Series(list(df[df['percentile_finish'] <= 0.20][player_columns].values.flatten())).value_counts()
contest_len = len(df)
len_1per = len(df[df['percentile_finish'] <= 0.01])
len_5per = len(df[df['percentile_finish'] <= 0.05])
len_10per = len(df[df['percentile_finish'] <= 0.10])
len_20per = len(df[df['percentile_finish'] <= 0.20])
each_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%']
each_frame_set = [overall_players, top_1per_players, top_5per_players, top_10per_players, top_20per_players]
each_len_set = [contest_len, len_1per, len_5per, len_10per, len_20per]
player_count_var = 0
for each_set in each_frame_set:
set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
set_frame['Percent'] = set_frame['Count'] / each_len_set[player_count_var]
set_frame = set_frame[['Player', 'Percent']]
set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[player_count_var]}'})
if len(player_frame) == 0:
player_frame = set_frame
else:
player_frame = pd.merge(player_frame, set_frame, on='Player', how='outer')
player_count_var += 1
return player_frame |