| import pandas as pd | |
| def create_general_exposures(df: pd.DataFrame, entrants: list = None): | |
| check_cols = ['salary', 'actual_fpts', 'actual_own', 'dupes'] | |
| general_exposures = pd.DataFrame() | |
| for each_col in check_cols: | |
| general_frame = pd.DataFrame() | |
| if entrants is not None: | |
| overall_general = pd.Series(list(df[df['BaseName'].isin(entrants)][each_col])).sum() | |
| else: | |
| overall_general = pd.Series(list(df[each_col])).sum() | |
| top_1per_general = pd.Series(list(df[df['percentile_finish'] <= 0.01][each_col])).sum() | |
| top_5per_general = pd.Series(list(df[df['percentile_finish'] <= 0.05][each_col])).sum() | |
| top_10per_general = pd.Series(list(df[df['percentile_finish'] <= 0.10][each_col])).sum() | |
| top_20per_general = pd.Series(list(df[df['percentile_finish'] <= 0.20][each_col])).sum() | |
| general_contest_len = len(df) | |
| general_len_1per = len(df[df['percentile_finish'] <= 0.01]) | |
| general_len_5per = len(df[df['percentile_finish'] <= 0.05]) | |
| general_len_10per = len(df[df['percentile_finish'] <= 0.10]) | |
| general_len_20per = len(df[df['percentile_finish'] <= 0.20]) | |
| each_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%'] | |
| each_general_set = [overall_general, top_1per_general, top_5per_general, top_10per_general, top_20per_general] | |
| each_general_len_set = [general_contest_len, general_len_1per, general_len_5per, general_len_10per, general_len_20per] | |
| general_count_var = 0 | |
| for each_general in each_general_set: | |
| general_frame['Stat'] = [each_col] | |
| general_frame['Average'] = [each_general / each_general_len_set[general_count_var]] | |
| general_frame = general_frame.rename(columns={'Average': f'Average {each_set_name[general_count_var]}'}) | |
| if len(general_exposures) == 0: | |
| general_row = general_frame | |
| else: | |
| general_row = pd.merge(general_exposures, general_frame, on='Stat', how='outer') | |
| general_count_var += 1 | |
| if len(general_exposures) == 0: | |
| general_exposures = general_row | |
| else: | |
| general_exposures = pd.concat([general_exposures, general_frame], ignore_index = True, axis = 0) | |
| general_exposures = general_exposures.replace(['salary', 'actual_fpts', 'actual_own', 'dupes'], ['Salary Used', 'Finishing Points', 'Total Ownership', 'Duplications']) | |
| return general_exposures |