Enhance general exposures calculation in create_general_exposures.py

- Added 'uniques' and 'under_5' to the list of columns checked for general exposures, improving the data analysis by incorporating additional metrics.
- Updated the naming in the output DataFrame to reflect these new metrics, enhancing clarity in the results.

global_func/create_general_exposures.py

@@ -1,7 +1,7 @@
 import pandas as pd
 
 def create_general_exposures(df: pd.DataFrame, entrants: list = None):
-    check_cols = ['salary', 'actual_fpts', 'actual_own', 'dupes']
+    check_cols = ['salary', 'actual_fpts', 'actual_own', 'dupes', 'uniques', 'under_5']
     general_exposures = pd.DataFrame()
     for each_col in check_cols:
         general_frame = pd.DataFrame()
@@ -19,7 +19,7 @@ def create_general_exposures(df: pd.DataFrame, entrants: list = None):
         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_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%', 'Uniques', 'Under 5']
         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
@@ -39,5 +39,5 @@ def create_general_exposures(df: pd.DataFrame, entrants: list = None):
             general_exposures = general_row
         else:
             general_exposures = pd.concat([general_exposures, general_frame], ignore_index = True, axis = 0)
-    general_exposures['Stat'] = general_exposures['Stat'].replace(['salary', 'actual_fpts', 'actual_own', 'dupes'], ['Salary Used', 'Finishing Points', 'Total Ownership', 'Duplications'])
+    general_exposures['Stat'] = general_exposures['Stat'].replace(['salary', 'actual_fpts', 'actual_own', 'dupes', 'uniques', 'under_5'], ['Salary Used', 'Finishing Points', 'Total Ownership', 'Duplications', 'Uniques', 'Under 5'])
     return general_exposures