Refactor stack metrics calculations in app.py

- Renamed variables for clarity, changing 'contest_len' and related length variables to 'stacks_contest_len' and 'stacks_len_*' to better reflect their purpose in stack metrics calculations.
- Updated the logic for processing stack data in session state, ensuring consistent naming and improving readability in the dataframe presentation of exposure metrics.

app.py

@@ -303,24 +303,24 @@ with tab2:
                     top_5per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.05]['stack'])).value_counts()
                     top_10per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.10]['stack'])).value_counts()
                     top_20per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.20]['stack'])).value_counts()
-                    contest_len = len(working_df)
-                    len_1per = len(working_df[working_df['percentile_finish'] <= 0.01])
-                    len_5per = len(working_df[working_df['percentile_finish'] <= 0.05])
-                    len_10per = len(working_df[working_df['percentile_finish'] <= 0.10])
-                    len_20per = len(working_df[working_df['percentile_finish'] <= 0.20])
+                    stacks_contest_len = len(working_df)
+                    stacks_len_1per = len(working_df[working_df['percentile_finish'] <= 0.01])
+                    stacks_len_5per = len(working_df[working_df['percentile_finish'] <= 0.05])
+                    stacks_len_10per = len(working_df[working_df['percentile_finish'] <= 0.10])
+                    stacks_len_20per = len(working_df[working_df['percentile_finish'] <= 0.20])
                     each_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%']
-                    each_frame_set = [overall_stacks, top_1per_stacks, top_5per_stacks, top_10per_stacks, top_20per_stacks]
-                    each_len_set = [contest_len, len_1per, len_5per, len_10per, len_20per]
+                    each_stacks_set = [overall_stacks, top_1per_stacks, top_5per_stacks, top_10per_stacks, top_20per_stacks]
+                    each_stacks_len_set = [stacks_contest_len, stacks_len_1per, stacks_len_5per, stacks_len_10per, stacks_len_20per]
                     stack_count_var = 0
-                    for each_set in each_frame_set:
-                        set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
-                        set_frame['Percent'] = set_frame['Count'] / each_len_set[stack_count_var]
-                        set_frame = set_frame[['Stack', 'Percent']]
-                        set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[stack_count_var]}'})
+                    for each_stack in each_stacks_set:
+                        stack_frame = each_stack.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
+                        stack_frame['Percent'] = stack_frame['Count'] / each_stacks_len_set[stack_count_var]
+                        stack_frame = stack_frame[['Stack', 'Percent']]
+                        stack_frame = stack_frame.rename(columns={'Percent': f'Exposure {each_set_name[stack_count_var]}'})
                         if 'stack_frame' not in st.session_state:
-                            st.session_state['stack_frame'] = set_frame
+                            st.session_state['stack_frame'] = stack_frame
                         else:
-                            st.session_state['stack_frame'] = pd.merge(st.session_state['stack_frame'], set_frame, on='Stack', how='outer')
+                            st.session_state['stack_frame'] = pd.merge(st.session_state['stack_frame'], stack_frame, on='Stack', how='outer')
                         stack_count_var += 1
                     st.dataframe(st.session_state['stack_frame'].
                         sort_values(by='Exposure Overall', ascending=False).
@@ -333,24 +333,24 @@ with tab2:
                     top_5per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.05]['stack'])).value_counts()
                     top_10per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.10]['stack'])).value_counts()
                     top_20per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.20]['stack'])).value_counts()
-                    contest_len = len(working_df)
-                    len_1per = len(working_df[working_df['percentile_finish'] <= 0.01])
-                    len_5per = len(working_df[working_df['percentile_finish'] <= 0.05])
-                    len_10per = len(working_df[working_df['percentile_finish'] <= 0.10])
-                    len_20per = len(working_df[working_df['percentile_finish'] <= 0.20])
+                    stacks_contest_len = len(working_df)
+                    stacks_len_1per = len(working_df[working_df['percentile_finish'] <= 0.01])
+                    stacks_len_5per = len(working_df[working_df['percentile_finish'] <= 0.05])
+                    stacks_len_10per = len(working_df[working_df['percentile_finish'] <= 0.10])
+                    stacks_len_20per = len(working_df[working_df['percentile_finish'] <= 0.20])
                     each_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%']
-                    each_frame_set = [overall_stacks, top_1per_stacks, top_5per_stacks, top_10per_stacks, top_20per_stacks]
-                    each_len_set = [contest_len, len_1per, len_5per, len_10per, len_20per]
+                    each_stacks_set = [overall_stacks, top_1per_stacks, top_5per_stacks, top_10per_stacks, top_20per_stacks]
+                    each_stacks_len_set = [stacks_contest_len, stacks_len_1per, stacks_len_5per, stacks_len_10per, stacks_len_20per]
                     stack_count_var = 0
-                    for each_set in each_frame_set:
-                        set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
-                        set_frame['Percent'] = set_frame['Count'] / each_len_set[stack_count_var]
-                        set_frame = set_frame[['Stack', 'Percent']]
-                        set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[stack_count_var]}'})
+                    for each_stack in each_stacks_set:
+                        stack_frame = each_stack.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
+                        stack_frame['Percent'] = stack_frame['Count'] / each_stacks_len_set[stack_count_var]
+                        stack_frame = stack_frame[['Stack', 'Percent']]
+                        stack_frame = stack_frame.rename(columns={'Percent': f'Exposure {each_set_name[stack_count_var]}'})
                         if 'stack_frame' not in st.session_state:
-                            st.session_state['stack_frame'] = set_frame
+                            st.session_state['stack_frame'] = stack_frame
                         else:
-                            st.session_state['stack_frame'] = pd.merge(st.session_state['stack_frame'], set_frame, on='Stack', how='outer')
+                            st.session_state['stack_frame'] = pd.merge(st.session_state['stack_frame'], stack_frame, on='Stack', how='outer')
                         stack_count_var += 1
                     st.dataframe(st.session_state['stack_frame'].
                         sort_values(by='Exposure Overall', ascending=False).