Refactor contest data handling in app.py for improved clarity and functionality

- Updated references from 'contest_df' to 'Contest' in session state to maintain consistency with naming conventions.
- Enhanced the logic for applying filters and calculating metrics based on game type, ensuring accurate data processing.
- Streamlined the process of creating a working copy of the contest dataframe for calculations, improving data handling and user experience.

app.py

@@ -76,12 +76,12 @@ with tab1:
                 st.session_state['projections_df'] = projections.copy()
                 st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int))
                 # Run name matching only once when first loading the files
-                st.session_state['contest_df'], st.session_state['projections_df'] = find_name_mismatches(st.session_state['Contest'], st.session_state['projections_df'])
+                st.session_state['Contest'], st.session_state['projections_df'] = find_name_mismatches(st.session_state['Contest'], st.session_state['projections_df'])
 
 with tab2:
     if st.button('Clear data', key='reset3'):
         st.session_state.clear()
-    if 'contest_df' in st.session_state and 'projections_df' in st.session_state:
+    if 'Contest' in st.session_state and 'projections_df' in st.session_state:
         col1, col2 = st.columns([1, 8])
         excluded_cols = ['BaseName', 'EntryCount']
         
@@ -97,6 +97,8 @@ with tab2:
             'cpt_proj_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
             'cpt_own_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
         }
+        # Create a copy of the dataframe for calculations
+        working_df = st.session_state['Contest'].copy()
 
         with col1:
             with st.form(key='filter_form'):
@@ -104,40 +106,32 @@ with tab2:
                 entry_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific'])
                 entry_names = st.multiselect("Select players", options=st.session_state['entry_list'], default=[])
                 submitted = st.form_submit_button("Submit")
-
-        # Create a copy of the dataframe for calculations
-        working_df = st.session_state['contest_df'].copy()
-
-        # Apply filters and calculations only after form submission
-        if submitted:
-            # Apply entry name filter if specific entries are selected
-            if entry_parse_var == 'Specific' and entry_names:
-                working_df = working_df[working_df['BaseName'].isin(entry_names)]
-
-            # Calculate metrics based on game type
-            if type_var == 'Classic':
-                working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
-                working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
-                working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
-            elif type_var == 'Showdown':
-                working_df['salary'] = working_df.apply(
-                    lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
-                              sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
-                    axis=1
-                )
-                working_df['median'] = working_df.apply(
-                    lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
-                              sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
-                    axis=1
-                )
-                working_df['Own'] = working_df.apply(
-                    lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
-                              sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
-                    axis=1
-                )
-
-            # Reset pagination when new filters are applied
-            st.session_state.current_page = 0
+                if submitted:
+                    # Apply entry name filter if specific entries are selected
+                    if entry_parse_var == 'Specific' and entry_names:
+                        working_df = working_df[working_df['BaseName'].isin(entry_names)]
+
+        # Calculate metrics based on game type
+        if type_var == 'Classic':
+            working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
+            working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
+            working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+        elif type_var == 'Showdown':
+            working_df['salary'] = working_df.apply(
+                lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
+                            sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
+                axis=1
+            )
+            working_df['median'] = working_df.apply(
+                lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
+                            sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
+                axis=1
+            )
+            working_df['Own'] = working_df.apply(
+                lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
+                            sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
+                axis=1
+            )
 
         # Initialize pagination in session state if not exists
         if 'current_page' not in st.session_state: