Refactor pagination and data display in app.py

- Improved the pagination logic by ensuring consistent handling of session state for current page navigation.
- Streamlined the creation of pagination controls and data display, enhancing user interaction and clarity.
- Maintained functionality for displaying player and stack information across multiple tabs, ensuring a cohesive user experience.

app.py

@@ -191,127 +191,127 @@ with tab2:
                 elif entry_parse_var == 'All':
                     st.session_state['calc_toggle'] = True
         
-        # Initialize pagination in session state if not exists
-        if 'current_page' not in st.session_state:
-            st.session_state.current_page = 1
+    # Initialize pagination in session state if not exists
+    if 'current_page' not in st.session_state:
+        st.session_state.current_page = 1
 
-        # Calculate total pages
-        rows_per_page = 500
-        total_rows = len(working_df)
-        total_pages = (total_rows + rows_per_page - 1) // rows_per_page
+    # Calculate total pages
+    rows_per_page = 500
+    total_rows = len(working_df)
+    total_pages = (total_rows + rows_per_page - 1) // rows_per_page
 
-        # Create pagination controls in a single row
-        pagination_cols = st.columns([4, 1, 1, 1, 4])
-        with pagination_cols[1]:
-            if st.button(f"Previous Page"):
-                if st.session_state['current_page'] > 1:
-                    st.session_state.current_page -= 1
-                else:
-                    st.session_state.current_page = 1
-                    if 'player_frame' in st.session_state:
-                        del st.session_state['player_frame']
-                    if 'stack_frame' in st.session_state:
-                        del st.session_state['stack_frame']
-
-        with pagination_cols[3]:
-            if st.button(f"Next Page"):
-                st.session_state.current_page += 1
+    # Create pagination controls in a single row
+    pagination_cols = st.columns([4, 1, 1, 1, 4])
+    with pagination_cols[1]:
+        if st.button(f"Previous Page"):
+            if st.session_state['current_page'] > 1:
+                st.session_state.current_page -= 1
+            else:
+                st.session_state.current_page = 1
                 if 'player_frame' in st.session_state:
                     del st.session_state['player_frame']
                 if 'stack_frame' in st.session_state:
                     del st.session_state['stack_frame']
 
-        # Calculate start and end indices for current page
-        start_idx = (st.session_state.current_page - 1) * rows_per_page
-        end_idx = min((st.session_state.current_page) * rows_per_page, total_rows)
-        st.dataframe(
-            working_df.iloc[start_idx:end_idx].style
-            .background_gradient(axis=0)
-            .background_gradient(cmap='RdYlGn')
-            .format(precision=2), 
-            height=500,
-            use_container_width=True,
-            hide_index=True
-        )
+    with pagination_cols[3]:
+        if st.button(f"Next Page"):
+            st.session_state.current_page += 1
+            if 'player_frame' in st.session_state:
+                del st.session_state['player_frame']
+            if 'stack_frame' in st.session_state:
+                del st.session_state['stack_frame']
+
+    # Calculate start and end indices for current page
+    start_idx = (st.session_state.current_page - 1) * rows_per_page
+    end_idx = min((st.session_state.current_page) * rows_per_page, total_rows)
+    st.dataframe(
+        working_df.iloc[start_idx:end_idx].style
+        .background_gradient(axis=0)
+        .background_gradient(cmap='RdYlGn')
+        .format(precision=2), 
+        height=500,
+        use_container_width=True,
+        hide_index=True
+    )
 
-        with st.container():
-            tab1, tab2, tab3 = st.tabs(['Player Used Info', 'Stack Used Info', 'Duplication Info'])
-            with tab1:
-                st.session_state['field_frame'] = create_player_exposures(working_df, player_columns)
-                if entry_parse_var == 'All':
-                    st.session_state['player_frame'] = create_player_exposures(working_df, player_columns)
-                    st.dataframe(st.session_state['player_frame'].
-                        sort_values(by='Exposure Overall', ascending=False).
-                        style.background_gradient(cmap='RdYlGn').
-                        format(formatter='{:.2%}', subset=st.session_state['player_frame'].select_dtypes(include=['number']).columns),
-                        hide_index=True)
-                else:
-                    st.session_state['player_frame'] = create_player_exposures(working_df, player_columns, entry_names)
-                    st.dataframe(st.session_state['player_frame'].
-                        sort_values(by='Exposure Overall', ascending=False).
-                        style.background_gradient(cmap='RdYlGn').
-                        format(formatter='{:.2%}', subset=st.session_state['player_frame'].select_dtypes(include=['number']).columns),
-                        hide_index=True)
-            with tab2:
-                if entry_parse_var == 'All':
-                    overall_stacks = pd.Series(list(working_df['stack'])).value_counts()
-                    top_1per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.01]['stack'])).value_counts()
-                    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()
-                    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_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_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'] = stack_frame
-                        else:
-                            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).
-                        style.background_gradient(cmap='RdYlGn').
-                        format(formatter='{:.2%}', subset=st.session_state['stack_frame'].select_dtypes(include=['number']).columns),
-                        hide_index=True)
-                else:
-                    overall_stacks = pd.Series(list(working_df[working_df['BaseName'].isin(entry_names)]['stack'])).value_counts()
-                    top_1per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.01]['stack'])).value_counts()
-                    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()
-                    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_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_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'] = stack_frame
-                        else:
-                            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).
-                        style.background_gradient(cmap='RdYlGn').
-                        format(formatter='{:.2%}', subset=st.session_state['stack_frame'].select_dtypes(include=['number']).columns),
-                        hide_index=True)
-            with tab3:
-                st.write('holding')
+    with st.container():
+        tab1, tab2, tab3 = st.tabs(['Player Used Info', 'Stack Used Info', 'Duplication Info'])
+        with tab1:
+            st.session_state['field_frame'] = create_player_exposures(working_df, player_columns)
+            if entry_parse_var == 'All':
+                st.session_state['player_frame'] = create_player_exposures(working_df, player_columns)
+                st.dataframe(st.session_state['player_frame'].
+                    sort_values(by='Exposure Overall', ascending=False).
+                    style.background_gradient(cmap='RdYlGn').
+                    format(formatter='{:.2%}', subset=st.session_state['player_frame'].select_dtypes(include=['number']).columns),
+                    hide_index=True)
+            else:
+                st.session_state['player_frame'] = create_player_exposures(working_df, player_columns, entry_names)
+                st.dataframe(st.session_state['player_frame'].
+                    sort_values(by='Exposure Overall', ascending=False).
+                    style.background_gradient(cmap='RdYlGn').
+                    format(formatter='{:.2%}', subset=st.session_state['player_frame'].select_dtypes(include=['number']).columns),
+                    hide_index=True)
+        with tab2:
+            if entry_parse_var == 'All':
+                overall_stacks = pd.Series(list(working_df['stack'])).value_counts()
+                top_1per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.01]['stack'])).value_counts()
+                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()
+                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_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_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'] = stack_frame
+                    else:
+                        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).
+                    style.background_gradient(cmap='RdYlGn').
+                    format(formatter='{:.2%}', subset=st.session_state['stack_frame'].select_dtypes(include=['number']).columns),
+                    hide_index=True)
+            else:
+                overall_stacks = pd.Series(list(working_df[working_df['BaseName'].isin(entry_names)]['stack'])).value_counts()
+                top_1per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.01]['stack'])).value_counts()
+                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()
+                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_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_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'] = stack_frame
+                    else:
+                        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).
+                    style.background_gradient(cmap='RdYlGn').
+                    format(formatter='{:.2%}', subset=st.session_state['stack_frame'].select_dtypes(include=['number']).columns),
+                    hide_index=True)
+        with tab3:
+            st.write('holding')