James McCool
commited on
Commit
·
38ab7f8
1
Parent(s):
53f8288
Refactor contest data handling in app.py for consistency and clarity
Browse files- Updated references from 'Contest' to 'contest_df' in session state to align with naming conventions.
- Adjusted calculations for salary, median, and ownership metrics to use the new 'contest_df' variable, ensuring accurate data processing.
- Enhanced pagination and display logic to reflect changes in data structure, improving user experience and data visualization.
app.py
CHANGED
@@ -82,7 +82,7 @@ with tab1:
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with tab2:
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if st.button('Clear data', key='reset3'):
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st.session_state.clear()
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-
if '
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col1, col2 = st.columns([1, 8])
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excluded_cols = ['BaseName', 'EntryCount']
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with col1:
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@@ -106,31 +106,31 @@ with tab2:
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}
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if entry_parse_var == 'Specific':
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-
st.session_state['
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else:
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-
st.session_state['
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if type_var == 'Classic':
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-
st.session_state['
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-
st.session_state['
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-
st.session_state['
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elif type_var == 'Showdown':
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# Calculate salary (CPT uses cpt_salary_map, others use salary_map)
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-
st.session_state['
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lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) +
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sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
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axis=1
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)
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# Calculate median (CPT uses cpt_proj_map, others use proj_map)
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-
st.session_state['
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lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) +
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sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
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axis=1
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)
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# Calculate ownership (CPT uses cpt_own_map, others use own_map)
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-
st.session_state['
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lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) +
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sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
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axis=1
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@@ -144,7 +144,7 @@ with tab2:
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# Calculate total pages
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rows_per_page = 500
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-
total_rows = len(st.session_state['
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total_pages = (total_rows + rows_per_page - 1) // rows_per_page
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# Create pagination controls in a single row
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@@ -164,7 +164,7 @@ with tab2:
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# Display the paginated dataframe
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st.dataframe(
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-
st.session_state['
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.background_gradient(axis=0)
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.background_gradient(cmap='RdYlGn')
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.format(precision=2),
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with tab2:
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if st.button('Clear data', key='reset3'):
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st.session_state.clear()
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+
if 'contest_df' in st.session_state and 'projections_df' in st.session_state:
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col1, col2 = st.columns([1, 8])
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excluded_cols = ['BaseName', 'EntryCount']
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with col1:
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}
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if entry_parse_var == 'Specific':
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+
st.session_state['contest_df'] = st.session_state['contest_df'][st.session_state['contest_df']['BaseName'].isin(entry_names)]
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else:
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+
st.session_state['contest_df'] = st.session_state['contest_df']
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if type_var == 'Classic':
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st.session_state['contest_df']['salary'] = st.session_state['contest_df'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
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+
st.session_state['contest_df']['median'] = st.session_state['contest_df'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
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+
st.session_state['contest_df']['Own'] = st.session_state['contest_df'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
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elif type_var == 'Showdown':
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# Calculate salary (CPT uses cpt_salary_map, others use salary_map)
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+
st.session_state['contest_df']['salary'] = st.session_state['contest_df'].apply(
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lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) +
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sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
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axis=1
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)
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# Calculate median (CPT uses cpt_proj_map, others use proj_map)
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st.session_state['contest_df']['median'] = st.session_state['contest_df'].apply(
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lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) +
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sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
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axis=1
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)
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# Calculate ownership (CPT uses cpt_own_map, others use own_map)
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st.session_state['contest_df']['Own'] = st.session_state['contest_df'].apply(
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lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) +
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sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
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axis=1
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# Calculate total pages
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rows_per_page = 500
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total_rows = len(st.session_state['contest_df'])
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total_pages = (total_rows + rows_per_page - 1) // rows_per_page
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# Create pagination controls in a single row
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# Display the paginated dataframe
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st.dataframe(
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+
st.session_state['contest_df'].iloc[start_idx:end_idx].style
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.background_gradient(axis=0)
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.background_gradient(cmap='RdYlGn')
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.format(precision=2),
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