James McCool
commited on
Commit
·
59dc088
1
Parent(s):
baabd98
Refactor game type handling and player filtering in app.py
Browse files- Updated the logic for calculating metrics based on game type to utilize a working dataframe instead of directly modifying the session state 'Contest' dataframe, improving code clarity and maintainability.
- Enhanced the player filtering process by integrating it into the game type selection, allowing for more dynamic user interactions and better data management.
- This change streamlines the overall data processing workflow, ensuring accurate calculations and improved user experience.
app.py
CHANGED
@@ -101,98 +101,89 @@ with tab2:
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'cpt_proj_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
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'cpt_own_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
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}
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-
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st.session_state['
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# Calculate metrics based on game type
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if
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-
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lambda row: Counter(
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map_dict['team_map'].get(player, '') for player in row[4:]
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if map_dict['team_map'].get(player, '') != ''
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).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
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axis=1
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)
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-
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lambda row: Counter(
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map_dict['team_map'].get(player, '') for player in row[4:]
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if map_dict['team_map'].get(player, '') != ''
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).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
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axis=1
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)
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-
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-
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lambda row: ','.join(sorted(row.values)),
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axis=1
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)
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-
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-
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elif
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-
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lambda row: Counter(
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map_dict['team_map'].get(player, '') for player in row
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if map_dict['team_map'].get(player, '') != ''
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).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row) else '',
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axis=1
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)
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-
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lambda row: Counter(
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map_dict['team_map'].get(player, '') for player in row
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if map_dict['team_map'].get(player, '') != ''
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).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row) else '',
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axis=1
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)
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-
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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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-
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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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-
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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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)
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-
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lambda row: row[0] + '|' + ','.join(sorted(row[1:].values)),
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axis=1
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)
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-
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# Create a copy of the dataframe for calculations
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for col in player_columns:
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contest_players = st.session_state['Contest'].copy()
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players_1per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.01))
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players_5per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.05))
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players_10per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.10))
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players_20per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.20))
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working_df = st.session_state['Contest'].copy()
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-
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with col1:
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with st.expander("Info and filters"):
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if st.button('Clear data', key='reset3'):
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st.session_state.clear()
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with st.form(key='filter_form'):
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st.session_state['type_var'] = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
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entry_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific'])
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entry_names = st.multiselect("Select players", options=st.session_state['entry_list'], default=[])
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submitted = st.form_submit_button("Submit")
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if submitted:
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if 'player_frame' in st.session_state:
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del st.session_state['player_frame']
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del st.session_state['stack_frame']
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# Apply entry name filter if specific entries are selected
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if entry_parse_var == 'Specific' and entry_names:
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working_df = working_df[working_df['BaseName'].isin(entry_names)]
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# Initialize pagination in session state if not exists
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if 'current_page' not in st.session_state:
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@@ -235,6 +226,17 @@ with tab2:
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hide_index=True
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)
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player_counts = pd.Series(list(contest_players[player_columns].values.flatten())).value_counts()
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player_1per_counts = pd.Series(list(players_1per[player_columns].values.flatten())).value_counts()
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player_5per_counts = pd.Series(list(players_5per[player_columns].values.flatten())).value_counts()
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@@ -252,13 +254,15 @@ with tab2:
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dupe_20per_counts = pd.Series(list(players_20per['dupes'])).value_counts()
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each_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%']
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each_frame_set = [contest_players, players_1per, players_5per, players_10per, players_20per]
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with st.container():
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tab1, tab2, tab3 = st.tabs(['Player Used Info', 'Stack Used Info', 'Duplication Info'])
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with tab1:
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player_count_var = 0
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for each_set in [player_counts, player_1per_counts, player_5per_counts, player_10per_counts, player20_per_counts]:
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set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
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-
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set_frame = set_frame[['Player', 'Percent']]
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set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[player_count_var]}'})
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if 'player_frame' not in st.session_state:
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@@ -275,7 +279,8 @@ with tab2:
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stack_count_var = 0
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for each_set in [stack_counts, stack_1per_counts, stack_5per_counts, stack_10per_counts, stack_20per_counts]:
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set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
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-
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set_frame = set_frame[['Stack', 'Percent']]
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set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[stack_count_var]}'})
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if 'stack_frame' not in st.session_state:
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@@ -292,7 +297,7 @@ with tab2:
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dupe_count_var = 0
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for each_set in [dupe_counts, dupe_1per_counts, dupe_5per_counts, dupe_10per_counts, dupe_20per_counts]:
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set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Dupes', 'count': 'Count'})
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set_frame['Percent'] = set_frame['Count'] /
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set_frame = set_frame[['Dupes', 'Percent']]
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set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[dupe_count_var]}'})
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if 'dupe_frame' not in st.session_state:
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'cpt_proj_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
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'cpt_own_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
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}
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+
# Create a copy of the dataframe for calculations
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working_df = st.session_state['Contest'].copy()
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+
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with col1:
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with st.expander("Info and filters"):
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if st.button('Clear data', key='reset3'):
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st.session_state.clear()
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with st.form(key='filter_form'):
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type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
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entry_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific'])
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entry_names = st.multiselect("Select players", options=st.session_state['entry_list'], default=[])
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submitted = st.form_submit_button("Submit")
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if submitted:
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if 'player_frame' in st.session_state:
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del st.session_state['player_frame']
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del st.session_state['stack_frame']
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# Apply entry name filter if specific entries are selected
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if entry_parse_var == 'Specific' and entry_names:
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working_df = working_df[working_df['BaseName'].isin(entry_names)]
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# Calculate metrics based on game type
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if type_var == 'Classic':
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working_df['stack'] = working_df.apply(
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lambda row: Counter(
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map_dict['team_map'].get(player, '') for player in row[4:]
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if map_dict['team_map'].get(player, '') != ''
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).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
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axis=1
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)
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working_df['stack_size'] = working_df.apply(
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lambda row: Counter(
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map_dict['team_map'].get(player, '') for player in row[4:]
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if map_dict['team_map'].get(player, '') != ''
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).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
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axis=1
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)
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working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
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working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
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working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
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working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
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working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
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working_df['sorted'] = working_df[player_columns].apply(
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lambda row: ','.join(sorted(row.values)),
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axis=1
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)
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working_df['dupes'] = working_df.groupby('sorted').transform('size')
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working_df = working_df.drop('sorted', axis=1)
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elif type_var == 'Showdown':
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working_df['stack'] = working_df.apply(
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lambda row: Counter(
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map_dict['team_map'].get(player, '') for player in row
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if map_dict['team_map'].get(player, '') != ''
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).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row) else '',
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axis=1
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)
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working_df['stack_size'] = working_df.apply(
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lambda row: Counter(
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map_dict['team_map'].get(player, '') for player in row
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if map_dict['team_map'].get(player, '') != ''
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).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row) else '',
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axis=1
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)
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working_df['salary'] = working_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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working_df['median'] = working_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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working_df['Own'] = working_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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)
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working_df['sorted'] = working_df[player_columns].apply(
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lambda row: row[0] + '|' + ','.join(sorted(row[1:].values)),
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axis=1
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)
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working_df['dupes'] = working_df.groupby('sorted').transform('size')
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working_df = working_df.drop('sorted', axis=1)
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# Initialize pagination in session state if not exists
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if 'current_page' not in st.session_state:
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hide_index=True
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)
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for col in player_columns:
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contest_players = working_df.copy()
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players_1per = working_df.head(int(len(working_df) * 0.01))
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players_5per = working_df.head(int(len(working_df) * 0.05))
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players_10per = working_df.head(int(len(working_df) * 0.10))
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players_20per = working_df.head(int(len(working_df) * 0.20))
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contest_len = len(st.session_state['Contest'])
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len_1per = len(st.session_state['Contest']).head(int(len(working_df) * 0.01))
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len_5per = len(st.session_state['Contest']).head(int(len(working_df) * 0.05))
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len_10per = len(st.session_state['Contest']).head(int(len(working_df) * 0.10))
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len_20per = len(st.session_state['Contest']).head(int(len(working_df) * 0.20))
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player_counts = pd.Series(list(contest_players[player_columns].values.flatten())).value_counts()
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player_1per_counts = pd.Series(list(players_1per[player_columns].values.flatten())).value_counts()
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player_5per_counts = pd.Series(list(players_5per[player_columns].values.flatten())).value_counts()
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dupe_20per_counts = pd.Series(list(players_20per['dupes'])).value_counts()
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each_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%']
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each_frame_set = [contest_players, players_1per, players_5per, players_10per, players_20per]
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each_len_set = [contest_len, len_1per, len_5per, len_10per, len_20per]
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with st.container():
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tab1, tab2, tab3 = st.tabs(['Player Used Info', 'Stack Used Info', 'Duplication Info'])
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with tab1:
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player_count_var = 0
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for each_set in [player_counts, player_1per_counts, player_5per_counts, player_10per_counts, player20_per_counts]:
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set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
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st.write(len(each_frame_set[player_count_var]))
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set_frame['Percent'] = set_frame['Count'] / each_len_set[player_count_var]
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set_frame = set_frame[['Player', 'Percent']]
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set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[player_count_var]}'})
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if 'player_frame' not in st.session_state:
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stack_count_var = 0
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for each_set in [stack_counts, stack_1per_counts, stack_5per_counts, stack_10per_counts, stack_20per_counts]:
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set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
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st.write(len(each_frame_set[stack_count_var]))
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set_frame['Percent'] = set_frame['Count'] / each_len_set[stack_count_var]
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set_frame = set_frame[['Stack', 'Percent']]
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set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[stack_count_var]}'})
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if 'stack_frame' not in st.session_state:
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dupe_count_var = 0
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for each_set in [dupe_counts, dupe_1per_counts, dupe_5per_counts, dupe_10per_counts, dupe_20per_counts]:
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set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Dupes', 'count': 'Count'})
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set_frame['Percent'] = set_frame['Count'] / each_len_set[dupe_count_var]
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set_frame = set_frame[['Dupes', 'Percent']]
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set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[dupe_count_var]}'})
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if 'dupe_frame' not in st.session_state:
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