Spaces:
Running
Running
James McCool
commited on
Commit
·
9c3aa05
1
Parent(s):
02d2bcd
Refactor app.py layout and logic for improved user interaction: reorganize UI components for league and site selection, enhance data loading functionality, and streamline slate type handling, ensuring a more intuitive experience for users managing NBA and WNBA lineups.
Browse files
app.py
CHANGED
@@ -363,72 +363,85 @@ tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimals'])
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with tab1:
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st.info(t_stamp)
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with col2:
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if st.button("Load/Reset Data", key='reset1'):
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st.cache_data.clear()
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dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA')
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salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
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id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
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salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
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dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
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fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
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dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
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dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
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fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
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fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
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dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
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dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
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fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
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fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
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t_stamp = f"Last Update: " + str(timestamp) + f" CST"
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for key in st.session_state.keys():
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del st.session_state[key]
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col1, col2, col3, col4, col5, col6 = st.columns(6)
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with col1:
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with col2:
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slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2')
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with
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site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
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-
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# Process site selection
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if site_var2 == 'Draftkings':
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if slate_type_var2 == 'Regular':
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site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
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elif slate_type_var2 == 'Showdown':
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site_baselines = sd_raw[sd_raw['site'] == 'Draftkings']
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elif site_var2 == 'Fanduel':
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if slate_type_var2 == 'Regular':
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site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
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elif slate_type_var2 == 'Showdown':
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site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
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with col5:
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slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary'), key='slate_split')
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if slate_split == 'Main Slate':
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if
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elif slate_split == 'Secondary':
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-
if
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with
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split_var2 = st.radio("Slate Range", ('Full Slate Run', 'Specific Games'), key='split_var2')
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if split_var2 == 'Specific Games':
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team_var2 = st.multiselect('Select teams for ROO', options=raw_baselines['Team'].unique(), key='team_var2')
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@@ -501,25 +514,19 @@ with tab2:
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col1, col2, col3, col4, col5, col6 = st.columns(6)
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with col1:
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league_var2 = st.radio("What League to load:", ('NBA', 'WNBA'), key='league_var2')
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with col2:
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slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
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with
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site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
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if 'working_seed' in st.session_state:
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del st.session_state['working_seed']
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with col4:
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slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'))
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with
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lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
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with
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if
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if
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if slate_type_var1 == 'Regular':
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column_names = dk_nba_columns
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elif slate_type_var1 == 'Showdown':
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column_names = dk_nba_sd_columns
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-
elif
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if slate_type_var1 == 'Regular':
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column_names = dk_wnba_columns
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elif slate_type_var1 == 'Showdown':
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@@ -531,13 +538,13 @@ with tab2:
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elif player_var1 == 'Full Slate':
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player_var2 = dk_raw.Player.values.tolist()
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elif
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if
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if slate_type_var1 == 'Regular':
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column_names = fd_nba_columns
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elif slate_type_var1 == 'Showdown':
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column_names = fd_nba_sd_columns
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elif
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if slate_type_var1 == 'Regular':
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column_names = fd_wnba_columns
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elif slate_type_var1 == 'Showdown':
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@@ -550,26 +557,26 @@ with tab2:
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player_var2 = fd_raw.Player.values.tolist()
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if st.button("Prepare data export", key='data_export'):
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if
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if slate_type_var1 == 'Regular':
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data_export = init_DK_lineups(slate_var1,
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data_export_names = data_export.copy()
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for col_idx in range(8):
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data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
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elif slate_type_var1 == 'Showdown':
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data_export = init_DK_SD_lineups(slate_var1,
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data_export_names = data_export.copy()
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for col_idx in range(6):
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data_export[:, col_idx] = np.array([dk_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
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elif
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if slate_type_var1 == 'Regular':
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data_export = init_FD_lineups(slate_var1,
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data_export_names = data_export.copy()
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for col_idx in range(9):
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data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
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elif slate_type_var1 == 'Showdown':
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data_export = init_FD_SD_lineups(slate_var1,
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data_export_names = data_export.copy()
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for col_idx in range(6):
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data_export[:, col_idx] = np.array([fd_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
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@@ -587,7 +594,7 @@ with tab2:
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)
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if
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if 'working_seed' in st.session_state:
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st.session_state.working_seed = st.session_state.working_seed
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if player_var1 == 'Specific Players':
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@@ -598,20 +605,20 @@ with tab2:
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elif 'working_seed' not in st.session_state:
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if slate_type_var1 == 'Regular':
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st.session_state.working_seed = init_DK_lineups(slate_var1,
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elif slate_type_var1 == 'Showdown':
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st.session_state.working_seed = init_DK_SD_lineups(slate_var1,
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st.session_state.working_seed = st.session_state.working_seed
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if player_var1 == 'Specific Players':
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st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
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elif player_var1 == 'Full Slate':
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if slate_type_var1 == 'Regular':
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st.session_state.working_seed = init_DK_lineups(slate_var1,
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elif slate_type_var1 == 'Showdown':
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st.session_state.working_seed = init_DK_SD_lineups(slate_var1,
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
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elif
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if 'working_seed' in st.session_state:
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st.session_state.working_seed = st.session_state.working_seed
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if player_var1 == 'Specific Players':
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@@ -622,28 +629,28 @@ with tab2:
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elif 'working_seed' not in st.session_state:
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if slate_type_var1 == 'Regular':
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st.session_state.working_seed = init_FD_lineups(slate_var1,
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elif slate_type_var1 == 'Showdown':
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st.session_state.working_seed = init_FD_SD_lineups(slate_var1,
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st.session_state.working_seed = st.session_state.working_seed
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if player_var1 == 'Specific Players':
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st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
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elif player_var1 == 'Full Slate':
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if slate_type_var1 == 'Regular':
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st.session_state.working_seed = init_FD_lineups(slate_var1,
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elif slate_type_var1 == 'Showdown':
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st.session_state.working_seed = init_FD_SD_lineups(slate_var1,
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
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export_file = st.session_state.data_export_display.copy()
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if
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if slate_type_var1 == 'Regular':
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for col_idx in range(8):
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export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
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elif slate_type_var1 == 'Showdown':
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for col_idx in range(6):
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export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(dk_id_dict_sd)
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-
elif
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if slate_type_var1 == 'Regular':
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for col_idx in range(9):
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export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
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if st.button("Reset Optimals", key='reset3'):
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for key in st.session_state.keys():
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del st.session_state[key]
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if
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if
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if slate_type_var1 == 'Regular':
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st.session_state.working_seed = dk_nba_lineups.copy()
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elif slate_type_var1 == 'Showdown':
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st.session_state.working_seed = dk_nba_sd_lineups.copy()
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-
elif
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if slate_type_var1 == 'Regular':
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st.session_state.working_seed = dk_wnba_lineups.copy()
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elif slate_type_var1 == 'Showdown':
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st.session_state.working_seed = dk_wnba_sd_lineups.copy()
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elif
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if
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if slate_type_var1 == 'Regular':
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st.session_state.working_seed = fd_nba_lineups.copy()
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elif slate_type_var1 == 'Showdown':
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st.session_state.working_seed = fd_nba_sd_lineups.copy()
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-
elif
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if slate_type_var1 == 'Regular':
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st.session_state.working_seed = fd_wnba_lineups.copy()
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elif slate_type_var1 == 'Showdown':
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@@ -689,8 +696,8 @@ with tab2:
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with st.container():
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if 'working_seed' in st.session_state:
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# Create a new dataframe with summary statistics
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if
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if
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if slate_type_var1 == 'Regular':
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summary_df = pd.DataFrame({
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'Metric': ['Min', 'Average', 'Max', 'STDdev'],
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np.std(st.session_state.working_seed[:,12])
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]
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})
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elif
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if slate_type_var1 == 'Regular':
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summary_df = pd.DataFrame({
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'Metric': ['Min', 'Average', 'Max', 'STDdev'],
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]
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})
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elif
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if
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if slate_type_var1 == 'Regular':
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summary_df = pd.DataFrame({
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'Metric': ['Min', 'Average', 'Max', 'STDdev'],
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@@ -827,7 +834,7 @@ with tab2:
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np.std(st.session_state.working_seed[:,12])
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]
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})
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-
elif
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if slate_type_var1 == 'Regular':
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summary_df = pd.DataFrame({
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'Metric': ['Min', 'Average', 'Max', 'STDdev'],
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@@ -888,27 +895,27 @@ with tab2:
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tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
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with tab1:
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if 'data_export_display' in st.session_state:
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if
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if slate_type_var1 == 'Regular':
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if
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player_columns = st.session_state.data_export_display.iloc[:, :8]
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elif
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player_columns = st.session_state.data_export_display.iloc[:, :9]
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elif slate_type_var1 == 'Showdown':
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if
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player_columns = st.session_state.data_export_display.iloc[:, :5]
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elif
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player_columns = st.session_state.data_export_display.iloc[:, :5]
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elif
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if slate_type_var1 == 'Regular':
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if
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player_columns = st.session_state.data_export_display.iloc[:, :7]
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elif
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player_columns = st.session_state.data_export_display.iloc[:, :8]
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elif slate_type_var1 == 'Showdown':
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if
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player_columns = st.session_state.data_export_display.iloc[:, :5]
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elif
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player_columns = st.session_state.data_export_display.iloc[:, :5]
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@@ -941,27 +948,27 @@ with tab2:
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)
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with tab2:
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if 'working_seed' in st.session_state:
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if
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if slate_type_var1 == 'Regular':
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if
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player_columns = st.session_state.working_seed[:, :8]
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-
elif
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player_columns = st.session_state.working_seed[:, :9]
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elif slate_type_var1 == 'Showdown':
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-
if
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player_columns = st.session_state.working_seed[:, :5]
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-
elif
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player_columns = st.session_state.working_seed[:, :5]
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-
elif
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if slate_type_var1 == 'Regular':
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if
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player_columns = st.session_state.working_seed[:, :7]
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-
elif
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player_columns = st.session_state.working_seed[:, :8]
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elif slate_type_var1 == 'Showdown':
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if
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player_columns = st.session_state.working_seed[:, :5]
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elif
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player_columns = st.session_state.working_seed[:, :5]
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# Flatten the DataFrame and count unique values
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with tab1:
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+
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with st.container():
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st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile")
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reset_col, view_col, site_col, league_col = st.columns(4)
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with reset_col:
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# First row - timestamp and reset button
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col1, col2 = st.columns([3, 1])
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with col1:
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st.info(t_stamp)
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with col2:
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if st.button("Load/Reset Data", key='reset1'):
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st.cache_data.clear()
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+
dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA')
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+
salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
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id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
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salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
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dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
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fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
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dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
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dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
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fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
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fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
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+
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dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
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390 |
+
dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
|
391 |
+
fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
|
392 |
+
fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
|
393 |
+
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
|
394 |
+
for key in st.session_state.keys():
|
395 |
+
del st.session_state[key]
|
396 |
+
with view_col:
|
397 |
+
view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
|
398 |
+
with site_col:
|
399 |
+
site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
|
400 |
+
if 'working_seed' in st.session_state:
|
401 |
+
del st.session_state['working_seed']
|
402 |
+
with league_col:
|
403 |
+
league_var = st.radio("What League to load:", ('WNBA', 'NBA'), key='league_var')
|
404 |
+
dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats(league_var)
|
405 |
+
with st.expander("Info and Filters"):
|
406 |
+
col1, col2, col3 = st.columns(3)
|
407 |
+
|
408 |
+
with col1:
|
409 |
slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2')
|
410 |
+
with col2:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
411 |
slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary'), key='slate_split')
|
412 |
|
413 |
if slate_split == 'Main Slate':
|
414 |
+
if site_var2 == 'Draftkings':
|
415 |
+
if slate_type_var2 == 'Regular':
|
416 |
+
site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
|
417 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
|
418 |
+
elif slate_type_var2 == 'Showdown':
|
419 |
+
site_baselines = sd_raw[sd_raw['site'] == 'Draftkings']
|
420 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1']
|
421 |
+
elif site_var2 == 'Fanduel':
|
422 |
+
if slate_type_var2 == 'Regular':
|
423 |
+
site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
|
424 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
|
425 |
+
elif slate_type_var2 == 'Showdown':
|
426 |
+
site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
|
427 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1']
|
428 |
elif slate_split == 'Secondary':
|
429 |
+
if site_var2 == 'Draftkings':
|
430 |
+
if slate_type_var2 == 'Regular':
|
431 |
+
site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
|
432 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
|
433 |
+
elif slate_type_var2 == 'Showdown':
|
434 |
+
site_baselines = sd_raw[sd_raw['site'] == 'Draftkings']
|
435 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
|
436 |
+
elif site_var2 == 'Fanduel':
|
437 |
+
if slate_type_var2 == 'Regular':
|
438 |
+
site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
|
439 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
|
440 |
+
elif slate_type_var2 == 'Showdown':
|
441 |
+
site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
|
442 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
|
443 |
|
444 |
+
with col3:
|
445 |
split_var2 = st.radio("Slate Range", ('Full Slate Run', 'Specific Games'), key='split_var2')
|
446 |
if split_var2 == 'Specific Games':
|
447 |
team_var2 = st.multiselect('Select teams for ROO', options=raw_baselines['Team'].unique(), key='team_var2')
|
|
|
514 |
|
515 |
col1, col2, col3, col4, col5, col6 = st.columns(6)
|
516 |
with col1:
|
|
|
|
|
517 |
slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
|
518 |
+
with col2:
|
|
|
|
|
|
|
|
|
519 |
slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'))
|
520 |
+
with col3:
|
521 |
lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
|
522 |
+
with col4:
|
523 |
+
if site_var2 == 'Draftkings':
|
524 |
+
if league_var == 'NBA':
|
525 |
if slate_type_var1 == 'Regular':
|
526 |
column_names = dk_nba_columns
|
527 |
elif slate_type_var1 == 'Showdown':
|
528 |
column_names = dk_nba_sd_columns
|
529 |
+
elif league_var == 'WNBA':
|
530 |
if slate_type_var1 == 'Regular':
|
531 |
column_names = dk_wnba_columns
|
532 |
elif slate_type_var1 == 'Showdown':
|
|
|
538 |
elif player_var1 == 'Full Slate':
|
539 |
player_var2 = dk_raw.Player.values.tolist()
|
540 |
|
541 |
+
elif site_var2 == 'Fanduel':
|
542 |
+
if league_var == 'NBA':
|
543 |
if slate_type_var1 == 'Regular':
|
544 |
column_names = fd_nba_columns
|
545 |
elif slate_type_var1 == 'Showdown':
|
546 |
column_names = fd_nba_sd_columns
|
547 |
+
elif league_var == 'WNBA':
|
548 |
if slate_type_var1 == 'Regular':
|
549 |
column_names = fd_wnba_columns
|
550 |
elif slate_type_var1 == 'Showdown':
|
|
|
557 |
player_var2 = fd_raw.Player.values.tolist()
|
558 |
if st.button("Prepare data export", key='data_export'):
|
559 |
|
560 |
+
if site_var2 == 'Draftkings':
|
561 |
if slate_type_var1 == 'Regular':
|
562 |
+
data_export = init_DK_lineups(slate_var1, league_var)
|
563 |
data_export_names = data_export.copy()
|
564 |
for col_idx in range(8):
|
565 |
data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
|
566 |
elif slate_type_var1 == 'Showdown':
|
567 |
+
data_export = init_DK_SD_lineups(slate_var1, league_var)
|
568 |
data_export_names = data_export.copy()
|
569 |
for col_idx in range(6):
|
570 |
data_export[:, col_idx] = np.array([dk_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
|
571 |
|
572 |
+
elif site_var2 == 'Fanduel':
|
573 |
if slate_type_var1 == 'Regular':
|
574 |
+
data_export = init_FD_lineups(slate_var1, league_var)
|
575 |
data_export_names = data_export.copy()
|
576 |
for col_idx in range(9):
|
577 |
data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
|
578 |
elif slate_type_var1 == 'Showdown':
|
579 |
+
data_export = init_FD_SD_lineups(slate_var1, league_var)
|
580 |
data_export_names = data_export.copy()
|
581 |
for col_idx in range(6):
|
582 |
data_export[:, col_idx] = np.array([fd_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
|
|
|
594 |
)
|
595 |
|
596 |
|
597 |
+
if site_var2 == 'Draftkings':
|
598 |
if 'working_seed' in st.session_state:
|
599 |
st.session_state.working_seed = st.session_state.working_seed
|
600 |
if player_var1 == 'Specific Players':
|
|
|
605 |
|
606 |
elif 'working_seed' not in st.session_state:
|
607 |
if slate_type_var1 == 'Regular':
|
608 |
+
st.session_state.working_seed = init_DK_lineups(slate_var1, league_var)
|
609 |
elif slate_type_var1 == 'Showdown':
|
610 |
+
st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var)
|
611 |
st.session_state.working_seed = st.session_state.working_seed
|
612 |
if player_var1 == 'Specific Players':
|
613 |
st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
|
614 |
elif player_var1 == 'Full Slate':
|
615 |
if slate_type_var1 == 'Regular':
|
616 |
+
st.session_state.working_seed = init_DK_lineups(slate_var1, league_var)
|
617 |
elif slate_type_var1 == 'Showdown':
|
618 |
+
st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var)
|
619 |
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
620 |
|
621 |
+
elif site_var2 == 'Fanduel':
|
622 |
if 'working_seed' in st.session_state:
|
623 |
st.session_state.working_seed = st.session_state.working_seed
|
624 |
if player_var1 == 'Specific Players':
|
|
|
629 |
|
630 |
elif 'working_seed' not in st.session_state:
|
631 |
if slate_type_var1 == 'Regular':
|
632 |
+
st.session_state.working_seed = init_FD_lineups(slate_var1, league_var)
|
633 |
elif slate_type_var1 == 'Showdown':
|
634 |
+
st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var)
|
635 |
st.session_state.working_seed = st.session_state.working_seed
|
636 |
if player_var1 == 'Specific Players':
|
637 |
st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
|
638 |
elif player_var1 == 'Full Slate':
|
639 |
if slate_type_var1 == 'Regular':
|
640 |
+
st.session_state.working_seed = init_FD_lineups(slate_var1, league_var)
|
641 |
elif slate_type_var1 == 'Showdown':
|
642 |
+
st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var)
|
643 |
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
644 |
|
645 |
export_file = st.session_state.data_export_display.copy()
|
646 |
+
if site_var2 == 'Draftkings':
|
647 |
if slate_type_var1 == 'Regular':
|
648 |
for col_idx in range(8):
|
649 |
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
|
650 |
elif slate_type_var1 == 'Showdown':
|
651 |
for col_idx in range(6):
|
652 |
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(dk_id_dict_sd)
|
653 |
+
elif site_var2 == 'Fanduel':
|
654 |
if slate_type_var1 == 'Regular':
|
655 |
for col_idx in range(9):
|
656 |
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
|
|
|
662 |
if st.button("Reset Optimals", key='reset3'):
|
663 |
for key in st.session_state.keys():
|
664 |
del st.session_state[key]
|
665 |
+
if site_var2 == 'Draftkings':
|
666 |
+
if league_var == 'NBA':
|
667 |
if slate_type_var1 == 'Regular':
|
668 |
st.session_state.working_seed = dk_nba_lineups.copy()
|
669 |
elif slate_type_var1 == 'Showdown':
|
670 |
st.session_state.working_seed = dk_nba_sd_lineups.copy()
|
671 |
+
elif league_var == 'WNBA':
|
672 |
if slate_type_var1 == 'Regular':
|
673 |
st.session_state.working_seed = dk_wnba_lineups.copy()
|
674 |
elif slate_type_var1 == 'Showdown':
|
675 |
st.session_state.working_seed = dk_wnba_sd_lineups.copy()
|
676 |
+
elif site_var2 == 'Fanduel':
|
677 |
+
if league_var == 'NBA':
|
678 |
if slate_type_var1 == 'Regular':
|
679 |
st.session_state.working_seed = fd_nba_lineups.copy()
|
680 |
elif slate_type_var1 == 'Showdown':
|
681 |
st.session_state.working_seed = fd_nba_sd_lineups.copy()
|
682 |
+
elif league_var == 'WNBA':
|
683 |
if slate_type_var1 == 'Regular':
|
684 |
st.session_state.working_seed = fd_wnba_lineups.copy()
|
685 |
elif slate_type_var1 == 'Showdown':
|
|
|
696 |
with st.container():
|
697 |
if 'working_seed' in st.session_state:
|
698 |
# Create a new dataframe with summary statistics
|
699 |
+
if site_var2 == 'Draftkings':
|
700 |
+
if league_var == 'NBA':
|
701 |
if slate_type_var1 == 'Regular':
|
702 |
summary_df = pd.DataFrame({
|
703 |
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
|
|
742 |
np.std(st.session_state.working_seed[:,12])
|
743 |
]
|
744 |
})
|
745 |
+
elif league_var == 'WNBA':
|
746 |
if slate_type_var1 == 'Regular':
|
747 |
summary_df = pd.DataFrame({
|
748 |
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
|
|
788 |
]
|
789 |
})
|
790 |
|
791 |
+
elif site_var2 == 'Fanduel':
|
792 |
+
if league_var == 'NBA':
|
793 |
if slate_type_var1 == 'Regular':
|
794 |
summary_df = pd.DataFrame({
|
795 |
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
|
|
834 |
np.std(st.session_state.working_seed[:,12])
|
835 |
]
|
836 |
})
|
837 |
+
elif league_var == 'WNBA':
|
838 |
if slate_type_var1 == 'Regular':
|
839 |
summary_df = pd.DataFrame({
|
840 |
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
|
|
895 |
tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
|
896 |
with tab1:
|
897 |
if 'data_export_display' in st.session_state:
|
898 |
+
if league_var == 'NBA':
|
899 |
if slate_type_var1 == 'Regular':
|
900 |
+
if site_var2 == 'Draftkings':
|
901 |
player_columns = st.session_state.data_export_display.iloc[:, :8]
|
902 |
+
elif site_var2 == 'Fanduel':
|
903 |
player_columns = st.session_state.data_export_display.iloc[:, :9]
|
904 |
elif slate_type_var1 == 'Showdown':
|
905 |
+
if site_var2 == 'Draftkings':
|
906 |
player_columns = st.session_state.data_export_display.iloc[:, :5]
|
907 |
+
elif site_var2 == 'Fanduel':
|
908 |
player_columns = st.session_state.data_export_display.iloc[:, :5]
|
909 |
+
elif league_var == 'WNBA':
|
910 |
if slate_type_var1 == 'Regular':
|
911 |
+
if site_var2 == 'Draftkings':
|
912 |
player_columns = st.session_state.data_export_display.iloc[:, :7]
|
913 |
+
elif site_var2 == 'Fanduel':
|
914 |
player_columns = st.session_state.data_export_display.iloc[:, :8]
|
915 |
elif slate_type_var1 == 'Showdown':
|
916 |
+
if site_var2 == 'Draftkings':
|
917 |
player_columns = st.session_state.data_export_display.iloc[:, :5]
|
918 |
+
elif site_var2 == 'Fanduel':
|
919 |
player_columns = st.session_state.data_export_display.iloc[:, :5]
|
920 |
|
921 |
|
|
|
948 |
)
|
949 |
with tab2:
|
950 |
if 'working_seed' in st.session_state:
|
951 |
+
if league_var == 'NBA':
|
952 |
if slate_type_var1 == 'Regular':
|
953 |
+
if site_var2 == 'Draftkings':
|
954 |
player_columns = st.session_state.working_seed[:, :8]
|
955 |
+
elif site_var2 == 'Fanduel':
|
956 |
player_columns = st.session_state.working_seed[:, :9]
|
957 |
elif slate_type_var1 == 'Showdown':
|
958 |
+
if site_var2 == 'Draftkings':
|
959 |
player_columns = st.session_state.working_seed[:, :5]
|
960 |
+
elif site_var2 == 'Fanduel':
|
961 |
player_columns = st.session_state.working_seed[:, :5]
|
962 |
+
elif league_var == 'WNBA':
|
963 |
if slate_type_var1 == 'Regular':
|
964 |
+
if site_var2 == 'Draftkings':
|
965 |
player_columns = st.session_state.working_seed[:, :7]
|
966 |
+
elif site_var2 == 'Fanduel':
|
967 |
player_columns = st.session_state.working_seed[:, :8]
|
968 |
elif slate_type_var1 == 'Showdown':
|
969 |
+
if site_var2 == 'Draftkings':
|
970 |
player_columns = st.session_state.working_seed[:, :5]
|
971 |
+
elif site_var2 == 'Fanduel':
|
972 |
player_columns = st.session_state.working_seed[:, :5]
|
973 |
|
974 |
# Flatten the DataFrame and count unique values
|