Spaces:
Running
Running
James McCool
commited on
Commit
·
b7f0617
1
Parent(s):
841c7fd
Refactor UI layout and improve data loading process for NBA and WNBA; streamline user interactions with tabs and columns for better mobile usability.
Browse files
app.py
CHANGED
@@ -327,76 +327,88 @@ 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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with tab1:
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with st.expander("Info and Filters"):
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-
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-
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with st.container():
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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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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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view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
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with col2:
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league_var = st.radio("What League to load:", ('NBA', 'WNBA'), key='league_var')
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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(league_var)
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with col3:
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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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@@ -469,25 +481,19 @@ 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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@@ -499,13 +505,13 @@ 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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@@ -518,26 +524,26 @@ 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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@@ -555,7 +561,7 @@ 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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@@ -566,20 +572,20 @@ 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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@@ -590,28 +596,28 @@ 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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@@ -623,24 +629,24 @@ with tab2:
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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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@@ -657,8 +663,8 @@ 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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@@ -703,7 +709,7 @@ 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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@@ -749,8 +755,8 @@ with tab2:
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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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@@ -795,7 +801,7 @@ 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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@@ -856,27 +862,27 @@ 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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@@ -909,27 +915,27 @@ 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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t_stamp = f"Last Update: " + str(timestamp) + f" CST"
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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, 3])
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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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349 |
+
fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
|
350 |
+
fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
|
351 |
|
352 |
+
dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
|
353 |
+
dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
|
354 |
+
fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
|
355 |
+
fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
|
356 |
+
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
|
357 |
+
for key in st.session_state.keys():
|
358 |
+
del st.session_state[key]
|
359 |
+
with view_col:
|
360 |
+
view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
|
361 |
+
with site_col:
|
362 |
+
site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
|
363 |
+
if 'working_seed' in st.session_state:
|
364 |
+
del st.session_state['working_seed']
|
365 |
+
with league_col:
|
366 |
+
league_var = st.radio("What League to load:", ('WNBA', 'NBA'), key='league_var')
|
367 |
+
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)
|
368 |
+
|
369 |
+
tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimals'])
|
370 |
with tab1:
|
371 |
|
372 |
with st.expander("Info and Filters"):
|
373 |
+
col1, col2, col3 = st.columns(3)
|
374 |
+
|
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|
375 |
with col1:
|
|
|
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|
|
376 |
slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2')
|
377 |
+
with col2:
|
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|
378 |
slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary'), key='slate_split')
|
379 |
|
380 |
if slate_split == 'Main Slate':
|
381 |
+
if site_var2 == 'Draftkings':
|
382 |
+
if slate_type_var2 == 'Regular':
|
383 |
+
site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
|
384 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
|
385 |
+
elif slate_type_var2 == 'Showdown':
|
386 |
+
site_baselines = sd_raw[sd_raw['site'] == 'Draftkings']
|
387 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1']
|
388 |
+
elif site_var2 == 'Fanduel':
|
389 |
+
if slate_type_var2 == 'Regular':
|
390 |
+
site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
|
391 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
|
392 |
+
elif slate_type_var2 == 'Showdown':
|
393 |
+
site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
|
394 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1']
|
395 |
elif slate_split == 'Secondary':
|
396 |
+
if site_var2 == 'Draftkings':
|
397 |
+
if slate_type_var2 == 'Regular':
|
398 |
+
site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
|
399 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
|
400 |
+
elif slate_type_var2 == 'Showdown':
|
401 |
+
site_baselines = sd_raw[sd_raw['site'] == 'Draftkings']
|
402 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
|
403 |
+
elif site_var2 == 'Fanduel':
|
404 |
+
if slate_type_var2 == 'Regular':
|
405 |
+
site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
|
406 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
|
407 |
+
elif slate_type_var2 == 'Showdown':
|
408 |
+
site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
|
409 |
+
raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
|
410 |
|
411 |
+
with col3:
|
412 |
split_var2 = st.radio("Slate Range", ('Full Slate Run', 'Specific Games'), key='split_var2')
|
413 |
if split_var2 == 'Specific Games':
|
414 |
team_var2 = st.multiselect('Select teams for ROO', options=raw_baselines['Team'].unique(), key='team_var2')
|
|
|
481 |
|
482 |
col1, col2, col3, col4, col5, col6 = st.columns(6)
|
483 |
with col1:
|
|
|
|
|
484 |
slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
|
485 |
+
with col2:
|
|
|
|
|
|
|
|
|
486 |
slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'))
|
487 |
+
with col3:
|
488 |
lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
|
489 |
+
with col4:
|
490 |
+
if site_var2 == 'Draftkings':
|
491 |
+
if league_var == 'NBA':
|
492 |
if slate_type_var1 == 'Regular':
|
493 |
column_names = dk_nba_columns
|
494 |
elif slate_type_var1 == 'Showdown':
|
495 |
column_names = dk_nba_sd_columns
|
496 |
+
elif league_var == 'WNBA':
|
497 |
if slate_type_var1 == 'Regular':
|
498 |
column_names = dk_wnba_columns
|
499 |
elif slate_type_var1 == 'Showdown':
|
|
|
505 |
elif player_var1 == 'Full Slate':
|
506 |
player_var2 = dk_raw.Player.values.tolist()
|
507 |
|
508 |
+
elif site_var2 == 'Fanduel':
|
509 |
+
if league_var == 'NBA':
|
510 |
if slate_type_var1 == 'Regular':
|
511 |
column_names = fd_nba_columns
|
512 |
elif slate_type_var1 == 'Showdown':
|
513 |
column_names = fd_nba_sd_columns
|
514 |
+
elif league_var == 'WNBA':
|
515 |
if slate_type_var1 == 'Regular':
|
516 |
column_names = fd_wnba_columns
|
517 |
elif slate_type_var1 == 'Showdown':
|
|
|
524 |
player_var2 = fd_raw.Player.values.tolist()
|
525 |
if st.button("Prepare data export", key='data_export'):
|
526 |
|
527 |
+
if site_var2 == 'Draftkings':
|
528 |
if slate_type_var1 == 'Regular':
|
529 |
+
data_export = init_DK_lineups(slate_var1, league_var)
|
530 |
data_export_names = data_export.copy()
|
531 |
for col_idx in range(8):
|
532 |
data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
|
533 |
elif slate_type_var1 == 'Showdown':
|
534 |
+
data_export = init_DK_SD_lineups(slate_var1, league_var)
|
535 |
data_export_names = data_export.copy()
|
536 |
for col_idx in range(6):
|
537 |
data_export[:, col_idx] = np.array([dk_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
|
538 |
|
539 |
+
elif site_var2 == 'Fanduel':
|
540 |
if slate_type_var1 == 'Regular':
|
541 |
+
data_export = init_FD_lineups(slate_var1, league_var)
|
542 |
data_export_names = data_export.copy()
|
543 |
for col_idx in range(9):
|
544 |
data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
|
545 |
elif slate_type_var1 == 'Showdown':
|
546 |
+
data_export = init_FD_SD_lineups(slate_var1, league_var)
|
547 |
data_export_names = data_export.copy()
|
548 |
for col_idx in range(6):
|
549 |
data_export[:, col_idx] = np.array([fd_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
|
|
|
561 |
)
|
562 |
|
563 |
|
564 |
+
if site_var2 == 'Draftkings':
|
565 |
if 'working_seed' in st.session_state:
|
566 |
st.session_state.working_seed = st.session_state.working_seed
|
567 |
if player_var1 == 'Specific Players':
|
|
|
572 |
|
573 |
elif 'working_seed' not in st.session_state:
|
574 |
if slate_type_var1 == 'Regular':
|
575 |
+
st.session_state.working_seed = init_DK_lineups(slate_var1, league_var)
|
576 |
elif slate_type_var1 == 'Showdown':
|
577 |
+
st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var)
|
578 |
st.session_state.working_seed = st.session_state.working_seed
|
579 |
if player_var1 == 'Specific Players':
|
580 |
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)]
|
581 |
elif player_var1 == 'Full Slate':
|
582 |
if slate_type_var1 == 'Regular':
|
583 |
+
st.session_state.working_seed = init_DK_lineups(slate_var1, league_var)
|
584 |
elif slate_type_var1 == 'Showdown':
|
585 |
+
st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var)
|
586 |
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
587 |
|
588 |
+
elif site_var2 == 'Fanduel':
|
589 |
if 'working_seed' in st.session_state:
|
590 |
st.session_state.working_seed = st.session_state.working_seed
|
591 |
if player_var1 == 'Specific Players':
|
|
|
596 |
|
597 |
elif 'working_seed' not in st.session_state:
|
598 |
if slate_type_var1 == 'Regular':
|
599 |
+
st.session_state.working_seed = init_FD_lineups(slate_var1, league_var)
|
600 |
elif slate_type_var1 == 'Showdown':
|
601 |
+
st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var)
|
602 |
st.session_state.working_seed = st.session_state.working_seed
|
603 |
if player_var1 == 'Specific Players':
|
604 |
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)]
|
605 |
elif player_var1 == 'Full Slate':
|
606 |
if slate_type_var1 == 'Regular':
|
607 |
+
st.session_state.working_seed = init_FD_lineups(slate_var1, league_var)
|
608 |
elif slate_type_var1 == 'Showdown':
|
609 |
+
st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var)
|
610 |
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
611 |
|
612 |
export_file = st.session_state.data_export_display.copy()
|
613 |
+
if site_var2 == 'Draftkings':
|
614 |
if slate_type_var1 == 'Regular':
|
615 |
for col_idx in range(8):
|
616 |
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
|
617 |
elif slate_type_var1 == 'Showdown':
|
618 |
for col_idx in range(6):
|
619 |
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(dk_id_dict_sd)
|
620 |
+
elif site_var2 == 'Fanduel':
|
621 |
if slate_type_var1 == 'Regular':
|
622 |
for col_idx in range(9):
|
623 |
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
|
|
|
629 |
if st.button("Reset Optimals", key='reset3'):
|
630 |
for key in st.session_state.keys():
|
631 |
del st.session_state[key]
|
632 |
+
if site_var2 == 'Draftkings':
|
633 |
+
if league_var == 'NBA':
|
634 |
if slate_type_var1 == 'Regular':
|
635 |
st.session_state.working_seed = dk_nba_lineups.copy()
|
636 |
elif slate_type_var1 == 'Showdown':
|
637 |
st.session_state.working_seed = dk_nba_sd_lineups.copy()
|
638 |
+
elif league_var == 'WNBA':
|
639 |
if slate_type_var1 == 'Regular':
|
640 |
st.session_state.working_seed = dk_wnba_lineups.copy()
|
641 |
elif slate_type_var1 == 'Showdown':
|
642 |
st.session_state.working_seed = dk_wnba_sd_lineups.copy()
|
643 |
+
elif site_var2 == 'Fanduel':
|
644 |
+
if league_var == 'NBA':
|
645 |
if slate_type_var1 == 'Regular':
|
646 |
st.session_state.working_seed = fd_nba_lineups.copy()
|
647 |
elif slate_type_var1 == 'Showdown':
|
648 |
st.session_state.working_seed = fd_nba_sd_lineups.copy()
|
649 |
+
elif league_var == 'WNBA':
|
650 |
if slate_type_var1 == 'Regular':
|
651 |
st.session_state.working_seed = fd_wnba_lineups.copy()
|
652 |
elif slate_type_var1 == 'Showdown':
|
|
|
663 |
with st.container():
|
664 |
if 'working_seed' in st.session_state:
|
665 |
# Create a new dataframe with summary statistics
|
666 |
+
if site_var2 == 'Draftkings':
|
667 |
+
if league_var == 'NBA':
|
668 |
if slate_type_var1 == 'Regular':
|
669 |
summary_df = pd.DataFrame({
|
670 |
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
|
|
709 |
np.std(st.session_state.working_seed[:,12])
|
710 |
]
|
711 |
})
|
712 |
+
elif league_var == 'WNBA':
|
713 |
if slate_type_var1 == 'Regular':
|
714 |
summary_df = pd.DataFrame({
|
715 |
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
|
|
755 |
]
|
756 |
})
|
757 |
|
758 |
+
elif site_var2 == 'Fanduel':
|
759 |
+
if league_var == 'NBA':
|
760 |
if slate_type_var1 == 'Regular':
|
761 |
summary_df = pd.DataFrame({
|
762 |
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
|
|
801 |
np.std(st.session_state.working_seed[:,12])
|
802 |
]
|
803 |
})
|
804 |
+
elif league_var == 'WNBA':
|
805 |
if slate_type_var1 == 'Regular':
|
806 |
summary_df = pd.DataFrame({
|
807 |
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
|
|
862 |
tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
|
863 |
with tab1:
|
864 |
if 'data_export_display' in st.session_state:
|
865 |
+
if league_var == 'NBA':
|
866 |
if slate_type_var1 == 'Regular':
|
867 |
+
if site_var2 == 'Draftkings':
|
868 |
player_columns = st.session_state.data_export_display.iloc[:, :8]
|
869 |
+
elif site_var2 == 'Fanduel':
|
870 |
player_columns = st.session_state.data_export_display.iloc[:, :9]
|
871 |
elif slate_type_var1 == 'Showdown':
|
872 |
+
if site_var2 == 'Draftkings':
|
873 |
player_columns = st.session_state.data_export_display.iloc[:, :5]
|
874 |
+
elif site_var2 == 'Fanduel':
|
875 |
player_columns = st.session_state.data_export_display.iloc[:, :5]
|
876 |
+
elif league_var == 'WNBA':
|
877 |
if slate_type_var1 == 'Regular':
|
878 |
+
if site_var2 == 'Draftkings':
|
879 |
player_columns = st.session_state.data_export_display.iloc[:, :7]
|
880 |
+
elif site_var2 == 'Fanduel':
|
881 |
player_columns = st.session_state.data_export_display.iloc[:, :8]
|
882 |
elif slate_type_var1 == 'Showdown':
|
883 |
+
if site_var2 == 'Draftkings':
|
884 |
player_columns = st.session_state.data_export_display.iloc[:, :5]
|
885 |
+
elif site_var2 == 'Fanduel':
|
886 |
player_columns = st.session_state.data_export_display.iloc[:, :5]
|
887 |
|
888 |
|
|
|
915 |
)
|
916 |
with tab2:
|
917 |
if 'working_seed' in st.session_state:
|
918 |
+
if league_var == 'NBA':
|
919 |
if slate_type_var1 == 'Regular':
|
920 |
+
if site_var2 == 'Draftkings':
|
921 |
player_columns = st.session_state.working_seed[:, :8]
|
922 |
+
elif site_var2 == 'Fanduel':
|
923 |
player_columns = st.session_state.working_seed[:, :9]
|
924 |
elif slate_type_var1 == 'Showdown':
|
925 |
+
if site_var2 == 'Draftkings':
|
926 |
player_columns = st.session_state.working_seed[:, :5]
|
927 |
+
elif site_var2 == 'Fanduel':
|
928 |
player_columns = st.session_state.working_seed[:, :5]
|
929 |
+
elif league_var == 'WNBA':
|
930 |
if slate_type_var1 == 'Regular':
|
931 |
+
if site_var2 == 'Draftkings':
|
932 |
player_columns = st.session_state.working_seed[:, :7]
|
933 |
+
elif site_var2 == 'Fanduel':
|
934 |
player_columns = st.session_state.working_seed[:, :8]
|
935 |
elif slate_type_var1 == 'Showdown':
|
936 |
+
if site_var2 == 'Draftkings':
|
937 |
player_columns = st.session_state.working_seed[:, :5]
|
938 |
+
elif site_var2 == 'Fanduel':
|
939 |
player_columns = st.session_state.working_seed[:, :5]
|
940 |
|
941 |
# Flatten the DataFrame and count unique values
|