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| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| for name in dir(): | |
| if not name.startswith('_'): | |
| del globals()[name] | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import gc | |
| def init_conn(): | |
| scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] | |
| credentials = { | |
| "type": "service_account", | |
| "project_id": "model-sheets-connect", | |
| "private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e", | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n", | |
| "client_email": "[email protected]", | |
| "client_id": "100369174533302798535", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com" | |
| } | |
| gc_con = gspread.service_account_from_dict(credentials, scope) | |
| return gc_con | |
| gcservice_account = init_conn() | |
| NBA_Data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109' | |
| def init_baselines(): | |
| sh = gcservice_account.open_by_url(NBA_Data) | |
| worksheet = sh.worksheet('Trending') | |
| raw_display = pd.DataFrame(worksheet.get_values()) | |
| raw_display.columns = raw_display.iloc[0] | |
| raw_display = raw_display[1:] | |
| raw_display = raw_display.reset_index(drop=True) | |
| trend_table = raw_display[raw_display['PLAYER_NAME'] != ""] | |
| trend_table.replace('', np.nan, inplace=True) | |
| trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'FD_Position', 'L10 MIN', 'L10 Fantasy', 'L10 Ceiling', 'L10 FD_Fantasy', | |
| 'L10 FD_Ceiling', 'L5 MIN', 'L5 Fantasy', 'L5 Ceiling', 'L5 FD_Fantasy', 'L5 FD_Ceiling', 'L3 MIN', 'L3 Fantasy', | |
| 'L3 Ceiling', 'L3 FD_Fantasy', 'L3 FD_Ceiling', 'Trend Min', 'Trend Median', 'DK_Proj', 'Adj Median', 'Adj Ceiling', | |
| 'Trend FD_Median', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling', 'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value', | |
| 'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']] | |
| trend_table['DK_Salary'] = trend_table['DK_Salary'].str.replace(',', '').astype(float) | |
| trend_table['FD_Salary'] = trend_table['FD_Salary'].str.replace(',', '').astype(float) | |
| data_cols = trend_table.columns.drop(['PLAYER_NAME', 'Team', 'Position', 'FD_Position']) | |
| trend_table[data_cols] = trend_table[data_cols].apply(pd.to_numeric, errors='coerce') | |
| dk_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']] | |
| fd_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']] | |
| dk_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median']] | |
| fd_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy', 'Trend FD_Median']] | |
| dk_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'DK_Salary', 'DK_Proj', 'Adj Median', 'DK_Avg_Val', 'Adj Ceiling', 'DK_Ceiling_Value']] | |
| fd_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'FD_Salary', 'FD_Proj', 'Adj FD_Median', 'FD_Avg_Val', 'Adj FD_Ceiling', 'FD_Ceiling_Value']] | |
| return trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines() | |
| col1, col2 = st.columns([1, 9]) | |
| with col1: | |
| if st.button("Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines() | |
| split_var1 = st.radio("What table would you like to view?", ('Minutes Trends', 'Fantasy Trends', 'Slate specific', 'Overall'), key='split_var1') | |
| site_var1 = st.radio("What site would you like to view?", ('Draftkings', 'Fanduel'), key='site_var1') | |
| if site_var1 == 'Draftkings': | |
| trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'L10 MIN', 'L10 Fantasy', 'L10 Ceiling', | |
| 'L5 MIN', 'L5 Fantasy', 'L5 Ceiling', 'L3 MIN', 'L3 Fantasy', | |
| 'L3 Ceiling', 'Trend Min', 'Trend Median', 'DK_Proj', 'Adj Median', 'Adj Ceiling', | |
| 'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value']] | |
| minutes_table = dk_minutes_table | |
| medians_table = dk_medians_table | |
| proj_medians_table = dk_proj_medians_table | |
| elif site_var1 == 'Fanduel': | |
| trend_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'L10 MIN', 'L10 FD_Fantasy', | |
| 'L10 FD_Ceiling', 'L5 MIN', 'L5 FD_Fantasy', 'L5 FD_Ceiling', 'L3 MIN', 'L3 FD_Fantasy', | |
| 'L3 FD_Ceiling', 'Trend Min', 'Trend FD_Median', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling', | |
| 'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']] | |
| minutes_table = fd_minutes_table | |
| medians_table = fd_medians_table | |
| proj_medians_table = fd_proj_medians_table | |
| trend_table = trend_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'L10 MIN', 'L10 Fantasy', 'L10 Ceiling', | |
| 'L5 MIN', 'L5 Fantasy', 'L5 Ceiling', 'L3 MIN', 'L3 Fantasy', | |
| 'L3 Ceiling', 'Trend Min', 'Trend Median', 'Proj', 'Adj Median', 'Adj Ceiling', | |
| 'Salary', 'Avg_Val', 'Ceiling_Value'], axis=1) | |
| minutes_table = minutes_table.set_axis(['PLAYER_NAME', 'Team', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min'], axis=1) | |
| medians_table = medians_table.set_axis(['PLAYER_NAME', 'Team', 'L10 Fantasy','L5 Fantasy', 'L3 Fantasy', 'Trend Median'], axis=1) | |
| proj_medians_table = proj_medians_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Salary', 'Proj', | |
| 'Adj Median', 'Avg_Val', 'Adj Ceiling', 'Ceiling_Value'], axis=1) | |
| if split_var1 == 'Overall': | |
| view_var1 = trend_table.Team.values.tolist() | |
| split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2') | |
| if split_var2 == 'Specific Teams': | |
| team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1') | |
| elif split_var2 == 'All': | |
| team_var1 = view_var1 | |
| split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3') | |
| if split_var3 == 'Specific Positions': | |
| pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1') | |
| elif split_var3 == 'All': | |
| pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C'] | |
| proj_var1 = st.slider("Is there a certain projection range you want to view?", 0, 100, (10, 100), key='proj_var1') | |
| elif split_var1 == 'Minutes Trends': | |
| view_var1 = trend_table.Team.values.tolist() | |
| split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2') | |
| if split_var2 == 'Specific Teams': | |
| team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1') | |
| elif split_var2 == 'All': | |
| team_var1 = view_var1 | |
| elif split_var1 == 'Fantasy Trends': | |
| view_var1 = trend_table.Team.values.tolist() | |
| split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2') | |
| if split_var2 == 'Specific Teams': | |
| team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1') | |
| elif split_var2 == 'All': | |
| team_var1 = view_var1 | |
| elif split_var1 == 'Slate specific': | |
| view_var1 = trend_table.Team.values.tolist() | |
| split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2') | |
| if split_var2 == 'Specific Teams': | |
| team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1') | |
| elif split_var2 == 'All': | |
| team_var1 = view_var1 | |
| split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3') | |
| if split_var3 == 'Specific Positions': | |
| pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1') | |
| elif split_var3 == 'All': | |
| pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C'] | |
| proj_var1 = st.slider("Is there a certain projection range you want to view?", 0, 100, (10, 100), key='proj_var1') | |
| with col2: | |
| if split_var1 == 'Overall': | |
| table_display = trend_table[trend_table['Proj'] >= proj_var1[0]] | |
| table_display = table_display[table_display['Proj'] <= proj_var1[1]] | |
| table_display = table_display[table_display['Team'].isin(team_var1)] | |
| table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))] | |
| table_display = table_display.sort_values(by='Adj Ceiling', ascending=False) | |
| table_display = table_display.set_index('PLAYER_NAME') | |
| st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Trending Numbers", | |
| data=convert_df_to_csv(table_display), | |
| file_name='Trending_export.csv', | |
| mime='text/csv', | |
| ) | |
| elif split_var1 == 'Minutes Trends': | |
| table_display = minutes_table[minutes_table['Team'].isin(team_var1)] | |
| table_display = table_display.set_index('PLAYER_NAME') | |
| st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Trending Numbers", | |
| data=convert_df_to_csv(table_display), | |
| file_name='Trending_export.csv', | |
| mime='text/csv', | |
| ) | |
| elif split_var1 == 'Fantasy Trends': | |
| table_display = medians_table[medians_table['Team'].isin(team_var1)] | |
| table_display = table_display.set_index('PLAYER_NAME') | |
| st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Trending Numbers", | |
| data=convert_df_to_csv(table_display), | |
| file_name='Trending_export.csv', | |
| mime='text/csv', | |
| ) | |
| elif split_var1 == 'Slate specific': | |
| table_display = proj_medians_table[proj_medians_table['Proj'] >= proj_var1[0]] | |
| table_display = table_display[table_display['Proj'] <= proj_var1[1]] | |
| table_display = table_display[table_display['Team'].isin(team_var1)] | |
| table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))] | |
| table_display = table_display.sort_values(by='Adj Ceiling', ascending=False) | |
| table_display = table_display.set_index('PLAYER_NAME') | |
| st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Trending Numbers", | |
| data=convert_df_to_csv(table_display), | |
| file_name='Trending_export.csv', | |
| mime='text/csv', | |
| ) | |