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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 | |
| def init_conn(): | |
| scope = ['https://www.googleapis.com/auth/spreadsheets', | |
| "https://www.googleapis.com/auth/drive"] | |
| credentials = { | |
| "type": "service_account", | |
| "project_id": "sheets-api-connect-378620", | |
| "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", | |
| "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", | |
| "client_id": "106625872877651920064", | |
| "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%40sheets-api-connect-378620.iam.gserviceaccount.com" | |
| } | |
| gc = gspread.service_account_from_dict(credentials) | |
| return gc | |
| gspreadcon = init_conn() | |
| dk_player_url = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260' | |
| def load_overall_stats(): | |
| sh = gspreadcon.open_by_url(dk_player_url) | |
| worksheet = sh.worksheet('DK_Build_Up') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) | |
| raw_display.replace("", 'Welp', inplace=True) | |
| raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] | |
| raw_display = raw_display.loc[raw_display['Salary'] > 0] | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| dk_raw = raw_display.sort_values(by='Median', ascending=False) | |
| worksheet = sh.worksheet('FD_Build_Up') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) | |
| raw_display.replace("", 'Welp', inplace=True) | |
| raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| fd_raw = raw_display.sort_values(by='Median', ascending=False) | |
| worksheet = sh.worksheet('Secondary_DK_Build') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) | |
| raw_display.replace("", 'Welp', inplace=True) | |
| raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| dk_raw_sec = raw_display.sort_values(by='Median', ascending=False) | |
| worksheet = sh.worksheet('Secondary_FD_Build') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) | |
| raw_display.replace("", 'Welp', inplace=True) | |
| raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| fd_raw_sec = raw_display.sort_values(by='Median', ascending=False) | |
| worksheet = sh.worksheet('Player_Level_ROO') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.replace("", 'Welp', inplace=True) | |
| raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| roo_raw = raw_display.sort_values(by='Median', ascending=False) | |
| timestamp = raw_display['timestamp'].values[0] | |
| return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| tab1, tab2 = st.tabs(['Range of Outcomes', 'Uploads and Info']) | |
| with tab1: | |
| col1, col2 = st.columns([1, 9]) | |
| with col1: | |
| st.info(t_stamp) | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2') | |
| if site_var2 == 'Draftkings': | |
| site_baselines = roo_raw[roo_raw['site'] == 'Draftkings'] | |
| elif site_var2 == 'Fanduel': | |
| site_baselines = roo_raw[roo_raw['site'] == 'Fanduel'] | |
| slate_split = st.radio("Are you viewing the main slate or the secondary slate?", ('Main Slate', 'Secondary'), key='slate_split') | |
| if slate_split == 'Main Slate': | |
| raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate'] | |
| elif slate_split == 'Secondary': | |
| raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary'] | |
| split_var2 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var2') | |
| if split_var2 == 'Specific Games': | |
| team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2') | |
| elif split_var2 == 'Full Slate Run': | |
| team_var2 = raw_baselines.Team.values.tolist() | |
| pos_var2 = st.selectbox('View specific position?', options = ['All', 'PG', 'SG', 'SF', 'PF', 'C'], key='pos_var2') | |
| with col2: | |
| display_container_1 = st.empty() | |
| display_dl_container_1 = st.empty() | |
| display_proj = raw_baselines[raw_baselines['Team'].isin(team_var2)] | |
| display_proj = display_proj.drop(columns=['site', 'version', 'slate', 'timestamp']) | |
| st.session_state.display_proj = display_proj | |
| with display_container_1: | |
| display_container = st.empty() | |
| if 'display_proj' in st.session_state: | |
| if pos_var2 == 'All': | |
| st.session_state.display_proj = st.session_state.display_proj | |
| elif pos_var2 != 'All': | |
| st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)] | |
| st.dataframe(st.session_state.display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True) | |
| with display_dl_container_1: | |
| display_dl_container = st.empty() | |
| if 'display_proj' in st.session_state: | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(st.session_state.display_proj), | |
| file_name='NBA_ROO_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own'.") | |
| col1, col2 = st.columns([1, 5]) | |
| with col1: | |
| proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader') | |
| if proj_file is not None: | |
| try: | |
| proj_dataframe = pd.read_csv(proj_file) | |
| except: | |
| proj_dataframe = pd.read_excel(proj_file) | |
| with col2: | |
| if proj_file is not None: | |
| st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |