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Runtime error
| 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 | |
| import plotly.express as px | |
| import plotly.io as pio | |
| import pymongo | |
| import certifi | |
| ca = certifi.where() | |
| 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" | |
| } | |
| uri = "mongodb+srv://multichem:[email protected]/?retryWrites=true&w=majority&appName=TestCluster" | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000) | |
| db = client["testing_db"] | |
| gc_con = gspread.service_account_from_dict(credentials, scope) | |
| return gc_con, client, db | |
| gcservice_account, client, db = init_conn() | |
| MLB_Data = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=340831852' | |
| percentages_format = {'PG': '{:.2%}', 'SG': '{:.2%}', 'SF': '{:.2%}', 'PF': '{:.2%}', 'C': '{:.2%}'} | |
| def init_baselines(): | |
| sh = gcservice_account.open_by_url(MLB_Data) | |
| collection = db["DK_MLB_seed_frame"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| DK_seed = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'Team_count', 'salary', 'proj']] | |
| collection = db["FD_MLB_seed_frame"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| FD_seed = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'Team_count', 'salary', 'proj']] | |
| worksheet = sh.worksheet('DK_Projections') | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| load_display.replace('', np.nan, inplace=True) | |
| dk_raw = load_display.dropna(subset=['Median']) | |
| worksheet = sh.worksheet('FD_Projections') | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| load_display.replace('', np.nan, inplace=True) | |
| fd_raw = load_display.dropna(subset=['Median']) | |
| return DK_seed, FD_seed, dk_raw, fd_raw | |
| DK_seed, FD_seed, dk_raw, fd_raw = init_baselines() | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| DK_seed, FD_seed, dk_raw, fd_raw = init_baselines() | |
| slate_var1 = st.radio("Which data are you loading?", ('Main Slate')) | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) | |
| if site_var1 == 'Draftkings': | |
| raw_baselines = dk_raw | |
| elif site_var1 == 'Fanduel': | |
| raw_baselines = fd_raw | |
| contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large')) | |
| if contest_var1 == 'Small': | |
| Contest_Size = 1000 | |
| elif contest_var1 == 'Medium': | |
| Contest_Size = 5000 | |
| elif contest_var1 == 'Large': | |
| Contest_Size = 10000 | |
| elif contest_var1 == 'Massive': | |
| Contest_Size = 100000 | |
| strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very')) | |
| if strength_var1 == 'Not Very': | |
| sharp_split = [400000,100000] | |
| elif strength_var1 == 'Average': | |
| sharp_split = [500000,200000] | |
| elif strength_var1 == 'Very': | |
| sharp_split = [500000,300000] | |
| with col2: | |
| if site_var1 == 'Draftkings': | |
| st.session_state.Sim_Winner_Frame = DK_seed.head(Contest_Size) | |
| st.session_state.Sim_Winner_Display = DK_seed.head(Contest_Size) | |
| st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:9].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int) | |
| st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(1000) | |
| st.dataframe(st.session_state.Sim_Winner_Frame.style.format(precision=2), height=500, use_container_width=True) | |
| st.table(st.session_state.player_freq) | |
| elif site_var1 == 'Fanduel': | |
| st.session_state.Sim_Winner_Frame = FD_seed.head(Contest_Size) | |
| st.session_state.Sim_Winner_Display = FD_seed.head(Contest_Size) | |
| st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:8].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int) | |
| st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(1000) | |
| st.dataframe(st.session_state.Sim_Winner_Frame.style.format(precision=2), height=500, use_container_width=True) | |
| st.table(st.session_state.player_freq) |