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Update app.py
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import pulp
import numpy as np
import pandas as pd
import streamlit as st
import gspread
from itertools import combinations
scope = ['https://www.googleapis.com/auth/spreadsheets',
"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": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
"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"
}
credentials2 = {
"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)
gc2 = gspread.service_account_from_dict(credentials2)
st.set_page_config(layout="wide")
game_format = {'K_rate': '{:.2%}', 'Walk_rate': '{:.2%}', 'Hits_boost': '{:.2%}',
'Singles_boost': '{:.2%}', 'Doubles_boost': '{:.2%}', 'Homeruns_boost': '{:.2%}', 'K_rate_boost': '{:.2%}', 'Walk_rate_boost': '{:.2%}',
'xBA_avg_boost': '{:.2%}', 'xSLG_avg_boost': '{:.2%}', 'xwOBA_avg_boost': '{:.2%}'}
mlb_model_link = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=1769627309'
@st.cache_resource(ttl = 299)
def load_init():
try:
sh = gc.open_by_url(mlb_model_link)
except:
sh = gc2.open_by_url(mlb_model_link)
worksheet = sh.worksheet('Ballpark_stats')
raw_display = pd.DataFrame(worksheet.get_all_records())
overall_stats = raw_display[['Stadium', 'Split', 'PA', 'Hits', 'Singles', 'Doubles', 'Homeruns', 'K_rate', 'Walk_rate', 'xBA_avg', 'xSLG_avg', 'xwOBA_avg', 'Hits_boost',
'Singles_boost', 'Doubles_boost', 'Homeruns_boost', 'K_rate_boost', 'Walk_rate_boost', 'xBA_avg_boost', 'xSLG_avg_boost', 'xwOBA_avg_boost']]
raw_numbers = raw_display[['Stadium', 'Split', 'PA', 'Hits', 'Singles', 'Doubles', 'Homeruns', 'K_rate', 'Walk_rate', 'xBA_avg', 'xSLG_avg', 'xwOBA_avg']]
pa_numbers = raw_display[['Stadium', 'Split', 'PA', 'Hits/PA', 'Singles/PA', 'Doubles/PA', 'Homeruns/PA', 'K_rate', 'Walk_rate', 'xBA_avg', 'xSLG_avg', 'xwOBA_avg']]
boosts = raw_display[['Stadium', 'Split', 'PA', 'Hits_boost', 'Singles_boost', 'Doubles_boost', 'Homeruns_boost', 'K_rate_boost', 'Walk_rate_boost', 'xBA_avg_boost', 'xSLG_avg_boost', 'xwOBA_avg_boost']]
return overall_stats, raw_numbers, pa_numbers, boosts
@st.cache_data
def convert_df_to_csv(df):
return df.to_csv().encode('utf-8')
overall_stats, raw_numbers, pa_numbers, boosts = load_init()
col1, col2 = st.columns([2, 2])
#st.info(t_stamp)
if st.button("Load/Reset Data", key='reset3'):
overall_stats, raw_numbers, pa_numbers, boosts = load_init()
st.cache_data.clear()
with col1:
split_var1 = st.radio("Which data are you loading?", ('Overall', 'LHH', 'RHH'), key='split_var1')
with col2:
stat_var1 = st.radio("What table would you like to display?", ('All Stats', 'Raw Stats', 'Averages', 'Boosts'), key='stat_var1')
hold_container = st.empty()
with hold_container:
hold_container = st.empty()
if stat_var1 == 'All Stats':
final_Proj = overall_stats.copy()
if split_var1 == 'LHH':
final_Proj = final_Proj[final_Proj['Split'] == 'LHH']
elif split_var1 == 'RHH':
final_Proj = final_Proj[final_Proj['Split'] == 'RHH']
final_Proj = final_Proj.sort_values(by='xwOBA_avg', ascending=False)
elif stat_var1 == 'Raw Stats':
final_Proj = raw_numbers.copy()
if split_var1 == 'LHH':
final_Proj = final_Proj[final_Proj['Split'] == 'LHH']
elif split_var1 == 'RHH':
final_Proj = final_Proj[final_Proj['Split'] == 'RHH']
final_Proj = final_Proj.sort_values(by='xwOBA_avg', ascending=False)
elif stat_var1 == 'Averages':
final_Proj = pa_numbers.copy()
if split_var1 == 'LHH':
final_Proj = final_Proj[final_Proj['Split'] == 'LHH']
elif split_var1 == 'RHH':
final_Proj = final_Proj[final_Proj['Split'] == 'RHH']
final_Proj = final_Proj.sort_values(by='xwOBA_avg', ascending=False)
elif stat_var1 == 'Boosts':
final_Proj = boosts.copy()
if split_var1 == 'LHH':
final_Proj = final_Proj[final_Proj['Split'] == 'LHH']
elif split_var1 == 'RHH':
final_Proj = final_Proj[final_Proj['Split'] == 'RHH']
final_Proj = final_Proj.sort_values(by='xwOBA_avg_boost', ascending=False)
st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(game_format, precision=2), height=1000, use_container_width = True)
st.download_button(
label="Export Tables",
data=convert_df_to_csv(final_Proj),
file_name='MLB_ballpark_splits.csv',
mime='text/csv',
)