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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
@st.cache_resource
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()
NHL_data = 'https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=811139250'
percentages_format = {'Shots': '{:.2%}', 'HDCF': '{:.2%}', 'Goals': '{:.2%}', 'Assists': '{:.2%}', 'Blocks': '{:.2%}',
'L14_Shots': '{:.2%}', 'L14_HDCF': '{:.2%}', 'L14_Goals': '{:.2%}', 'L14_Assists': '{:.2%}', 'L14_Blocks': '{:.2%}'}
@st.cache_resource(ttl = 600)
def init_baselines():
sh = gcservice_account.open_by_url(NHL_data)
worksheet = sh.worksheet('Matchups')
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)
raw_display = raw_display[raw_display['Opp'] != ""]
matchups = raw_display[['Team', 'Opp', 'FL1$', 'FL2$', 'FL3$', 'Team Total', 'Game Pace', 'SF', 'o_SA', 'SF_m', 'HDCF',
'o_HDCA', 'HDCF_m']]
data_cols = matchups.columns.drop(['Team', 'Opp'])
# matchups[data_cols] = matchups[data_cols].apply(pd.to_numeric, errors='coerce')
worksheet = sh.worksheet('Marketshares')
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)
raw_display = raw_display[raw_display['Line'] != ""]
overall_ms = raw_display[['Line', 'SK1', 'SK2', 'SK3', 'Cost', 'Team Total', 'Shots', 'HDCF', 'Goals', 'Assists', 'Blocks',
'L14_Shots', 'L14_HDCF', 'L14_Goals', 'L14_Assists', 'L14_Blocks']]
data_cols = overall_ms.columns.drop(['Line', 'SK1', 'SK2', 'SK3'])
# overall_ms[data_cols] = overall_ms[data_cols].apply(pd.to_numeric, errors='coerce')
return matchups, overall_ms
def convert_df_to_csv(df):
return df.to_csv().encode('utf-8')
matchups, overall_ms = init_baselines()
col1, col2 = st.columns([1, 9])
with col1:
if st.button("Reset Data", key='reset1'):
st.cache_data.clear()
matchups, overall_ms = init_baselines()
split_var1 = st.radio("View matchups or line marketshares?", ('Slate Matchups', 'Line Marketshares'), key='split_var1')
with col2:
if split_var1 == 'Slate Matchups':
display_table = matchups
st.dataframe(display_table, use_container_width = True)
st.download_button(
label="Export Matchups",
data=convert_df_to_csv(display_table),
file_name='Matchups_export.csv',
mime='text/csv',
)
elif split_var1 == 'Line Marketshares':
display_table = overall_ms
st.dataframe(display_table, use_container_width = True)
st.download_button(
label="Export Marketshares",
data=convert_df_to_csv(display_table),
file_name='Marketshares_export.csv',
mime='text/csv',
) |