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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 plotly.express as px
import random
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": "dfsnew",
"private_key_id": "2432f6c3771f70a410c5c878d1359869fc9dddc8",
"private_key": "-----BEGIN PRIVATE KEY-----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\nbEyuT7sCPCLeOX/RLy/lCpA=\n-----END PRIVATE KEY-----\n",
"client_email": "[email protected]",
"client_id": "105107448378741046480",
"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/dfsapps%40dfsnew.iam.gserviceaccount.com",
"universe_domain": "googleapis.com"
}
header= {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) '
'AppleWebKit/537.11 (KHTML, like Gecko) '
'Chrome/23.0.1271.64 Safari/537.11',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
'Accept-Encoding': 'none',
'Accept-Language': 'en-US,en;q=0.8',
'Connection': 'keep-alive'}
gc_con = gspread.service_account_from_dict(credentials, scope)
return gc_con
gcservice_account = init_conn()
NBAGetGameData = 'https://docs.google.com/spreadsheets/d/1tRQrF_I5rS7Q0g9vE8NrENDZ2P3_DvtbBZzKEakwOI0/edit#gid=1373653837'
NBABettingModel = 'https://docs.google.com/spreadsheets/d/1WBnvOHQi_zVTGF63efejK5ho02AY00HiYrMHnMJXY1E/edit#gid=1157978351'
@st.cache_resource(ttl = 300)
def init_baselines():
sh = gcservice_account.open_by_url(NBAGetGameData)
worksheet = sh.worksheet('MinPublic')
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.replace('', np.nan, inplace=True)
raw_display = raw_display[['NBAID', 'PID', 'Player', 'TC', 'MP (Today)', 'Next Game', 'H/R', 'Injury Notes', 'Player Impact per 48', 'Player Impact',
'Team PM', 'Last Updated']]
raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
public_minutes = raw_display[raw_display['NBAID'] != ""]
sh = gcservice_account.open_by_url(NBABettingModel)
worksheet = sh.worksheet('PlayerImpactByTeam')
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.replace('', 0, inplace=True)
raw_display = raw_display[['PID', 'Player', 'Team', 'Avg Minutes last 30 days for team', 'Minutes Projection', 'Rotation Impact (versus last 30 days)',
'Injury Notes', 'Minute Change', 'Baseline Team PM', 'Net Rotation PM +/- for Team', 'Projected PM for Game', 'Offset', 'Rank']]
raw_display['Minute Change'].replace('+', '', inplace=True)
raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
player_impact = raw_display[raw_display['PID'] != ""]
return public_minutes, player_impact
def convert_df_to_csv(df):
return df.to_csv().encode('utf-8')
public_minutes, player_impact = init_baselines()
tab1, tab2 = st.tabs(["Minutes Baselines", "Player Impacts"])
with tab1:
if st.button("Reset Data", key='reset1'):
st.cache_data.clear()
public_minutes, player_impact = init_baselines()
split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
if split_var1 == 'Specific Teams':
team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = public_minutes['TC'].unique(), key='team_var1')
elif split_var1 == 'All':
team_var1 = public_minutes.TC.values.tolist()
public_minutes = public_minutes[public_minutes['TC'].isin(team_var1)]
player_min_disp = public_minutes.set_index('Player')
player_min_disp = player_min_disp.sort_values(by=['TC', 'MP (Today)'], ascending=[True, False])
st.dataframe(player_min_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
st.download_button(
label="Export Minutes Baselines",
data=convert_df_to_csv(public_minutes),
file_name='AmericanNumbers_Min_Baseline_export.csv',
mime='text/csv',
)
with tab2:
if st.button("Reset Data", key='reset2'):
st.cache_data.clear()
public_minutes, player_impact = init_baselines()
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_var2 = st.multiselect('Which teams would you like to include in the tables?', options = player_impact['Team'].unique(), key='team_var2')
elif split_var2 == 'All':
team_var2 = player_impact.Team.values.tolist()
player_impact = player_impact[player_impact['Team'].isin(team_var2)]
player_impact_disp = player_impact.set_index('Player')
player_impact_disp = player_impact_disp.sort_values(by=['Team', 'Rotation Impact (versus last 30 days)'], ascending=[True, False])
st.dataframe(player_impact_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
st.download_button(
label="Export Player Impacts",
data=convert_df_to_csv(player_impact),
file_name='AmericanNumbers_Impact_export.csv',
mime='text/csv',
) |