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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 pulp | |
import numpy as np | |
import pandas as pd | |
import streamlit as st | |
import gspread | |
from itertools import combinations | |
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() | |
player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', | |
'4x%': '{:.2%}'} | |
all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=1401252991' | |
def player_stat_table(): | |
sh = gspreadcon.open_by_url(all_dk_player_projections) | |
worksheet = sh.worksheet('Player_Level_ROO') | |
player_frame = pd.DataFrame(worksheet.get_all_records()) | |
sh = gspreadcon.open_by_url(all_dk_player_projections) | |
worksheet = sh.worksheet('Player_Lines_ROO') | |
line_frame = pd.DataFrame(worksheet.get_all_records()) | |
sh = gspreadcon.open_by_url(all_dk_player_projections) | |
worksheet = sh.worksheet('Player_PowerPlay_ROO') | |
pp_frame = pd.DataFrame(worksheet.get_all_records()) | |
sh = gspreadcon.open_by_url(all_dk_player_projections) | |
worksheet = sh.worksheet('Timestamp') | |
timestamp = worksheet.acell('A1').value | |
return player_frame, line_frame, pp_frame, timestamp | |
def convert_df_to_csv(df): | |
return df.to_csv().encode('utf-8') | |
player_frame, line_frame, pp_frame, timestamp = player_stat_table() | |
t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
tab1, tab2, tab3 = st.tabs(["Player Range of Outcomes", "Line Combo Range of Outcomes", "Power Play Range of Outcomes"]) | |
with tab1: | |
col1, col2 = st.columns([1, 7]) | |
with col1: | |
st.info(t_stamp) | |
if st.button("Load/Reset Data", key='reset1'): | |
st.cache_data.clear() | |
player_frame, line_frame, pp_frame, timestamp = player_stat_table() | |
t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1') | |
split_var1 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var1') | |
if split_var1 == 'Specific Games': | |
team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = player_frame['Team'].unique(), key='team_var1') | |
elif split_var1 == 'Full Slate Run': | |
team_var1 = player_frame.Team.values.tolist() | |
pos_split1 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1') | |
if pos_split1 == 'Specific Positions': | |
pos_var1 = st.multiselect('What Positions would you like to view?', options = ['C', 'W', 'D', 'G']) | |
elif pos_split1 == 'All Positions': | |
pos_var1 = 'All' | |
sal_var1 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 20000), key='sal_var1') | |
with col2: | |
final_Proj = player_frame[player_frame['Site'] == str(site_var1)] | |
final_Proj = final_Proj[final_Proj['Type'] == 'Basic'] | |
final_Proj = final_Proj[player_frame['Team'].isin(team_var1)] | |
final_Proj = final_Proj[final_Proj['Salary'] >= sal_var1[0]] | |
final_Proj = final_Proj[final_Proj['Salary'] <= sal_var1[1]] | |
if pos_var1 != 'All': | |
final_Proj = final_Proj[final_Proj['Position'].str.contains('|'.join(pos_var1))] | |
final_Proj = final_Proj.set_index('Player') | |
final_Proj = final_Proj.sort_values(by='Median', ascending=False) | |
if pos_var1 == 'All': | |
final_Proj = final_Proj.set_index('Player') | |
final_Proj = final_Proj.sort_values(by='Median', ascending=False) | |
st.dataframe(final_Proj.iloc[:, :-3].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) | |
st.download_button( | |
label="Export Tables", | |
data=convert_df_to_csv(final_Proj), | |
file_name='NHL_player_export.csv', | |
mime='text/csv', | |
) | |
with tab2: | |
col1, col2 = st.columns([1, 7]) | |
with col1: | |
st.info(t_stamp) | |
if st.button("Load/Reset Data", key='reset2'): | |
st.cache_data.clear() | |
player_frame, line_frame, pp_frame, timestamp = player_stat_table() | |
t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2') | |
with col2: | |
final_line_combos = line_frame[line_frame['Site'] == str(site_var2)] | |
final_line_combos = final_line_combos[final_line_combos['Type'] == 'Basic'] | |
final_line_combos = final_line_combos.sort_values(by='Median', ascending=False) | |
st.dataframe(final_line_combos.iloc[:, :-3].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
st.download_button( | |
label="Export Tables", | |
data=convert_df_to_csv(final_Proj), | |
file_name='NHL_linecombos_export.csv', | |
mime='text/csv', | |
) | |
with tab3: | |
col1, col2 = st.columns([1, 7]) | |
with col1: | |
st.info(t_stamp) | |
if st.button("Load/Reset Data", key='reset3'): | |
st.cache_data.clear() | |
player_frame, line_frame, pp_frame, timestamp = player_stat_table() | |
t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var3') | |
with col2: | |
final_pp_combos = pp_frame[pp_frame['Site'] == str(site_var3)] | |
final_pp_combos = final_pp_combos[final_pp_combos['Type'] == 'Basic'] | |
final_pp_combos = final_pp_combos.sort_values(by='Median', ascending=False) | |
st.dataframe(final_pp_combos.iloc[:, :-3].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
st.download_button( | |
label="Export Tables", | |
data=convert_df_to_csv(final_Proj), | |
file_name='NHL_powerplay_export.csv', | |
mime='text/csv', | |
) |