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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-----\nMIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQDiu1v/e6KBKOcK\ncx0KQ23nZK3ZVvADYy8u/RUn/EDI82QKxTd/DizRLIV81JiNQxDJXSzgkbwKYEDm\n48E8zGvupU8+Nk76xNPakrQKy2Y8+VJlq5psBtGchJTuUSHcXU5Mg2JhQsB376PJ\nsCw552K6Pw8fpeMDJDZuxpKSkaJR6k9G5Dhf5q8HDXnC5Rh/PRFuKJ2GGRpX7n+2\nhT/sCax0J8jfdTy/MDGiDfJqfQrOPrMKELtsGHR9Iv6F4vKiDqXpKfqH+02E9ptz\nBk+MNcbZ3m90M8ShfRu28ebebsASfarNMzc3dk7tb3utHOGXKCf4tF8yYKo7x8BZ\noO9X4gSfAgMBAAECggEAU8ByyMpSKlTCF32TJhXnVJi/kS+IhC/Qn5JUDMuk4LXr\naAEWsWO6kV/ZRVXArjmuSzuUVrXumISapM9Ps5Ytbl95CJmGDiLDwRL815nvv6k3\nUyAS8EGKjz74RpoIoH6E7EWCAzxlnUgTn+5oP9Flije97epYk3H+e2f1f5e1Nn1d\nYNe8U+1HqJgILcxA1TAUsARBfoD7+K3z/8DVPHI8IpzAh6kTHqhqC23Rram4XoQ6\nzj/ZdVBjvnKuazETfsD+Vl3jGLQA8cKQVV70xdz3xwLcNeHsbPbpGBpZUoF73c65\nkAXOrjYl0JD5yAk+hmYhXr6H9c6z5AieuZGDrhmlFQKBgQDzV6LRXmjn4854DP/J\nI82oX2GcI4eioDZPRukhiQLzYerMQBmyqZIRC+/LTCAhYQSjNgMa+ZKyvLqv48M0\n/x398op/+n3xTs+8L49SPI48/iV+mnH7k0WI/ycd4OOKh8rrmhl/0EWb9iitwJYe\nMjTV/QxNEpPBEXfR1/mvrN/lVQKBgQDuhomOxUhWVRVH6x03slmyRBn0Oiw4MW+r\nrt1hlNgtVmTc5Mu+4G0USMZwYuOB7F8xG4Foc7rIlwS7Ic83jMJxemtqAelwOLdV\nXRLrLWJfX8+O1z/UE15l2q3SUEnQ4esPHbQnZowHLm0mdL14qSVMl1mu1XfsoZ3z\nJZTQb48CIwKBgEWbzQRtKD8lKDupJEYqSrseRbK/ax43DDITS77/DWwHl33D3FYC\nMblUm8ygwxQpR4VUfwDpYXBlklWcJovzamXpSnsfcYVkkQH47NuOXPXPkXQsw+w+\nDYcJzeu7F/vZqk9I7oBkWHUrrik9zPNoUzrfPvSRGtkAoTDSwibhoc5dAoGBAMHE\nK0T/ANeZQLNuzQps6S7G4eqjwz5W8qeeYxsdZkvWThOgDd/ewt3ijMnJm5X05hOn\ni4XF1euTuvUl7wbqYx76Wv3/1ZojiNNgy7ie4rYlyB/6vlBS97F4ZxJdxMlabbCW\n6b3EMWa4EVVXKoA1sCY7IVDE+yoQ1JYsZmq45YzPAoGBANWWHuVueFGZRDZlkNlK\nh5OmySmA0NdNug3G1upaTthyaTZ+CxGliwBqMHAwpkIRPwxUJpUwBTSEGztGTAxs\nWsUOVWlD2/1JaKSmHE8JbNg6sxLilcG6WEDzxjC5dLL1OrGOXj9WhC9KX3sq6qb6\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()
NBA_Data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109'
@st.cache_resource(ttl = 600)
def init_baselines():
sh = gcservice_account.open_by_url(NBA_Data)
worksheet = sh.worksheet('Trending')
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)
trend_table = raw_display[raw_display['PLAYER_NAME'] != ""]
trend_table.replace('', np.nan, inplace=True)
trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'FD_Position', 'L10 MIN', 'L10 Fantasy', 'L10 Ceiling', 'L10 FD_Fantasy',
'L10 FD_Ceiling', 'L5 MIN', 'L5 Fantasy', 'L5 Ceiling', 'L5 FD_Fantasy', 'L5 FD_Ceiling', 'L3 MIN', 'L3 Fantasy',
'L3 Ceiling', 'L3 FD_Fantasy', 'L3 FD_Ceiling', 'Trend Min', 'Trend Median', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
'Trend FD_Median', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling', 'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value',
'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']]
trend_table['DK_Salary'] = trend_table['DK_Salary'].str.replace(',', '').astype(float)
trend_table['FD_Salary'] = trend_table['FD_Salary'].str.replace(',', '').astype(float)
data_cols = trend_table.columns.drop(['PLAYER_NAME', 'Team', 'Position', 'FD_Position'])
trend_table[data_cols] = trend_table[data_cols].apply(pd.to_numeric, errors='coerce')
dk_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']]
fd_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']]
dk_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median']]
fd_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy', 'Trend FD_Median']]
dk_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'DK_Salary', 'DK_Proj', 'Adj Median', 'DK_Avg_Val', 'Adj Ceiling', 'DK_Ceiling_Value']]
fd_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'FD_Salary', 'FD_Proj', 'Adj FD_Median', 'FD_Avg_Val', 'Adj FD_Ceiling', 'FD_Ceiling_Value']]
return trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table
def convert_df_to_csv(df):
return df.to_csv().encode('utf-8')
trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines()
col1, col2 = st.columns([1, 9])
with col1:
if st.button("Reset Data", key='reset1'):
st.cache_data.clear()
trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines()
split_var1 = st.radio("What table would you like to view?", ('Minutes Trends', 'Fantasy Trends', 'Slate specific', 'Overall'), key='split_var1')
site_var1 = st.radio("What site would you like to view?", ('Draftkings', 'Fanduel'), key='site_var1')
if site_var1 == 'Draftkings':
trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'L10 MIN', 'L10 Fantasy', 'L10 Ceiling',
'L5 MIN', 'L5 Fantasy', 'L5 Ceiling', 'L3 MIN', 'L3 Fantasy',
'L3 Ceiling', 'Trend Min', 'Trend Median', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value']]
minutes_table = dk_minutes_table
medians_table = dk_medians_table
proj_medians_table = dk_proj_medians_table
elif site_var1 == 'Fanduel':
trend_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'L10 MIN', 'L10 FD_Fantasy',
'L10 FD_Ceiling', 'L5 MIN', 'L5 FD_Fantasy', 'L5 FD_Ceiling', 'L3 MIN', 'L3 FD_Fantasy',
'L3 FD_Ceiling', 'Trend Min', 'Trend FD_Median', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling',
'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']]
minutes_table = fd_minutes_table
medians_table = fd_medians_table
proj_medians_table = fd_proj_medians_table
trend_table = trend_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'L10 MIN', 'L10 Fantasy', 'L10 Ceiling',
'L5 MIN', 'L5 Fantasy', 'L5 Ceiling', 'L3 MIN', 'L3 Fantasy',
'L3 Ceiling', 'Trend Min', 'Trend Median', 'Proj', 'Adj Median', 'Adj Ceiling',
'Salary', 'Avg_Val', 'Ceiling_Value'], axis=1)
minutes_table = minutes_table.set_axis(['PLAYER_NAME', 'Team', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min'], axis=1)
medians_table = medians_table.set_axis(['PLAYER_NAME', 'Team', 'L10 Fantasy','L5 Fantasy', 'L3 Fantasy', 'Trend Median'], axis=1)
proj_medians_table = proj_medians_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Salary', 'Proj',
'Adj Median', 'Avg_Val', 'Adj Ceiling', 'Ceiling_Value'], axis=1)
if split_var1 == 'Overall':
view_var1 = trend_table.Team.values.tolist()
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_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
elif split_var2 == 'All':
team_var1 = view_var1
split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
if split_var3 == 'Specific Positions':
pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = trend_table['Position'].unique(), key='pos_var1')
elif split_var3 == 'All':
pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
proj_var1 = st.slider("Is there a certain projection range you want to view?", 0, 100, (10, 100), key='proj_var1')
elif split_var1 == 'Minutes Trends':
view_var1 = trend_table.Team.values.tolist()
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_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
elif split_var2 == 'All':
team_var1 = view_var1
elif split_var1 == 'Fantasy Trends':
view_var1 = trend_table.Team.values.tolist()
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_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
elif split_var2 == 'All':
team_var1 = view_var1
elif split_var1 == 'Slate specific':
view_var1 = trend_table.Team.values.tolist()
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_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
elif split_var2 == 'All':
team_var1 = view_var1
split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
if split_var3 == 'Specific Positions':
pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = proj_medians_table['Position'].unique(), key='pos_var1')
elif split_var3 == 'All':
pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
proj_var1 = st.slider("Is there a certain projection range you want to view?", 0, 100, (10, 100), key='proj_var1')
with col2:
if split_var1 == 'Overall':
table_display = trend_table[trend_table['Proj'] >= proj_var1[0]]
table_display = table_display[table_display['Proj'] <= proj_var1[1]]
table_display = table_display[table_display['Team'].isin(team_var1)]
table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))]
table_display = table_display.sort_values(by='Adj Ceiling', ascending=False)
table_display = table_display.set_index('PLAYER_NAME')
st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
st.download_button(
label="Export Trending Numbers",
data=convert_df_to_csv(table_display),
file_name='Trending_export.csv',
mime='text/csv',
)
elif split_var1 == 'Minutes Trends':
table_display = minutes_table[minutes_table['Team'].isin(team_var1)]
table_display = table_display.set_index('PLAYER_NAME')
st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
st.download_button(
label="Export Trending Numbers",
data=convert_df_to_csv(table_display),
file_name='Trending_export.csv',
mime='text/csv',
)
elif split_var1 == 'Fantasy Trends':
table_display = medians_table[medians_table['Team'].isin(team_var1)]
table_display = table_display.set_index('PLAYER_NAME')
st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
st.download_button(
label="Export Trending Numbers",
data=convert_df_to_csv(table_display),
file_name='Trending_export.csv',
mime='text/csv',
)
elif split_var1 == 'Slate specific':
table_display = proj_medians_table[proj_medians_table['Proj'] >= proj_var1[0]]
table_display = table_display[table_display['Proj'] <= proj_var1[1]]
table_display = table_display[table_display['Team'].isin(team_var1)]
table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))]
table_display = table_display.sort_values(by='Adj Ceiling', ascending=False)
table_display = table_display.set_index('PLAYER_NAME')
st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
st.download_button(
label="Export Trending Numbers",
data=convert_df_to_csv(table_display),
file_name='Trending_export.csv',
mime='text/csv',
)
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