Spaces:
Running
Running
File size: 15,111 Bytes
9b82ed9 056c8db 9b82ed9 025741f 9b82ed9 132cb49 9b82ed9 132cb49 9b82ed9 132cb49 9b82ed9 132cb49 9b82ed9 132cb49 9b82ed9 132cb49 9b82ed9 132cb49 9b82ed9 0134c99 9b82ed9 0134c99 9b82ed9 0134c99 132cb49 0134c99 132cb49 9b82ed9 132cb49 0134c99 132cb49 0134c99 132cb49 9b82ed9 132cb49 0134c99 132cb49 0134c99 132cb49 9b82ed9 0134c99 9b82ed9 0134c99 9b82ed9 056c8db 9b82ed9 132cb49 9b82ed9 132cb49 9b82ed9 132cb49 9b82ed9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 |
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
from database import gcservice_account
import os
NBA_Data = os.getenv('NBA_Data')
@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', 'Season MIN', 'Season Fantasy', 'Season FPPM', 'Season Ceiling', 'Season FD_Fantasy', 'Season FD_Ceiling', 'L10 MIN', 'L10 Fantasy', 'L10 FPPM', 'L10 Ceiling', 'L10 FD_Fantasy',
'L10 FD_Ceiling', 'L5 MIN', 'L5 Fantasy', 'L5 FPPM', 'L5 Ceiling', 'L5 FD_Fantasy', 'L5 FD_Ceiling', 'L3 MIN', 'L3 Fantasy',
'L3 FPPM', 'L3 Ceiling', 'L3 FD_Fantasy', 'L3 FD_Ceiling', 'Trend Min', 'Trend Median', 'Trend FPPM', '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)
trend_table = trend_table.dropna(subset=['Position'])
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', 'Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']]
fd_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']]
dk_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median']]
fd_medians_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'Season FD_Fantasy', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy', 'Trend FD_Median']]
dk_fppm_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM']]
fd_fppm_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM']]
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_fppm_table, fd_fppm_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_fppm_table, fd_fppm_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', 'FPPM 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', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy',
'Trend Median', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value']]
minutes_table = dk_minutes_table
medians_table = dk_medians_table
fppm_table = dk_fppm_table
proj_medians_table = dk_proj_medians_table
elif site_var1 == 'Fanduel':
trend_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min', 'Season FD_Fantasy', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy',
'Trend FD_Median', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM', '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
fppm_table = fd_fppm_table
proj_medians_table = fd_proj_medians_table
trend_table = trend_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy',
'Trend Median', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
'Salary', 'Avg_Val', 'Ceiling_Value'], axis=1)
minutes_table = minutes_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min'], axis=1)
medians_table = medians_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median'], axis=1)
fppm_table = fppm_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM'], 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_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var2')
if split_var2 == 'Specific Positions':
pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
elif split_var2 == '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
pos_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='pos_var2')
if pos_var2 == 'Specific Positions':
pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
elif pos_var2 == 'All':
pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
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
pos_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='pos_var2')
if pos_var2 == 'Specific Positions':
pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
elif pos_var2 == 'All':
pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
elif split_var1 == 'FPPM 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
pos_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='pos_var2')
if pos_var2 == 'Specific Positions':
pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
elif pos_var2 == 'All':
pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
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
pos_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='pos_var2')
if pos_var2 == 'Specific Positions':
pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
elif pos_var2 == '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[table_display['Position'].str.contains('|'.join(pos_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[table_display['Position'].str.contains('|'.join(pos_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 == 'FPPM Trends':
table_display = fppm_table[fppm_table['Team'].isin(team_var1)]
table_display = table_display[table_display['Position'].str.contains('|'.join(pos_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='NBA_Trending_export.csv',
mime='text/csv',
)
|