Removed custom ROO, adjusted some object names, added a TLL to data

app.py

@@ -35,7 +35,7 @@ gspreadcon = init_conn()
 
 dk_player_url = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260'
 
-@st.cache_data
+@st.cache_data(ttl=300)
 def load_overall_stats():
     sh = gspreadcon.open_by_url(dk_player_url)
     worksheet = sh.worksheet('DK_Build_Up')
@@ -83,8 +83,7 @@ def load_overall_stats():
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     roo_raw = raw_display.sort_values(by='Median', ascending=False)
 
-    worksheet = sh.worksheet('Timestamp')
-    timestamp = worksheet.acell('A1').value
+    timestamp = raw_display['timestamp'].values[0]
 
     return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp
 
@@ -94,25 +93,9 @@ def convert_df_to_csv(df):
 dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
-tab1, tab2, tab3 = st.tabs(['Uploads and Info', 'Range of Outcomes', 'Custom Range of Outcomes'])
+tab1, tab2, tab3 = st.tabs(['Range of Outcomes', 'Uploads and Info'])
 
 with tab1:
-    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own'.")
-    col1, col2 = st.columns([1, 5])
-
-    with col1:
-        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
-    
-        if proj_file is not None:
-                  try:
-                            proj_dataframe = pd.read_csv(proj_file)
-                  except:
-                            proj_dataframe = pd.read_excel(proj_file)
-    with col2:
-        if proj_file is not None:  
-                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-
-with tab2:
 
     col1, col2 = st.columns([1, 9])
 
@@ -126,15 +109,15 @@ with tab2:
                   del st.session_state[key]
         site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
         if site_var2 == 'Draftkings':
-            site_baselines = roo_raw[roo_raw['Site'] == 'Draftkings']
+            site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
         elif site_var2 == 'Fanduel':
-            site_baselines = roo_raw[roo_raw['Site'] == 'Fanduel']
+            site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
         slate_split = st.radio("Are you viewing the main slate or the secondary slate?", ('Main Slate', 'Secondary'), key='slate_split')
         if slate_split == 'Main Slate':
-            raw_baselines = site_baselines[site_baselines['Slate'] == 'Main Slate']
+            raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
         elif slate_split == 'Secondary':
-            raw_baselines = site_baselines[site_baselines['Slate'] == 'Secondary']
-        split_var2 = st.radio("Are you running the full slate or crtain games?", ('Full Slate Run', 'Specific Games'), key='split_var2')
+            raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary']
+        split_var2 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var2')
         if split_var2 == 'Specific Games':
             team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2')
         elif split_var2 == 'Full Slate Run':
@@ -155,7 +138,7 @@ with tab2:
                     st.session_state.display_proj = st.session_state.display_proj
                 elif pos_var2 != 'All':
                     st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
-                st.dataframe(st.session_state.display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+                st.dataframe(st.session_state.display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True)
         
         with display_dl_container_1:
                 display_dl_container = st.empty()
@@ -167,147 +150,18 @@ with tab2:
                                 mime='text/csv',
                     )
 
-with tab3:
-
-    col1, col2 = st.columns([1, 9])
+with tab2:
+    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own'.")
+    col1, col2 = st.columns([1, 5])
 
     with col1:
-        st.info(t_stamp)
-        if st.button("Load/Reset Data", key='reset2'):
-              st.cache_data.clear()
-              dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-              for key in st.session_state.keys():
-                  del st.session_state[key]
-        slate_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='slate_var1')
-        site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')  
-        slate_split2 = st.radio("Are you viewing the main slate or the secondary slate?", ('Main Slate', 'Secondary'), key='slate_split2')
-        if site_var1 == 'Draftkings':
-              if slate_var1 == 'User':
-                  raw_baselines = proj_dataframe
-              elif slate_var1 != 'User':
-                  if slate_split2 == 'Main Slate':
-                      raw_baselines = dk_raw
-                  elif slate_split2 == 'Secondary':
-                      raw_baselines = dk_raw_sec
-        elif site_var1 == 'Fanduel':
-              if slate_var1 == 'User':
-                  raw_baselines = proj_dataframe
-              elif slate_var1 != 'User':
-                  if slate_split2 == 'Main Slate':
-                      raw_baselines = fd_raw
-                  elif slate_split2 == 'Secondary':
-                      raw_baselines = fd_raw_sec
-        split_var1 = st.radio("Are you running the full slate or crtain 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 = raw_baselines['Team'].unique(), key='team_var1')
-        elif split_var1 == 'Full Slate Run':
-            team_var1 = raw_baselines.Team.values.tolist()
-        pos_var1 = st.selectbox('View specific position?', options = ['All', 'PG', 'SG', 'SF', 'PF', 'C'])
-
+        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
+    
+        if proj_file is not None:
+                  try:
+                            proj_dataframe = pd.read_csv(proj_file)
+                  except:
+                            proj_dataframe = pd.read_excel(proj_file)
     with col2:
-        display_container = st.empty()
-        display_dl_container = st.empty()
-        hold_container = st.empty()
-        if st.button('Create Range of Outcomes for Slate'):
-            with hold_container:
-                        
-                        working_roo = raw_baselines
-                        working_roo = working_roo[working_roo['Team'].isin(team_var1)]
-                        own_dict = dict(zip(working_roo.Player, working_roo.Own))
-                        min_dict = dict(zip(working_roo.Player, working_roo.Minutes))
-                        team_dict = dict(zip(working_roo.Player, working_roo.Team))
-                        total_sims = 1000
-
-                        flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
-                        flex_file.rename(columns={"Agg": "Median"}, inplace = True)
-                        flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                        flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
-                        flex_file['STD'] = (flex_file['Median']/4)
-                        flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
-                        hold_file = flex_file
-                        overall_file = flex_file
-                        salary_file = flex_file
-
-                        overall_players = overall_file[['Player']]
-
-                        for x in range(0,total_sims):    
-                            salary_file[x] = salary_file['Salary']
-
-                        salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                        salary_file.astype('int').dtypes
-
-                        salary_file = salary_file.div(1000)
-
-                        for x in range(0,total_sims):    
-                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-
-                        overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                        overall_file.astype('int').dtypes
-
-                        players_only = hold_file[['Player']]
-                        raw_lineups_file = players_only
-
-                        for x in range(0,total_sims):
-                            maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
-                            raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
-                            players_only[x] = raw_lineups_file[x].rank(ascending=False)
-
-                        players_only=players_only.drop(['Player'], axis=1)
-                        players_only.astype('int').dtypes
-
-                        salary_2x_check = (overall_file - (salary_file*4))
-                        salary_3x_check = (overall_file - (salary_file*5))
-                        salary_4x_check = (overall_file - (salary_file*6))
-                        gpp_check = (overall_file - ((salary_file*5)+10))
-
-                        players_only['Average_Rank'] = players_only.mean(axis=1)
-                        players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
-                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
-                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
-                        players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
-                        players_only['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
-                        players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
-                        players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-                        players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
-
-                        players_only['Player'] = hold_file[['Player']]
-
-                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
-
-                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
-                        final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
-
-                        final_Proj['Own'] = final_Proj['Player'].map(own_dict)
-                        final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
-                        final_Proj['Team'] = final_Proj['Player'].map(team_dict)
-                        final_Proj['Own'] = final_Proj['Own'].astype('float')
-                        final_Proj['LevX'] = ((final_Proj[['Top_finish', '4x%', 'Top_5_finish']].mean(axis=1))*100) - final_Proj['Own']
-                        final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
-
-                        final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']]
-                        final_Proj = final_Proj.set_index('Player')
-                        final_Proj = final_Proj.sort_values(by='Median', ascending=False)
-                        
-                        st.session_state.final_Proj = final_Proj
-                        
-                        hold_container = st.empty()
-            
-        with display_container:
-            display_container = st.empty()
-            if 'final_Proj' in st.session_state:
-                if pos_var1 == 'All':
-                    st.session_state.final_Proj = st.session_state.final_Proj
-                elif pos_var1 != 'All':
-                    st.session_state.final_Proj = st.session_state.final_Proj[st.session_state.final_Proj['Position'].str.contains(pos_var1)]
-                st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-        
-        with display_dl_container:
-                display_dl_container = st.empty()
-                if 'final_Proj' in st.session_state:
-                    st.download_button(
-                                label="Export Tables",
-                                data=convert_df_to_csv(st.session_state.final_Proj),
-                                file_name='Custom_NBA_export.csv',
-                                mime='text/csv',
-                    )
+        if proj_file is not None:  
+                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)