Update app.py

app.py

@@ -77,73 +77,133 @@ def init_baselines():
 
 DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
 
-
-col1, col2 = st.columns([1, 7])
-with col1:
-    if st.button("Load/Reset Data", key='reset1'):
-          st.cache_data.clear()
-          for key in st.session_state.keys():
-              del st.session_state[key]
-          DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
-          
-    slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
-    site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
-    if site_var1 == 'Draftkings':
-        raw_baselines = dk_raw
-    elif site_var1 == 'Fanduel':
-        raw_baselines = fd_raw
-        
-    contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
-    if contest_var1 == 'Small':
-        Contest_Size = 1000
-    elif contest_var1 == 'Medium':
-        Contest_Size = 5000
-    elif contest_var1 == 'Large':
-        Contest_Size = 10000
-    elif contest_var1 == 'Massive':
-        Contest_Size = 100000
-    strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
-    if strength_var1 == 'Not Very':
-        sharp_split = [400000,100000] 
-    elif strength_var1 == 'Average':
-        sharp_split = [500000,200000]
-    elif strength_var1 == 'Very':
-        sharp_split = [500000,300000]
-
-with col2:
-    if site_var1 == 'Draftkings':
-        st.session_state.Sim_Winner_Frame = DK_seed.head(Contest_Size)
-        st.session_state.Sim_Winner_Display = DK_seed.head(Contest_Size)
-        st.session_state.Sim_Winner_Export = DK_seed
-        st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:9].values, return_counts=True)),
-                                    columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
-        st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(Contest_Size)
-        
-        if 'Sim_Winner_Export' in st.session_state:
-                st.download_button(
-                    label="Export 500k optimals",
-                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
-                    file_name='MLB_consim_export.csv',
-                    mime='text/csv',
-                )
-        st.dataframe(st.session_state.Sim_Winner_Display.style.format(precision=2), height=500, use_container_width=True)
-        st.dataframe(st.session_state.player_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
-    elif site_var1 == 'Fanduel':
-        st.session_state.Sim_Winner_Frame = FD_seed.head(Contest_Size)
-        st.session_state.Sim_Winner_Display = FD_seed.head(Contest_Size)
-        st.session_state.Sim_Winner_Export = FD_seed
-        st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:8].values, return_counts=True)),
-                                    columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
-        st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(Contest_Size)
-        
-        if 'Sim_Winner_Export' in st.session_state:
-                st.download_button(
-                    label="Export 500k optimals",
-                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
-                    file_name='MLB_consim_export.csv',
-                    mime='text/csv',
-                )
-        st.dataframe(st.session_state.Sim_Winner_Display.style.format(precision=2), height=500, use_container_width=True)
-        st.dataframe(st.session_state.player_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
+tab1, tab2 = ['Data Export', 'Contest Sims']
+with tab1:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset1'):
+              st.cache_data.clear()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
+              DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
+              
+        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
+        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
+        if site_var1 == 'Draftkings':
+            raw_baselines = dk_raw
+        elif site_var1 == 'Fanduel':
+            raw_baselines = fd_raw
+        team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
+        if team_var1 == 'Specific Teams':
+                team_var2 = st.multiselect('Which teams do you want?', options = DK_seed['Team'].unique())
+        elif team_var1 == 'Full Players':
+                team_var2 = DK_seed.Team.values.tolist()
+    
+    with col2:
+        if site_var1 == 'Draftkings':
+            DK_seed_parse = DK_seed[DK_seed['Team'].isin[team_var2]]
+            st.session_state.data_export_display = DK_seed_parse.head(1000)
+            st.session_state.data_export = DK_seed_parse
+            st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export.iloc[:,0:9].values, return_counts=True)),
+                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
+            st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(500000)
+            
+            if 'data_export' in st.session_state:
+                    st.download_button(
+                        label="Export optimals set",
+                        data=st.session_state.data_export.to_csv().encode('utf-8'),
+                        file_name='MLB_optimals_export.csv',
+                        mime='text/csv',
+                    )
+            st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
+            st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
+        elif site_var1 == 'Fanduel':
+            FD_seed_parse = FD_seed[FD_seed['Team'].isin[team_var2]]
+            st.session_state.data_export_display = FD_seed_parse.head(1000)
+            st.session_state.data_export = FD_seed_parse
+            st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export.iloc[:,0:8].values, return_counts=True)),
+                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
+            st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(500000)
+            
+            if 'data_export' in st.session_state:
+                    st.download_button(
+                        label="Export optimals set",
+                        data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+                        file_name='MLB_optimals_export.csv',
+                        mime='text/csv',
+                    )
+            st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
+            st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
+            
+with tab2:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset2'):
+              st.cache_data.clear()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
+              DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
+              
+    #     slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
+    #     site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
+    #     if site_var1 == 'Draftkings':
+    #         raw_baselines = dk_raw
+    #     elif site_var1 == 'Fanduel':
+    #         raw_baselines = fd_raw
+            
+    #     contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
+    #     if contest_var1 == 'Small':
+    #         Contest_Size = 1000
+    #     elif contest_var1 == 'Medium':
+    #         Contest_Size = 5000
+    #     elif contest_var1 == 'Large':
+    #         Contest_Size = 10000
+    #     elif contest_var1 == 'Massive':
+    #         Contest_Size = 100000
+    #     strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
+    #     if strength_var1 == 'Not Very':
+    #         sharp_split = [400000,100000] 
+    #     elif strength_var1 == 'Average':
+    #         sharp_split = [500000,200000]
+    #     elif strength_var1 == 'Very':
+    #         sharp_split = [500000,300000]
+    
+    with col2:
+    #     if site_var1 == 'Draftkings':
+    #         st.session_state.Sim_Winner_Frame = DK_seed.head(Contest_Size)
+    #         st.session_state.Sim_Winner_Display = DK_seed.head(Contest_Size)
+    #         st.session_state.Sim_Winner_Export = DK_seed
+    #         st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:9].values, return_counts=True)),
+    #                                     columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+    #         st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
+    #         st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(Contest_Size)
+            
+    #         if 'Sim_Winner_Export' in st.session_state:
+    #                 st.download_button(
+    #                     label="Export 500k optimals",
+    #                     data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+    #                     file_name='MLB_consim_export.csv',
+    #                     mime='text/csv',
+    #                 )
+    #         st.dataframe(st.session_state.Sim_Winner_Display.style.format(precision=2), height=500, use_container_width=True)
+    #         st.dataframe(st.session_state.player_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
+    #     elif site_var1 == 'Fanduel':
+    #         st.session_state.Sim_Winner_Frame = FD_seed.head(Contest_Size)
+    #         st.session_state.Sim_Winner_Display = FD_seed.head(Contest_Size)
+    #         st.session_state.Sim_Winner_Export = FD_seed
+    #         st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:8].values, return_counts=True)),
+    #                                     columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+    #         st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
+    #         st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(Contest_Size)
+            
+    #         if 'Sim_Winner_Export' in st.session_state:
+    #                 st.download_button(
+    #                     label="Export 500k optimals",
+    #                     data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+    #                     file_name='MLB_consim_export.csv',
+    #                     mime='text/csv',
+    #                 )
+    #         st.dataframe(st.session_state.Sim_Winner_Display.style.format(precision=2), height=500, use_container_width=True)
+    #         st.dataframe(st.session_state.player_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)