Update app.py

app.py

@@ -840,6 +840,8 @@ with tab2:
         st.info(t_stamp)
         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_roo_raw = load_dk_player_projections()
               fd_roo_raw = load_fd_player_projections()
               t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
@@ -891,7 +893,20 @@ with tab2:
             st.session_state.Sim_Winner_Frame = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
         if 'Sim_Winner_Export' not in st.session_state:
             st.session_state.Sim_Winner_Export = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-            
+        if 'player_freq' not in st.session_state: 
+            st.session_state.player_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'qb_freq' not in st.session_state: 
+            st.session_state.qb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'rb_freq' not in st.session_state: 
+            st.session_state.rb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'wr_freq' not in st.session_state: 
+            st.session_state.wr_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'te_freq' not in st.session_state: 
+            st.session_state.te_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'flex_freq' not in st.session_state: 
+            st.session_state.flex_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'dst_freq' not in st.session_state: 
+            st.session_state.dst_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
     with col2:
         with st.container():
             if st.button("Simulate Contest"):
@@ -1340,8 +1355,16 @@ with tab2:
                 
         with st.container():
             simulate_container = st.empty()
-            if "df" not in st.session_state:
-                st.session_state["df"] = None
+            player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'))
+            if player_split_var2 == 'Specific Players':
+                      find_var2 = st.multiselect('Which players must be included in the lineups?', options = player_freq['Player'].unique())
+            elif player_split_var2 == 'Full Players':
+                      find_var2 = player_freq.Player.values.tolist()
+
+            if player_split_var2 == 'Specific Players':
+                      st.session_state.Sim_Winner_Frame = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
+            elif player_split_var2 == 'Full Players':
+                      st.session_state.Sim_Winner_Frame = st.session_state.Sim_Winner_Frame
             st.dataframe(st.session_state.Sim_Winner_Frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
         
             st.download_button(
@@ -1358,7 +1381,7 @@ with tab2:
                 st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                 st.download_button(
                     label="Export Exposures",
-                    data=convert_df_to_csv(player_freq),
+                    data=convert_df_to_csv(st.session_state.player_freq),
                     file_name='player_freq_export.csv',
                     mime='text/csv',
                 )
@@ -1366,7 +1389,7 @@ with tab2:
                 st.dataframe(qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                 st.download_button(
                     label="Export Exposures",
-                    data=convert_df_to_csv(qb_freq),
+                    data=convert_df_to_csv(st.session_state.qb_freq),
                     file_name='qb_freq_export.csv',
                     mime='text/csv',
                 )
@@ -1374,7 +1397,7 @@ with tab2:
                 st.dataframe(rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                 st.download_button(
                     label="Export Exposures",
-                    data=convert_df_to_csv(rb_freq),
+                    data=convert_df_to_csv(st.session_state.rb_freq),
                     file_name='rb_freq_export.csv',
                     mime='text/csv',
                 )
@@ -1382,7 +1405,7 @@ with tab2:
                 st.dataframe(wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                 st.download_button(
                     label="Export Exposures",
-                    data=convert_df_to_csv(wr_freq),
+                    data=convert_df_to_csv(st.session_state.wr_freq),
                     file_name='wr_freq_export.csv',
                     mime='text/csv',
                 )
@@ -1390,7 +1413,7 @@ with tab2:
                 st.dataframe(te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                 st.download_button(
                     label="Export Exposures",
-                    data=convert_df_to_csv(te_freq),
+                    data=convert_df_to_csv(st.session_state.te_freq),
                     file_name='te_freq_export.csv',
                     mime='text/csv',
                 )
@@ -1398,7 +1421,7 @@ with tab2:
                 st.dataframe(flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                 st.download_button(
                     label="Export Exposures",
-                    data=convert_df_to_csv(flex_freq),
+                    data=convert_df_to_csv(st.session_state.flex_freq),
                     file_name='flex_freq_export.csv',
                     mime='text/csv',
                 )
@@ -1406,7 +1429,7 @@ with tab2:
                 st.dataframe(dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                 st.download_button(
                     label="Export Exposures",
-                    data=convert_df_to_csv(dst_freq),
+                    data=convert_df_to_csv(st.session_state.dst_freq),
                     file_name='dst_freq_export.csv',
                     mime='text/csv',
                 )