fixed player search for optimals

app.py

@@ -274,22 +274,14 @@ with tab2:
                 st.session_state.working_seed = st.session_state.working_seed
                 st.session_state.working_seed = st.session_state.working_seed[st.session_state.working_seed[:,14] >= own_var_low]
                 st.session_state.working_seed = st.session_state.working_seed[st.session_state.working_seed[:,14] <= own_var_high]
-                # Create a mask to check if any column contains a player from player_var2
-                player_mask = st.session_state.working_seed.apply(lambda row: any(player in str(cell) for player in player_var2 for cell in row), axis=1)
-                
-                # Apply the mask to filter the DataFrame
-                st.session_state.working_seed = st.session_state.working_seed[player_mask]
+                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed.to_numpy(), player_var2).any(axis=1).all(axis=1)]
                 st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:151], columns=column_names)
             elif 'working_seed' not in st.session_state:
                 st.session_state.working_seed = dk_lineups.copy()
                 st.session_state.working_seed = st.session_state.working_seed
                 st.session_state.working_seed = st.session_state.working_seed[st.session_state.working_seed[:,14] >= own_var_low]
                 st.session_state.working_seed = st.session_state.working_seed[st.session_state.working_seed[:,14] <= own_var_high]
-                # Create a mask to check if any column contains a player from player_var2
-                player_mask = st.session_state.working_seed.apply(lambda row: any(player in str(cell) for player in player_var2 for cell in row), axis=1)
-                
-                # Apply the mask to filter the DataFrame
-                st.session_state.working_seed = st.session_state.working_seed[player_mask]
+                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed.to_numpy(), player_var2).any(axis=1).all(axis=1)]
                 st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:151], columns=column_names)
             
         elif site_var1 == 'Fanduel':
@@ -297,22 +289,14 @@ with tab2:
                 st.session_state.working_seed = st.session_state.working_seed
                 st.session_state.working_seed = st.session_state.working_seed[st.session_state.working_seed[:,15] >= own_var_low]
                 st.session_state.working_seed = st.session_state.working_seed[st.session_state.working_seed[:,15] <= own_var_high]
-                # Create a mask to check if any column contains a player from player_var2
-                player_mask = st.session_state.working_seed.apply(lambda row: any(player in str(cell) for player in player_var2 for cell in row), axis=1)
-                
-                # Apply the mask to filter the DataFrame
-                st.session_state.working_seed = st.session_state.working_seed[player_mask]
+                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed.to_numpy(), player_var2).any(axis=1).all(axis=1)]
                 st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:151], columns=column_names)
             elif 'working_seed' not in st.session_state:
                 st.session_state.working_seed = fd_lineups.copy()
                 st.session_state.working_seed = st.session_state.working_seed
                 st.session_state.working_seed = st.session_state.working_seed[st.session_state.working_seed[:,15] >= own_var_low]
                 st.session_state.working_seed = st.session_state.working_seed[st.session_state.working_seed[:,15] <= own_var_high]
-                # Create a mask to check if any column contains a player from player_var2
-                player_mask = st.session_state.working_seed.apply(lambda row: any(player in str(cell) for player in player_var2 for cell in row), axis=1)
-                
-                # Apply the mask to filter the DataFrame
-                st.session_state.working_seed = st.session_state.working_seed[player_mask]
+                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed.to_numpy(), player_var2).any(axis=1).all(axis=1)]
                 st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:151], columns=column_names)
                 
         with st.container():