Update app.py

app.py

@@ -80,10 +80,9 @@ def init_FD_seed_frame():
     return FD_seed
 
 @st.cache_data
-def convert_df_from_parquet(df):
+def convert_df(df):
     # IMPORTANT: Cache the conversion to prevent computation on every rerun
-    working_file = df.read_parquet()
-    return working_file.to_csv().encode('utf-8')
+    return df.to_csv().encode('utf-8')
 
 dk_raw, fd_raw = init_baselines()
 
@@ -138,9 +137,8 @@ with tab1:
                 DK_seed_parse = DK_seed_parse[DK_seed_parse['Team_count'].isin(stack_var2)]
                 data_export_display = DK_seed_parse.head(1000)
                 st.session_state.data_export_display = data_export_display.copy()
-                st.session_state.data_export_expo = DK_seed.copy()
-                st.session_state.data_export = DK_seed.to_parquet()
-                st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export_expo.iloc[:,0:9].values, return_counts=True)),
+                st.session_state.data_export = DK_seed.copy()
+                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']/(len(st.session_state.data_export_expo['Team']))
@@ -148,7 +146,7 @@ with tab1:
                 if 'data_export' in st.session_state:
                         st.download_button(
                             label="Export optimals set",
-                            data=convert_df_from_parquet(st.session_state.data_export),
+                            data=convert_df(st.session_state.data_export),
                             file_name='MLB_optimals_export.csv',
                             mime='text/csv',
                         )
@@ -165,9 +163,8 @@ with tab1:
                 FD_seed_parse = FD_seed_parse[FD_seed_parse['Team_count'].isin(stack_var2)]
                 data_export_display = FD_seed_parse.head(1000)
                 st.session_state.data_export_display = data_export_display.copy()
-                st.session_state.data_export_expo = FD_seed.copy()
-                st.session_state.data_export = FD_seed.to_parquet()
-                st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export_expo.iloc[:,0:8].values, return_counts=True)),
+                st.session_state.data_export = FD_seed.copy()
+                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']/(len(st.session_state.data_export_expo['Team']))
@@ -175,7 +172,7 @@ with tab1:
                 if 'data_export' in st.session_state:
                         st.download_button(
                             label="Export optimals set",
-                            data=convert_df_from_parquet(st.session_state.data_export),
+                            data=convert_df(st.session_state.data_export),
                             file_name='MLB_optimals_export.csv',
                             mime='text/csv',
                         )