Update app.py

app.py

@@ -60,9 +60,9 @@ def init_baselines():
     
     fd_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']]
     
-    dk_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 FANTASY', 'L5 FANTASY', 'L3 FANTASY', 'Trend Median']]
+    dk_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median']]
     
-    fd_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 FD_FANTASY', 'L5 FD_FANTASY', 'L3 FD_FANTASY', 'Trend FD_Median']]
+    fd_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy', 'Trend FD_Median']]
     
     dk_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'DK_Salary', 'DK_Proj', 'Adj Median', 'DK_Avg_Val', 'Adj Ceiling', 'DK_Ceiling_Value']]
     
@@ -103,7 +103,7 @@ with col1:
                                         'L3 Ceiling', 'Trend Min', 'Trend Median', 'Proj', 'Adj Median', 'Adj Ceiling',
                                         'Salary', 'Avg_Val', 'Ceiling_Value'], axis=1)
     minutes_table = minutes_table.set_axis(['PLAYER_NAME', 'Team', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min'], axis=1)
-    medians_table = medians_table.set_axis(['PLAYER_NAME', 'Team', 'L10 FANTASY','L5 FANTASY', 'L3 FANTASY', 'Trend Median'], axis=1)
+    medians_table = medians_table.set_axis(['PLAYER_NAME', 'Team', 'L10 Fantasy','L5 Fantasy', 'L3 Fantasy', 'Trend Median'], axis=1)
     proj_medians_table = proj_medians_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Salary', 'Proj',
                                                       'Adj Median', 'Avg_Val', 'Adj Ceiling', 'Ceiling_Value'], axis=1)
     if split_var1 == 'Overall':
@@ -166,7 +166,7 @@ with col2:
         table_display = table_display[table_display['Position'].isin(pos_var1)]
         table_display = table_display.sort_values(by='Adj Ceiling', ascending=False)
         table_display = table_display.set_index('PLAYER_NAME')
-        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
         st.download_button(
             label="Export Trending Numbers",
             data=convert_df_to_csv(table_display),
@@ -177,7 +177,7 @@ with col2:
     elif split_var1 == 'Minutes Trends':
         table_display = minutes_table[minutes_table['Team'].isin(team_var1)]
         table_display = table_display.set_index('PLAYER_NAME')
-        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
         st.download_button(
             label="Export Trending Numbers",
             data=convert_df_to_csv(table_display),
@@ -188,7 +188,7 @@ with col2:
     elif split_var1 == 'Fantasy Trends':
         table_display = medians_table[medians_table['Team'].isin(team_var1)]
         table_display = table_display.set_index('PLAYER_NAME')
-        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
         st.download_button(
             label="Export Trending Numbers",
             data=convert_df_to_csv(table_display),
@@ -203,7 +203,7 @@ with col2:
         table_display = table_display[table_display['Position'].isin(pos_var1)]
         table_display = table_display.sort_values(by='Adj Ceiling', ascending=False)
         table_display = table_display.set_index('PLAYER_NAME')
-        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
         st.download_button(
             label="Export Trending Numbers",
             data=convert_df_to_csv(table_display),