James McCool commited on
Commit
fb4d438
·
1 Parent(s): b88451c

SET PLAYER INDEXING

Browse files
Files changed (1) hide show
  1. app.py +83 -75
app.py CHANGED
@@ -348,21 +348,22 @@ with tab2:
348
 
349
  with col2:
350
  if custom_var2 == 'No':
351
- final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
352
- final_Proj = final_Proj[final_Proj['Salary'] >= sal_var2[0]]
353
- final_Proj = final_Proj[final_Proj['Salary'] <= sal_var2[1]]
354
- if pos_var2 != 'All':
355
- final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var2))]
356
- final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
357
- final_Proj = final_Proj.set_index('Player')
358
- final_Proj = final_Proj.sort_values(by='Median', ascending=False)
359
- st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
360
- st.download_button(
361
- label="Export Tables",
362
- data=convert_df_to_csv(final_Proj),
363
- file_name='NFL_overall_export.csv',
364
- mime='text/csv',
365
- )
 
366
  elif custom_var2 == 'Yes':
367
  hold_container = st.empty()
368
  if st.button('Create Range of Outcomes for Slate'):
@@ -524,21 +525,22 @@ with tab3:
524
 
525
  with col2:
526
  if custom_var3 == 'No':
527
- final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var3)]
528
- final_Proj = final_Proj[final_Proj['Salary'] >= sal_var3[0]]
529
- final_Proj = final_Proj[final_Proj['Salary'] <= sal_var3[1]]
530
- if pos_var3 != 'All':
531
- final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var3))]
532
- final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
533
- final_Proj = final_Proj.set_index('Player')
534
- final_Proj = final_Proj.sort_values(by='Median', ascending=False)
535
- st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
536
- st.download_button(
537
- label="Export Tables",
538
- data=convert_df_to_csv(final_Proj),
539
- file_name='NFL_qb_export.csv',
540
- mime='text/csv',
541
- )
 
542
  elif custom_var3 == 'Yes':
543
  hold_container = st.empty()
544
  if st.button('Create Range of Outcomes for Slate'):
@@ -700,21 +702,23 @@ with tab4:
700
 
701
  with col2:
702
  if custom_var4 == 'No':
703
- final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var4)]
704
- final_Proj = final_Proj[final_Proj['Salary'] >= sal_var4[0]]
705
- final_Proj = final_Proj[final_Proj['Salary'] <= sal_var4[1]]
706
- if pos_var4 != 'All':
707
- final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var4))]
708
- final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
709
- final_Proj = final_Proj.set_index('Player')
710
- final_Proj = final_Proj.sort_values(by='Median', ascending=False)
711
- st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
712
- st.download_button(
713
- label="Export Tables",
714
- data=convert_df_to_csv(final_Proj),
715
- file_name='NFL_rb_export.csv',
716
- mime='text/csv',
717
- )
 
 
718
  elif custom_var4 == 'Yes':
719
  hold_container = st.empty()
720
  if st.button('Create Range of Outcomes for Slate'):
@@ -876,21 +880,23 @@ with tab5:
876
 
877
  with col2:
878
  if custom_var5 == 'No':
879
- final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var5)]
880
- final_Proj = final_Proj[final_Proj['Salary'] >= sal_var5[0]]
881
- final_Proj = final_Proj[final_Proj['Salary'] <= sal_var5[1]]
882
- if pos_var5 != 'All':
883
- final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var5))]
884
- final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
885
- final_Proj = final_Proj.set_index('Player')
886
- final_Proj = final_Proj.sort_values(by='Median', ascending=False)
887
- st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
888
- st.download_button(
889
- label="Export Tables",
890
- data=convert_df_to_csv(final_Proj),
891
- file_name='NFL_wr_export.csv',
892
- mime='text/csv',
893
- )
 
 
894
  elif custom_var5 == 'Yes':
895
  hold_container = st.empty()
896
  if st.button('Create Range of Outcomes for Slate'):
@@ -1052,21 +1058,23 @@ with tab6:
1052
 
1053
  with col2:
1054
  if custom_var6 == 'No':
1055
- final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var6)]
1056
- final_Proj = final_Proj[final_Proj['Salary'] >= sal_var6[0]]
1057
- final_Proj = final_Proj[final_Proj['Salary'] <= sal_var6[1]]
1058
- if pos_var6 != 'All':
1059
- final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var6))]
1060
- final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
1061
- final_Proj = final_Proj.set_index('Player')
1062
- final_Proj = final_Proj.sort_values(by='Median', ascending=False)
1063
- st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
1064
- st.download_button(
1065
- label="Export Tables",
1066
- data=convert_df_to_csv(final_Proj),
1067
- file_name='NFL_te_export.csv',
1068
- mime='text/csv',
1069
- )
 
 
1070
  elif custom_var6 == 'Yes':
1071
  hold_container = st.empty()
1072
  if st.button('Create Range of Outcomes for Slate'):
 
348
 
349
  with col2:
350
  if custom_var2 == 'No':
351
+ final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
352
+ final_Proj = final_Proj[final_Proj['Salary'] >= sal_var2[0]]
353
+ final_Proj = final_Proj[final_Proj['Salary'] <= sal_var2[1]]
354
+ if pos_var2 != 'All':
355
+ final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var2))]
356
+ final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
357
+ final_Proj = final_Proj.sort_values(by='Median', ascending=False)
358
+
359
+ final_Proj = final_Proj.set_index('Player')
360
+ st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
361
+ st.download_button(
362
+ label="Export Tables",
363
+ data=convert_df_to_csv(final_Proj),
364
+ file_name='NFL_overall_export.csv',
365
+ mime='text/csv',
366
+ )
367
  elif custom_var2 == 'Yes':
368
  hold_container = st.empty()
369
  if st.button('Create Range of Outcomes for Slate'):
 
525
 
526
  with col2:
527
  if custom_var3 == 'No':
528
+ final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var3)]
529
+ final_Proj = final_Proj[final_Proj['Salary'] >= sal_var3[0]]
530
+ final_Proj = final_Proj[final_Proj['Salary'] <= sal_var3[1]]
531
+ if pos_var3 != 'All':
532
+ final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var3))]
533
+ final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
534
+ final_Proj = final_Proj.sort_values(by='Median', ascending=False)
535
+
536
+ final_Proj = final_Proj.set_index('Player')
537
+ st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
538
+ st.download_button(
539
+ label="Export Tables",
540
+ data=convert_df_to_csv(final_Proj),
541
+ file_name='NFL_qb_export.csv',
542
+ mime='text/csv',
543
+ )
544
  elif custom_var3 == 'Yes':
545
  hold_container = st.empty()
546
  if st.button('Create Range of Outcomes for Slate'):
 
702
 
703
  with col2:
704
  if custom_var4 == 'No':
705
+ final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var4)]
706
+ final_Proj = final_Proj[final_Proj['Salary'] >= sal_var4[0]]
707
+ final_Proj = final_Proj[final_Proj['Salary'] <= sal_var4[1]]
708
+ if pos_var4 != 'All':
709
+ final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var4))]
710
+ final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
711
+ final_Proj = final_Proj.set_index('Player')
712
+ final_Proj = final_Proj.sort_values(by='Median', ascending=False)
713
+
714
+ final_Proj = final_Proj.set_index('Player')
715
+ st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
716
+ st.download_button(
717
+ label="Export Tables",
718
+ data=convert_df_to_csv(final_Proj),
719
+ file_name='NFL_rb_export.csv',
720
+ mime='text/csv',
721
+ )
722
  elif custom_var4 == 'Yes':
723
  hold_container = st.empty()
724
  if st.button('Create Range of Outcomes for Slate'):
 
880
 
881
  with col2:
882
  if custom_var5 == 'No':
883
+ final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var5)]
884
+ final_Proj = final_Proj[final_Proj['Salary'] >= sal_var5[0]]
885
+ final_Proj = final_Proj[final_Proj['Salary'] <= sal_var5[1]]
886
+ if pos_var5 != 'All':
887
+ final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var5))]
888
+ final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
889
+ final_Proj = final_Proj.set_index('Player')
890
+ final_Proj = final_Proj.sort_values(by='Median', ascending=False)
891
+
892
+ final_Proj = final_Proj.set_index('Player')
893
+ st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
894
+ st.download_button(
895
+ label="Export Tables",
896
+ data=convert_df_to_csv(final_Proj),
897
+ file_name='NFL_wr_export.csv',
898
+ mime='text/csv',
899
+ )
900
  elif custom_var5 == 'Yes':
901
  hold_container = st.empty()
902
  if st.button('Create Range of Outcomes for Slate'):
 
1058
 
1059
  with col2:
1060
  if custom_var6 == 'No':
1061
+ final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var6)]
1062
+ final_Proj = final_Proj[final_Proj['Salary'] >= sal_var6[0]]
1063
+ final_Proj = final_Proj[final_Proj['Salary'] <= sal_var6[1]]
1064
+ if pos_var6 != 'All':
1065
+ final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var6))]
1066
+ final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
1067
+ final_Proj = final_Proj.set_index('Player')
1068
+ final_Proj = final_Proj.sort_values(by='Median', ascending=False)
1069
+
1070
+ final_Proj = final_Proj.set_index('Player')
1071
+ st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
1072
+ st.download_button(
1073
+ label="Export Tables",
1074
+ data=convert_df_to_csv(final_Proj),
1075
+ file_name='NFL_te_export.csv',
1076
+ mime='text/csv',
1077
+ )
1078
  elif custom_var6 == 'Yes':
1079
  hold_container = st.empty()
1080
  if st.button('Create Range of Outcomes for Slate'):