Update app.py

app.py

@@ -31,10 +31,67 @@ mma_format = {'ML_perc': '{:.2%}', 'Min_%': '{:.2%}', 'Med_%': '{:.2%}', }
 @st.cache_resource(ttl = 600)
 def init_baselines():
     sh = gc.open_by_url("https://docs.google.com/spreadsheets/d/17OAf4OAfW92-loMNUFvIubNmgF9111dsObybo6xhtYY/edit?gid=1468336051#gid=1468336051")
+    worksheet = sh.worksheet('QB')
+    all_values = worksheet.get_all_values()
+    cell_vals = [row[0:7] for row in all_values[2:500]]
+    frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank'])
+    frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
+    qb_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $']]
+    string_cols = ['Team']
+    qb_frame = qb_frame.drop_duplicates(subset='Player')
+    qb_frame = qb_frame.set_index('Player')
+    
+    for col in qb_frame.columns:
+        if col not in string_cols:
+            try:
+                qb_frame[col] = pd.to_numeric(qb_frame[col], errors='coerce')
+            except ValueError:
+                pass  # Ignore columns that cannot be converted
+                
+    qb_frame = qb_frame.sort_values(by='PointsAvg', ascending=False)
+    
+    worksheet = sh.worksheet('RB')
+    all_values = worksheet.get_all_values()
+    cell_vals = [row[0:7] for row in all_values[2:500]]
+    frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank'])
+    frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
+    rb_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $']]
+    string_cols = ['Team']
+    rb_frame = rb_frame.drop_duplicates(subset='Player')
+    rb_frame = rb_frame.set_index('Player')
+    
+    for col in rb_frame.columns:
+        if col not in string_cols:
+            try:
+                rb_frame[col] = pd.to_numeric(rb_frame[col], errors='coerce')
+            except ValueError:
+                pass  # Ignore columns that cannot be converted
+                
+    rb_frame = rb_frame.sort_values(by='PointsAvg', ascending=False)
+    
+    worksheet = sh.worksheet('WR')
+    all_values = worksheet.get_all_values()
+    cell_vals = [row[0:7] for row in all_values[2:500]]
+    frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank'])
+    frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
+    wr_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $']]
+    string_cols = ['Team']
+    wr_frame = wr_frame.drop_duplicates(subset='Player')
+    wr_frame = wr_frame.set_index('Player')
+    
+    for col in wr_frame.columns:
+        if col not in string_cols:
+            try:
+                wr_frame[col] = pd.to_numeric(wr_frame[col], errors='coerce')
+            except ValueError:
+                pass  # Ignore columns that cannot be converted
+                
+    wr_frame = wr_frame.sort_values(by='PointsAvg', ascending=False)
+    
     worksheet = sh.worksheet('Flex')
     all_values = worksheet.get_all_values()
-    cell_vals = [row[0:13] for row in all_values[2:500]]
-    frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'chop1', 'chop2', 'chop3', 'chop4', 'chop6', 'OPP'])
+    cell_vals = [row[0:7] for row in all_values[2:500]]
+    frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank'])
     frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
     flex_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $']]
     string_cols = ['Team']
@@ -49,19 +106,50 @@ def init_baselines():
                 pass  # Ignore columns that cannot be converted
                 
     flex_frame = flex_frame.sort_values(by='PointsAvg', ascending=False)
-    return flex_frame
+    
+    return qb_frame, rb_frame, wr_frame, flex_frame
 
 @st.cache_resource()
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-flex_base = init_baselines()
+qb_frame, rb_frame, wr_frame, flex_frame = init_baselines()
+
+tab1, tab2, tab3, tab4 = st.tabs(['QB data', 'RB data', 'WR data', 'Flex data'])
 
-with st.container():
-          st.dataframe(flex_base.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
-          st.download_button(
-                  label="Export Tables",
-                  data=convert_df_to_csv(flex_base),
-                  file_name='tennis_model_export.csv',
-                  mime='text/csv',
-          )
+with tab1:
+    with st.container():
+              st.dataframe(qb_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
+              st.download_button(
+                      label="Export Tables",
+                      data=convert_df_to_csv(qb_frame),
+                      file_name='NCAAF_model_export.csv',
+                      mime='text/csv',
+              )
+with tab2:
+    with st.container():
+              st.dataframe(rb_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
+              st.download_button(
+                      label="Export Tables",
+                      data=convert_df_to_csv(rb_frame),
+                      file_name='NCAAF_model_export.csv',
+                      mime='text/csv',
+              )
+with tab3:
+    with st.container():
+              st.dataframe(wr_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
+              st.download_button(
+                      label="Export Tables",
+                      data=convert_df_to_csv(wr_frame),
+                      file_name='NCAAF_model_export.csv',
+                      mime='text/csv',
+              )
+with tab4:
+    with st.container():
+              st.dataframe(flex_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
+              st.download_button(
+                      label="Export Tables",
+                      data=convert_df_to_csv(flex_frame),
+                      file_name='NCAAF_model_export.csv',
+                      mime='text/csv',
+              )