Spaces:

Multichem
/

Tribe_NCAAF

Sleeping

App Files Files Community

Multichem commited on Aug 29, 2024

Commit

e239116

verified ·

1 Parent(s): 7e61f4f

Update app.py

Browse files

Files changed (1) hide show

app.py +12 -4

app.py CHANGED Viewed

@@ -36,7 +36,9 @@ def init_baselines():
     cell_vals = [row[0:11] for row in all_values[2:500]]
     frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank'])
     frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
-    qb_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'GPP Rank']]
     string_cols = ['Team']
     qb_frame = qb_frame.drop_duplicates(subset='Player')
     qb_frame = qb_frame.set_index('Player')
@@ -55,7 +57,9 @@ def init_baselines():
     cell_vals = [row[0:11] for row in all_values[2:500]]
     frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank'])
     frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
-    rb_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'GPP Rank']]
     string_cols = ['Team']
     rb_frame = rb_frame.drop_duplicates(subset='Player')
     rb_frame = rb_frame.set_index('Player')
@@ -74,7 +78,9 @@ def init_baselines():
     cell_vals = [row[0:11] for row in all_values[2:500]]
     frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank'])
     frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
-    wr_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'GPP Rank']]
     string_cols = ['Team']
     wr_frame = wr_frame.drop_duplicates(subset='Player')
     wr_frame = wr_frame.set_index('Player')
@@ -93,7 +99,9 @@ def init_baselines():
     cell_vals = [row[0:11] for row in all_values[2:500]]
     frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank'])
     frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
-    flex_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'GPP Rank']]
     string_cols = ['Team']
     flex_frame = flex_frame.drop_duplicates(subset='Player')
     flex_frame = flex_frame.set_index('Player')

     cell_vals = [row[0:11] for row in all_values[2:500]]
     frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank'])
     frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
+    frame_hold['Floor'] = frame_hold['PointsAvg'] * .15
+    frame_hold['Ceiling'] = frame_hold['PointsAvg'] * 1.85
+    qb_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'Floor', 'PointsAvg', 'Ceiling', 'Points per $', 'GPP Rank']]
     string_cols = ['Team']
     qb_frame = qb_frame.drop_duplicates(subset='Player')
     qb_frame = qb_frame.set_index('Player')
     cell_vals = [row[0:11] for row in all_values[2:500]]
     frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank'])
     frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
+    frame_hold['Floor'] = frame_hold['PointsAvg'] * .15
+    frame_hold['Ceiling'] = frame_hold['PointsAvg'] * 1.85
+    rb_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'Floor', 'PointsAvg', 'Ceiling', 'Points per $', 'GPP Rank']]
     string_cols = ['Team']
     rb_frame = rb_frame.drop_duplicates(subset='Player')
     rb_frame = rb_frame.set_index('Player')
     cell_vals = [row[0:11] for row in all_values[2:500]]
     frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank'])
     frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
+    frame_hold['Floor'] = frame_hold['PointsAvg'] * .15
+    frame_hold['Ceiling'] = frame_hold['PointsAvg'] * 1.85
+    wr_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'Floor', 'PointsAvg', 'Ceiling', 'Points per $', 'GPP Rank']]
     string_cols = ['Team']
     wr_frame = wr_frame.drop_duplicates(subset='Player')
     wr_frame = wr_frame.set_index('Player')
     cell_vals = [row[0:11] for row in all_values[2:500]]
     frame_hold = pd.DataFrame(cell_vals, columns=['Player', 'Team', 'Salary', 'OwnAvg', 'PointsAvg', 'Points per $', 'blank', 'drop', 'drop2', 'drop3', 'GPP Rank'])
     frame_hold['OwnAvg'] = frame_hold['OwnAvg'].str.replace('%', '').astype(float)/100
+    frame_hold['Floor'] = frame_hold['PointsAvg'] * .15
+    frame_hold['Ceiling'] = frame_hold['PointsAvg'] * 1.85
+    flex_frame = frame_hold[['Player', 'Team', 'Salary', 'OwnAvg', 'Floor', 'PointsAvg', 'Ceiling', 'Points per $', 'GPP Rank']]
     string_cols = ['Team']
     flex_frame = flex_frame.drop_duplicates(subset='Player')
     flex_frame = flex_frame.set_index('Player')