Reputation: 141
EDIT: To put in sample data df and expected output. EDIT 2: I've modified the data slightly so that the results are not uniformly largest number associated with 'cc' in each case.
My problem is:
The df is:
df = pd.DataFrame({'Index1': ['A', 'A', 'A', 'B', 'B', 'B'],
'Index2': ['aa', 'bb', 'cc', 'aa', 'bb', 'cc'],
'X': [1, 2, 7, 3, 6, 1],
'Y': [2, 3, 6, 2, 4, 1],
'Z': [3, 5, 9, 1, 2, 1]})
Then the code is:
df_scored = pd.DataFrame() #new df to hold results
cats = [X, Y, Z] #categories (columns of df) to be scaled
grouped = df.groupby([Index 1, Index 2]).sum()
for cat in cats :
df_scored[cat] = grouped.groupby(level = 0)[cat].apply(lambda x: x / x.max())
df_scored['Score'] = df_scored.sum(axis = 1)
This produces:
X Y Z Score
Index1 Index2
A aa 0.142857 0.333333 0.333333 0.809524
bb 0.285714 0.500000 0.555556 1.341270
cc 1.000000 1.000000 1.000000 3.000000
B aa 0.500000 0.500000 0.500000 1.500000
bb 1.000000 1.000000 1.000000 3.000000
cc 0.166667 0.250000 0.500000 0.916667
Now I want to sort the resulting df_scored by each grouping of Index 1 (so that Index 2 is sorted by 'Score' within each group of Index 1), with this as the desired result:
X Y Z Score
Index1 Index2
A cc 1.000000 1.000000 1.000000 3.000000
bb 0.285714 0.500000 0.555556 1.341270
aa 0.142857 0.333333 0.333333 0.809524
B bb 1.000000 1.000000 1.000000 3.000000
aa 0.500000 0.500000 0.500000 1.500000
cc 0.166667 0.250000 0.500000 0.916667
How do I do this?
I've seen a few other questions on this here and here but not getting it to work for me in this case.
Upvotes: 4
Views: 4266
Reputation: 13401
Add this at the end of your code
df_scored.sort_values('Score', ascending= False).sort_index(level='Index1', sort_remaining=False)
Upvotes: 6