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Say that I have this data frame:

 1 2 3 4 100 8 12 5 14 99 1 6 4 3 98 2 5 4 11 97 5 3 7 2

In this above data frame, the values indicate counts of how many observations take on (100, 1), (99, 1), etc.

In my context, the diagonals have the same meanings:

 1 2 3 4 100 A B C D 99 B C D E 98 C D E F 97 D E F G

How would I sum across the diagonals (i.e., sum the counts of the like letters) in the first data frame?

This would produce:

group sum A 8 B 13 C 13 D 28 E 10 F 18 G 2 For example, D is 5+5+4+14
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2 Answers 2

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2

If there are same indices in both DataFrames and columns use DataFrame.stack with aggregate sum:

df = df1.stack().groupby(df2.stack()).sum().rename_axis('group').reset_index(name='sum') print (df) group sum 0 A 8 1 B 13 2 C 13 3 D 28 4 E 10 5 F 18 6 G 2

EDIT: Because second DataFrame not exist use numpy.diag with flipping by numpy.flipud and sum:

#inspired by https://stackoverflow.com/a/59173028/2901002 c = df.to_numpy() v = [np.sum(np.diag(np.flipud(c), k=i)) for i in range(-len(df) + 1, len(df), 1)] print(v) [8, 13, 13, 28, 10, 18, 2] df = pd.DataFrame({'sum': v}) print (df) sum 0 8 1 13 2 13 3 28 4 10 5 18 6 2
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3 Comments

there is only one dataframe
@user3102103 - In sample data are first and second DataFrame. Can you explain more?
second one is an example of how i want sum ``` 1 2 3 4 100 8 12 5 14 99 1 6 4 3 98 2 5 4 11 97 5 3 7 2 ``` like 8,1+12,2+6+5,5+5+4+14 etc
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I found answer :

digonalsum = pd.concat([df.iloc[:, i].shift(i) for i in range(df.shape[1])], axis=1).sum(axis=1)
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