get the first row of mask that meets conditions and create a new column

Question

This is my pandas dataframe:

df = pd.DataFrame({'a': [20, 21, 333, 444], 'b': [20, 20, 20, 20]})

I want to create column c by using this mask:

mask = (df.a >= df.b)

and I want to get the second row that meets this condition and create column c.
The output that I want looks like this:

     a   b    c
0   20  20  NaN
1   21  20    x
2  333  20  NaN
3  444  20  NaN

And this is my try but it put ‘x’ on all rows after the first row:

df.loc[mask.cumsum().ne(1) & mask, 'c'] = 'x'

Asked By: Amir

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Answer 1

Your condition is to mark all matching rows but the first one (mask.cumsum().ne(1)).

If you want to mark only the second use:

mask = (df.a >= df.b)
df.loc[mask.cumsum().eq(2) & mask, 'c'] = 'x'

Output:


     a   b    c
0   20  20  NaN
1   21  20    x
2  333  20  NaN
3  444  20  NaN

Answered By: mozway

Question: