How to use pandas.Series.str.contains to return true value for row after row that contains the given-CodePudding

I am using the following code to make a mask of a data frame. The mask means that I return TRUE for all values in a row where one cell in that row has a certain condition, for instance where one cell value is exactly 21.

 mask_pipe21 = np.column_stack([output[col].str.contains("^21$", regex=True, na=False) for col in output])

What I want to do now, is to return TRUE not for the row that contains 21, but for the row after the row that contains 21.

To use a simpler example from the pandas instruction:

s1 = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.NaN])
s1.str.contains('og', regex=False)
0    False
1     True
2    False
3    False
4      NaN

Instead of this results, with the same logic, I would like to return:

0    False
1    False
2    True
3    False
4      NaN

Does anybody know how I could achieve this with my code line? Thanks in advance.

CodePudding user response：

Try

s1 = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.NaN])
s1.str.contains('og').shift(1)
>>>
0      NaN
1    False
2     True
3    False
4    False

This is not 100% your wanted output. Therefor you maybe want to change the NaN values afterwards.

CodePudding user response：

A complete solution would be to fill the shift with False, and to mask the original NaN.

(s1.str.contains('og', regex=False)
   .shift(fill_value=False)
   .mask(s1.isna())
)

Output:

0    False
1    False
2     True
3    False
4      NaN
dtype: object