How to count the number of days since a column flag without zeros?-CodePudding

I have a dataframe defined as follows. I'd like to count the number of days (or rows) when the input column changes from 1 to 0 but without zeros:

import pandas as pd
df = pd.DataFrame({'input': [1,1,1,0,0,0,1,1,1,0,0,0]}, 
                  index=pd.date_range('2021-10-01', periods=12))
# I can mark the points of interest, i.e. when it goes from 1 to 0
df['change'] = 0
df.loc[(df['input'].shift(1) - df['input']) > 0, 'change'] = 1
print(df)

I end up with the following:

            input   change
2021-10-01      1        0
2021-10-02      1        0 
2021-10-03      1        0
2021-10-04      0        1
2021-10-05      0        0
2021-10-06      0        0
2021-10-07      1        0
2021-10-08      1        0
2021-10-09      1        0
2021-10-10      0        1
2021-10-11      0        0
2021-10-12      0        0

What I want is a res output, so every time I get a one restart a count:

            input   change     res
2021-10-01      1        0       0
2021-10-02      1        0       0  
2021-10-03      1        0       0
2021-10-04      0        1       1
2021-10-05      0        0       2
2021-10-06      0        0       3
2021-10-07      1        0       4
2021-10-08      1        0       5
2021-10-09      1        0       6
2021-10-10      0        1       1
2021-10-11      0        0       2
2021-10-12      0        0       3

Note that it is very similar to question How to count the number of days since a column flag? but without having zeros in between cases.

CodePudding user response：

You can use groupby to generate groups restarting at each 1, then cumcount:

s = df['change'].cumsum()
df['res'] = s.groupby(s).cumcount().add(1).mask(s.eq(0), 0)

output:

            input  change  res
2021-10-01      1       0    0
2021-10-02      1       0    0
2021-10-03      1       0    0
2021-10-04      0       1    1
2021-10-05      0       0    2
2021-10-06      0       0    3
2021-10-07      1       0    4
2021-10-08      1       0    5
2021-10-09      1       0    6
2021-10-10      0       1    1
2021-10-11      0       0    2
2021-10-12      0       0    3