Python: How do I get a new value every instance in a np.where

Question

I have a data set with a column filled with 1s and 0s as such:

column 1:
1
1
1
1
0
0
0
1
1
0
1
1

and I am currently using np.where() to create a new column that segments the data from column 1. The issue is, whenever it comes across a new segments of 1s I want the values in column 2 to increase by 1.

n = 1
df['column2'] = np.where(df['column1'] == 1 ,  n + 1  , 0)

The results I am getting:

column 1: column 2:
1            1
1            1
1            1
1            1
0            0
0            0
0            0 
1            1
1            1
0            0
1            1
1            1

what I am trying to achieve

column 1: column 2:
1            1
1            1
1            1
1            1
0            0
0            0
0            0 
1            2
1            2
0            0
1            3
1            3

Answer 1

Cheeky one-liner:

df["column2"] = df['column1'].diff().ne(0).cumsum().add(1).floordiv(2).where(df['column1'].astype(bool), other=0)

df:

    column1  column2
0         1        1
1         1        1
2         1        1
3         1        1
4         0        0
5         0        0
6         0        0
7         1        2
8         1        2
9         0        0
10        1        3
11        1        3

Answer 2

You can do it like this also:

df['column 2:'] = (df.assign(grp = (df['column 1:'].diff() != 0).cumsum())
                     .where(df['column 1:'].astype(bool))
                     .groupby('grp').ngroup().add(1).fillna(0))

Output:

    column 1:  column 2:
0           1        1.0
1           1        1.0
2           1        1.0
3           1        1.0
4           0        0.0
5           0        0.0
6           0        0.0
7           1        2.0
8           1        2.0
9           0        0.0
10          1        3.0
11          1        3.0