Add a column with sequence values if conditions of value in another column with binary values is satisfied

I have a dataframe df with column A with random numbers and column B with categories. Now, I obtain another column C using the code below:

df.loc[df['A'] >= 50, 'C'] = 1 
df.loc[df['A'] < 50, 'C'] = 0 

I want to obtain a column ‘D’ which creates a sequence if 1 is encountered else returns the value 0. The required dataframe is given below.

Required df

A   B   C   D
17  a   0   0
88  a   1   1
99  a   1   2
76  a   1   3
73  a   1   4
23  b   0   0
36  b   0   0
47  b   0   0
74  b   1   1
80  c   1   1
77  c   1   2
97  d   1   1
30  d   0   0
80  d   1   2

>Solution :

Use GroupBy.cumcount with Series.mask:

df['D'] = df.groupby(['B', 'C']).cumcount().add(1).mask(df['C'].eq(0), 0)
print (df)
A   B   C   D
17  a   0   0
88  a   1   1
99  a   1   2
76  a   1   3
73  a   1   4
23  b   0   0
36  b   0   0
47  b   0   0
74  b   1   1
80  c   1   1
77  c   1   2
97  d   1   1
30  d   0   0
80  d   1   2

Or numpy.where:

df['D'] = np.where(df['C'].eq(0), 0, df.groupby(['B', 'C']).cumcount().add(1))