How to Efficiently Add a Sequential Counter Column to Grouped Data in Pandas?

Add Sequential Counter Column to Groups in Pandas DataFrame

In the context of data wrangling, there is a common task of adding a sequential counter column to groups within a pandas dataframe. One approach is to use a callback function as you have demonstrated:

def callback(x):
    x['seq'] = range(1, x.shape[0] + 1)
    return x

While this method works, it can be cumbersome and involves defining a separate function. A more concise and elegant solution is to utilize the cumcount() method:

df.groupby(['c1', 'c2']).cumcount()

This method computes the cumulative count for each group, effectively assigning a sequential number to each row within a group. For example, consider the following dataframe:

index	c1	c2	v1
0	A	X	3
1	A	X	5
2	A	Y	7
3	A	Y	1
4	B	X	3
5	B	X	1
6	B	X	3
7	B	Y	1
8	C	X	7
9	C	Y	4
10	C	Y	1
11	C	Y	6

Applying cumcount() to this dataframe, grouped by c1 and c2, would produce:

index	c1	c2	v1	seq
0	A	X	3	1
1	A	X	5	2
2	A	Y	7	1
3	A	Y	1	2
4	B	X	3	1
5	B	X	1	2
6	B	X	3	3
7	B	Y	1	1
8	C	X	7	1
9	C	Y	4	1
10	C	Y	1	2
11	C	Y	6	3

To start the ordering at 1 instead of 0, simply add 1 to the cumcount() result:

df.groupby(['c1', 'c2']).cumcount() + 1

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