How to Efficiently Add a Sequential Counter Column to Grouped Pandas DataFrames Without Using a Callback Function?

Adding a Sequential Counter Column to Grouped DataFrames Without a Callback

When trying to add a sequential counter column to groups within a DataFrame, a callback function may not be the most efficient approach. Consider the following DataFrame:

df = pd.DataFrame(
    columns="index c1 c2 v1".split(),
    data=[
            [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, ],]).set_index("index", drop=True)

The goal is to create a new column "seq" that contains sequential numbers for each group, resulting in the following output:

   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

Avoidance of Callback Function:

Instead of using a callback function, we can leverage the cumcount() method to achieve the same result more efficiently. cumcount() counts the number of occurrences of each unique value in a group and returns a pandas Series with the cumulative count.

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

This approach directly modifies the DataFrame and avoids the overhead of a callback function.

Customizing Starting Number:

If you want the sequencing to start at 1 instead of 0, you can add 1 to the result:

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

By utilizing the cumcount() method, we simplify the process of adding a sequential counter column to grouped dataframes, improving both readability and performance.

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