How to Efficiently Add a Sequential Counter Column to Grouped Pandas DataFrames?

Add a Sequential Counter Column on Groups to a Pandas DataFrame Without a Callback

In the pandas library, one can utilize more efficient methods than callback functions to add sequential counter columns to grouped dataframes. The cumcount() function provides an alternative approach. Here's how to implement it:

import pandas as pd

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)

# Add a sequential counter column
df["seq"] = df.groupby(["c1", "c2"]).cumcount() + 1
print(df)

This code snippet groups the dataframe by 'c1' and 'c2', then applies the cumcount() function to each group to count the rows within that group. The result is a dataframe with an additional 'seq' column containing the sequential counts for each group:

   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

This approach eliminates the need for callback functions and provides a more concise and efficient solution for adding sequential counter columns to grouped pandas dataframes.

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