Number rows within group in increasing order in a pandas dataframe

Question

Given the following dataframe:

import pandas as pd
import numpy as np
df=pd.DataFrame({'A':['A','A','A','B','B','B'],
                'B':['a','a','b','a','a','a'],
                })
df

    A   B
0   A   a 
1   A   a 
2   A   b 
3   B   a 
4   B   a 
5   B   a

I’d like to create column ‘C’, which numbers the rows within each group in columns A and B like this:

    A   B   C
0   A   a   1
1   A   a   2
2   A   b   1
3   B   a   1
4   B   a   2
5   B   a   3

I’ve tried this so far:

df['C'] = df.groupby(['A','B'])['B'].transform('rank')

…but it doesn’t work!

Asked By: Dance Party2

||

Source

Answer 1

Use groupby/cumcount:

In [25]: df['C'] = df.groupby(['A','B']).cumcount()+1; df
Out[25]: 
   A  B  C
0  A  a  1
1  A  a  2
2  A  b  1
3  B  a  1
4  B  a  2
5  B  a  3

Answered By: unutbu

Answer 2

Use groupby.rank function.
Here the working example.

df = pd.DataFrame({'C1':['a', 'a', 'a', 'b', 'b'], 'C2': [1, 2, 3, 4, 5]})
df

C1 C2
a  1
a  2
a  3
b  4
b  5

df["RANK"] = df.groupby("C1")["C2"].rank(method="first", ascending=True)
df

C1 C2 RANK
a  1  1
a  2  2
a  3  3
b  4  1
b  5  2

Answered By: Gokulakrishnan

Answer 3

OP’s code was missing the appropriate method to get the correct output.

df['C'] = df.groupby(['A','B'])['B'].transform('rank', method='first')
df


    A   B     C
0   A   a   1.0
1   A   a   2.0
2   A   b   1.0
3   B   a   1.0
4   B   a   2.0
5   B   a   3.0

Answered By: cottontail

Number rows within group in increasing order in a pandas dataframe

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