Adding a 'count' column to the result of a groupby in pandas?

Question

I think this is a fairly basic question, but I can’t seem to find the solution.

I have a pandas dataframe similar to the following:

import pandas as pd

df = pd.DataFrame({'A' : ['x','x','y','z','z'],
                   'B' : ['p','p','q','r','r']})
df

which creates a table like this:

    A   B
0   x   p
1   x   p
2   y   q
3   z   r
4   z   r

I’m trying to create a table that represents the number of distinct values in that dataframe. So my goal is something like this:

    A   B   c
0   x   p   2
1   y   q   1
2   z   r   2

I can’t find the correct functions to achieve this, though. I’ve tried:

df.groupby(['A','B']).agg('count')

This produces a table with 3 rows (as expected) but without a ‘count’ column. I don’t know how to add in that count column. Could someone point me in the right direction?

Asked By: Oliver

||

Source

Answer 1

You can using size

df.groupby(['A','B']).size()
Out[590]: 
A  B
x  p    2
y  q    1
z  r    2
dtype: int64

For your solution adding one of the columns

df.groupby(['A','B']).B.agg('count')
Out[591]: 
A  B
x  p    2
y  q    1
z  r    2
Name: B, dtype: int64

Update :

df.groupby(['A','B']).B.agg('count').to_frame('c').reset_index()

#df.groupby(['A','B']).size().to_frame('c').reset_index()
Out[593]: 
   A  B  c
0  x  p  2
1  y  q  1
2  z  r  2

Answered By: BENY

Answer 2

pandas >= 1.1: `DataFrame.value_counts`

This is an identical replacement for df.groupby(['A', 'B']).size().

df.value_counts(['A', 'B'])

A  B
z  r    2
x  p    2
y  q    1
dtype: int64

df.value_counts(['A', 'B']).reset_index(name='c')

   A  B  c
0  z  r  2
1  x  p  2
2  y  q  1

Answered By: cs95

Adding a 'count' column to the result of a groupby in pandas?

Question:

Answers:

pandas >= 1.1: `DataFrame.value_counts`

Adding a 'count' column to the result of a groupby in pandas?

Question:

Answers:

pandas >= 1.1: DataFrame.value_counts

pandas >= 1.1: `DataFrame.value_counts`