Pandas create new column with count from groupby

Question

I have a df that looks like the following:

id        item        color
01        truck       red
02        truck       red
03        car         black
04        truck       blue
05        car         black

I am trying to create a df that looks like this:

item      color       count
truck     red          2
truck     blue         1
car       black        2

I have tried

df["count"] = df.groupby("item")["color"].transform('count')

But it is not quite what I am searching for.

Any guidance is appreciated

Asked By: GNMO11

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Source

Answer 1

That’s not a new column, that’s a new DataFrame:

In [11]: df.groupby(["item", "color"]).count()
Out[11]:
             id
item  color
car   black   2
truck blue    1
      red     2

To get the result you want is to use reset_index:

In [12]: df.groupby(["item", "color"])["id"].count().reset_index(name="count")
Out[12]:
    item  color  count
0    car  black      2
1  truck   blue      1
2  truck    red      2

To get a “new column” you could use transform:

In [13]: df.groupby(["item", "color"])["id"].transform("count")
Out[13]:
0    2
1    2
2    2
3    1
4    2
dtype: int64

I recommend reading the split-apply-combine section of the docs.

Answered By: Andy Hayden

Answer 2

Another possible way to achieve the desired output would be to use Named Aggregation. Which will allow you to specify the name and respective aggregation function for the desired output columns.

Named aggregation

(New in version 0.25.0.)

To support column-specific aggregation with control over the output
column names, pandas accepts the special syntax in GroupBy.agg(),
known as “named aggregation”, where:

The keywords are the output column names

The values are tuples whose first element is the column to select and
the second element is the aggregation to apply to that column. Pandas
provides the pandas.NamedAgg named tuple with the fields ['column','aggfunc'] to make it clearer what the arguments are. As usual, the
aggregation can be a callable or a string alias.

So to get the desired output – you could try something like…

import pandas as pd
# Setup
df = pd.DataFrame([
    {
        "item":"truck",
        "color":"red"
    },
    {
        "item":"truck",
        "color":"red"
    },
    {
        "item":"car",
        "color":"black"
    },
    {
        "item":"truck",
        "color":"blue"
    },
    {
        "item":"car",
        "color":"black"
    }
])

df_grouped = df.groupby(["item", "color"]).agg(
    count_col=pd.NamedAgg(column="color", aggfunc="count")
)
print(df_grouped)

Which produces the following output:

             count_col
item  color
car   black          2
truck blue           1
      red            2

Answered By: Jadon Manilall

Answer 3

Here is another option:

import numpy as np
df['Counts'] = np.zeros(len(df))
grp_df = df.groupby(['item', 'color']).count()

which results in

             Counts
item  color        
car   black       2
truck blue        1
      red         2

Answered By: Adrian Keister

Answer 4

You can use value_counts and name the column with reset_index:

In [3]: df[['item', 'color']].value_counts().reset_index(names='counts')
Out[3]: 
    item  color  counts
0    car  black       2
1  truck    red       2
2  truck   blue       1

Answered By: rachwa

Answer 5

An option that is more literal then the accepted answer.

df.groupby(["item", "color"], as_index=False).agg(count=("item", "count"))

Any column name can be used in place of "item" in the aggregation.

"as_index=False" prevents the grouped column from becoming the index.

Answered By: Cannon Lock

Pandas create new column with count from groupby

Question:

Answers:

Named aggregation