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Python - Can pandas groupby aggregate into a list, rather than sum, mean, etc?

Given a Pandas DataFrame, learn that can we groupby aggregate into a list rather than sum. By Pranit Sharma Last updated : September 26, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

Problem statement

Given a Pandas DataFrame, we have to groupby aggregate into a list rather than sum.

Here, we are going to learn that can we groupby aggregate into a list rather than a sum. We will try to understand this by applying the aggregate function inside groupby method.

Can pandas groupby aggregate into a list, rather than sum, mean, etc

Yes! It is possible. We will first create a DataFrame then we will use groupby inside which we will use the lambda function to convert this result into a list.

Let's learn about the groupby() method first.

pandas.DataFrame.groupby() Method

The pandas.DataFrame.groupby() is a simple but very useful concept in pandas. By using groupby, we can create a grouping of certain values and perform some operations on those values. This method splits the object, apply some operations, and then combines them to create a group hence a large amount of data and computations can be performed on these groups.

Let us understand with the help of an example,

Python program to demonstrate can pandas groupby aggregate into a list, rather than sum, mean

# Importing pandas package
import pandas as pd

# Creating a dictionary
d = {
    'A':[10,20,30,10,20,20],
    'B':['a','b','c','a','c','c'],
    'C':[40,50,50,50,60,40],
    'D':['d','e','f','e','e','e'],
    'E':[70,80,90,90,90,70]
}

# Creating a dataframe
df = pd.DataFrame(d)

# Display Dataframe
print("DataFrame:\n",df,"\n")

# Using groupby
df2 = df.groupby('A').aggregate(lambda x: x.unique().tolist())

# Display result
print("Result:\n",df2)

Output

The output of the above program is:

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