How to List values for each Pandas group?

Last Updated : 20 Aug, 2020

In this article, we’ll see how we can display all the values of each group in which a dataframe is divided. The dataframe is first divided into groups using the DataFrame.groupby() method. Then we modify it such that each group contains the values in a list.

First, Let’s create a Dataframe:

Python3

# import pandas library 
import pandas as pd 
  
# create a dataframe 
df = pd.DataFrame({'a': ['A', 'A', 'B', 
                          'B', 'B', 'C', 
                          'C', 'D'],  
                    'b': [1, 2, 5, 
                          3, 5, 4, 
                          8, 6]}) 
# show the dataframe                   
df

Output:

Dataframe

Method 1: Using DataFrame.groupby() and Series.apply() together.
Example: We’ll create lists of all values of each group and store it in new column called “listvalues”.

Python3

# import pandas library 
import pandas as pd 
  
# create a dataframe 
df = pd.DataFrame({'a': ['A', 'A', 'B', 
                          'B', 'B', 'C', 
                          'C', 'D'],  
                    'b': [1, 2, 5, 
                          3, 5, 4, 
                          8, 6]}) 
                   
# convert values of each group 
# into a list 
groups = df.groupby('a')['b'].apply(list) 
  
print(groups) 
  
# groups store in a new  
# column called listvalues 
df1 = groups.reset_index(name  
                         = 'listvalues') 
# show the dataframe 
df1

Output:

Group into list

Method 2: Using DataFrame.groupby() and Series.agg().

Example: We use the lambda function inside the Series.agg() to convert the all values of a group to a list.

Python3

# import pandas library 
import pandas as pd 
  
# create a dataframe 
df = pd.DataFrame( {'a': ['A', 'A', 'B', 
                         'B', 'B', 'C',  
                         'C', 'D'],  
                    'b': [1, 2, 5,  
                         3, 5, 4, 
                         8, 6]} 
                 ) 
# convert values of each group 
# into a list 
groups = df.groupby('a').agg(lambda
                             x: list(x)) 
  
print(groups) 