You can use the following methods to get a specific group after using the groupby() function on a pandas DataFrame:

Method 1: Get Group After Using groupby()

grouped_df.get_group('A')

Method 2: Get Specific Columns of Group After Using groupby()

grouped_df[['column1', 'column3']].get_group('A')

The following examples show how to use each method in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'store': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'sales': [12, 15, 24, 24, 14, 19, 12, 38],
                   'refunds': [4, 8, 7, 7, 10, 5, 4, 11]})

#view DataFrame
print(df)

  store  sales  refunds
0     A     12        4
1     A     15        8
2     A     24        7
3     A     24        7
4     B     14       10
5     B     19        5
6     B     12        4
7     B     38       11

Example 1: Get Group After Using groupby() 

The following code shows how to use the groupby() function to group the rows by store name, then use the get_group() function to retrieve all rows that belong to the group with the group name ‘A’:

#group rows of DataFrame based on value in 'store' column
grouped_stores = df.groupby(['store'])

#get all rows that belong to group name 'A'
grouped_stores.get_group('A')

    store    sales  refunds
0	A	12	  4
1	A	15	  8
2	A	24	  7
3	A	24	  7

Notice that get_group() returns all rows that belong to the group with the group name ‘A’.

Example 2: Get Specific Columns of Group After Using groupby() 

The following code shows how to use the groupby() function to group the rows by store name, then use the get_group() function to retrieve all rows that belong to the group with the group name ‘A’ only for the ‘sales’ and ‘refunds’ columns:

#group rows of DataFrame based on value in 'store' column
grouped_stores = df.groupby(['store'])

#get all rows that belong to group name 'A' for sales and refunds columns
grouped_stores[['store', 'refunds']].get_group('A')

    store  refunds
0	A	 4
1	A	 8
2	A	 7
3	A	 7

Notice that get_group() returns all rows that belong to the group with the group name ‘A’ for the ‘sales’ and ‘refunds’ columns only.

The following tutorials explain how to perform other common operations in pandas: