How to Iterate over Dataframe Groups in Python-Pandas?

In this article, we'll see how we can iterate over the groups in which a data frame is divided. So, let's see different ways to do this task.

First, Let's create a data frame:

# import pandas library
import pandas as pd

# dictionary
dict = {'X': ['A', 'B', 'A', 'B'],
        'Y': [1, 4, 3, 2]}

# create a dataframe
df = pd.DataFrame(dict)

# show the dataframe
df

Output:

Using DataFrame.groupby()  to Iterate over Data frame Groups

DataFrame.groupby() function in Python is used to split the data into groups based on some criteria. 

# import pandas library
import pandas as pd

# dictionary
dict = {'X': ['A', 'B', 'A', 'B'],
        'Y': [1, 4, 3, 2]}

# create a dataframe
df = pd.DataFrame(dict)

# group by 'X' column
groups = df.groupby("X")

for name, group in groups:
    print(name)
    print(group)
    print("\n")

Output:

In above example, we have grouped on the basis of column “X”. As there are two different values under column “X”, so our data frame will be divided into 2 groups. Then our for loop will run 2 times as the number groups are 2. “name” represents the group name and “group” represents the actual grouped data frame.

Using Dataframe.groupby() and Groupby_object.groups.keys() together

Groupby_object.groups.keys() method will return the keys of the groups.

# import pandas library
import pandas as pd

# dictionary
dict = {'X': ['A', 'B', 'A', 'B'],
        'Y': [1, 4, 3, 2]}

# create a dataframe
df = pd.DataFrame(dict)

# group by "X" column
groups = df.groupby('X')

# extract keys from groups
keys = groups.groups.keys()

for i in keys:
    print(groups.get_group(i))
    print('\n')

Output:

In above example, we'll use the function groups.get_group() to get all the groups. First we'll get all the keys of the group and then iterate through that and then calling get_group() method for each key. get_group()  method will return group corresponding to the key.