How to use DataFrame.groupby() to Iterate over Data frame Groups In Python
DataFrame.groupby() function in Python is used to split the data into groups based on some criteria.
Python3
# 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.
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:
Python3
# 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: