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 libraryimport pandas as pd# dictionarydict = {'X': ['A', 'B', 'A', 'B'], 'Y': [1, 4, 3, 2]}# create a dataframedf = pd.DataFrame(dict)# show the dataframedf |
Output:
Iterate over Data frame Groups in Python-Pandas
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.
Python3
# import pandas libraryimport pandas as pd# dictionarydict = {'X': ['A', 'B', 'A', 'B'], 'Y': [1, 4, 3, 2]}# create a dataframedf = pd.DataFrame(dict)# group by 'X' columngroups = df.groupby("X")for name, group in groups: print(name) print(group) print("\n") |
Output:
Iterate over Data frame Groups in Python-Pandas
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.
Python3
# import pandas libraryimport pandas as pd# dictionarydict = {'X': ['A', 'B', 'A', 'B'], 'Y': [1, 4, 3, 2]}# create a dataframedf = pd.DataFrame(dict)# group by "X" columngroups = df.groupby('X')# extract keys from groupskeys = groups.groups.keys()for i in keys: print(groups.get_group(i)) print('\n') |
Output:
Iterate over Data frame Groups in Python-Pandas
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.
