Using Pandas library, we can perform multiple operations on a DataFrame. We can even create and access the subset of a DataFrame in multiple formats. The task here is to create a subset DataFrame by column name. We can choose different methods to perform this task. Here are possible methods mentioned below –
Before performing any action, we need to write few lines of code to import necessary libraries and create a DataFrame.
Creating the DataFrame
Python3
#import pandas import pandas as pd # create dataframe data = {'Name': ['John', 'Emily', 'Lara', 'Lucas', 'Katy', 'Jordan'], 'Gender': [30, 27, 21, 21, 16, 20], 'Branch': ['Arts', 'Arts', 'Commerce', 'Science', 'Science', 'Science'], 'pre_1': [9, 9, 10, 7, 6, 9], 'pre_2': [8, 7, 10, 6, 8, 8]} df = pd.DataFrame(data) df |
Output:
Method 1: Using Python iloc() function
This function allows us to create a subset by choosing specific values from columns based on indexes.
Syntax:
df_name.iloc[beg_index:end_index+1,beg_index:end_index+1]
Example: Create a subset with Name, Gender and Branch column
Python3
# create a subset of all rows # and Name, Gender and Branch column df.iloc[:, 0:3] |
Output :
Method 2: Using Indexing Operator
We can use the indexing operator i.e. square brackets to create a subset dataframe
Example: Create a subset with Name, pre_1, and pre_2 column
Python3
# creating subset dataframe using # indexing operator df[['Name', 'pre_1', 'pre_2']] |
Output –
Method 3: Using filter() method with like keyword
We can use this method particularly when we have to create a subset dataframe with columns having similarly patterned names.
Example: Create a subset with pre_1 and pre_2 column
Python3
# create a subset of columns pre_1 and pre_2 # using filter() method df.filter(like='pre') |
Output:
