Add multiple columns to a data frame using Dataframe.insert() method
Using DataFrame.insert() method, we can add new columns at specific position of the column name sequence. Although insert takes single column name, value as input, but we can use it repeatedly to add multiple columns to the DataFrame.
Python3
# importing pandas library import pandas as pd # creating and initializing a nested list students = [[ 'jackma' , 34 , 'Sydeny' , 'Australia' ], [ 'Ritika' , 30 , 'Delhi' , 'India' ], [ 'Vansh' , 31 , 'Delhi' , 'India' ], [ 'Nany' , 32 , 'Tokyo' , 'Japan' ], [ 'May' , 16 , 'New York' , 'US' ], [ 'Michael' , 17 , 'las vegas' , 'US' ]] # Create a DataFrame object df = pd.DataFrame(students, columns = [ 'Name' , 'Age' , 'City' , 'Country' ], index = [ 'a' , 'b' , 'c' , 'd' , 'e' , 'f' ]) # creating columns 'Age' and 'ID' at # 2nd and 3rd position using # dataframe.insert() function df.insert( 2 , "Marks" , [ 90 , 70 , 45 , 33 , 88 , 77 ], True ) df.insert( 3 , "ID" , [ 101 , 201 , 401 , 303 , 202 , 111 ], True ) # Displaying the Data frame df |
Output :
Add multiple columns to dataframe in Pandas
In Pandas, we have the freedom to add columns in the data frame whenever needed. There are multiple ways to add columns to pandas dataframe.