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.
Add multiple columns to a DataFrame using Lists
Python3
# importing pandas libraryimport pandas as pd# creating and initializing a nested liststudents = [['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 objectdf = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Country'], index=['a', 'b', 'c', 'd', 'e', 'f'])# Creating 2 lists 'marks' and 'gender'marks = [85.4,94.9,55.2,100.0,40.5,33.5]gender = ['M','F','M','F','F','M']# adding lists as new column to dataframe dfdf['Uni_Marks'] = marksdf['Gender'] = gender# Displaying the Data framedf |
Output :
Add DataFrame columns using Lists
Add multiple columns to a data frame using Dataframe.assign() method
Using DataFrame.assign() method, we can set column names as parameters and pass values as list to replace/create the columns.
Python3
# importing pandas libraryimport pandas as pd# creating and initializing a nested liststudents = [['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 objectdf = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Country'], index=['a', 'b', 'c', 'd', 'e', 'f'])# creating columns 'Admissionnum' and 'Percentage'# using dataframe.assign() functiondf = df.assign(Admissionnum=[250, 800, 1200, 300, 400, 700], Percentage=['85%', '90%', '75%', '35%', '60%', '80%'])# Displaying the Data framedf |
Output :
Added multiple columns using DataFrame assign() Method
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 libraryimport pandas as pd# creating and initializing a nested liststudents = [['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 objectdf = 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() functiondf.insert(2, "Marks", [90, 70, 45, 33, 88, 77], True)df.insert(3, "ID", [101, 201, 401, 303, 202, 111], True)# Displaying the Data framedf |
Output :
Added multiple columns using DataFrame insert() Method
Add multiple columns to a data frame using Dictionary and zip()
Using Dict and zip() we can create a mapping of key values, which can be assigned to a new column name.
Python3
# importing pandas libraryimport pandas as pd# creating and initializing a nested liststudents = [['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 objectdf = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Country'], index=['a', 'b', 'c', 'd', 'e', 'f'])# creating 2 lists 'ids' and 'marks'ids = [11, 12, 13, 14, 15, 16]marks=[85,41,77,57,20,95,96]# Creating columns 'ID' and 'Uni_marks' # using Dictionary and zip()df['ID'] = dict(zip(ids, df['Name']))df['Uni_Marks'] = dict(zip(marks, df['Name'])) # Displaying the Data framedf |
Output :
Added multiple columns using Dictionary and zip()
