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 the Pandas data frame. 

Method 1: Add multiple columns to a data frame using Lists

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# 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 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 df
df['Uni_Marks'] = marks
df['Gender'] = gender
  
# Displaying the Data frame
df

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Output :



Method 2: Add multiple columns to a data frame using  Dataframe.assign() method

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# 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 'Admissionnum' and 'Percentage'
# using dataframe.assign() function
df = df.assign(Admissionnum=[250, 800, 1200, 300, 400, 700], 
               Percentage=['85%', '90%', '75%', '35%', '60%', '80%'])
  
# Displaying the Data frame
df

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Output :

Method 3: Add multiple columns to a data frame using  Dataframe.insert() method

Python3

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# 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

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Output :

Method 4: Add multiple columns to a data frame using  Dictionary and zip()

Python3

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# 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 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 frame
df

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Output :


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