How to convert index in a column of the Pandas dataframe?

Each row in a dataframe (i.e level=0) has an index value i.e value from 0 to n-1 index location and there are many ways to convert these index values into a column in a pandas dataframe. First, let’s create a Pandas dataframe. Here, we will create a Pandas dataframe regarding student’s marks in a particular subject with columns roll number, name, score, grade, and subject.

Example:

Python3

# importing the pandas library as pd  
import pandas as pd      
  
  
# Creating the dataframe Ab  
AB = pd.DataFrame({'Roll Number': ['9917102206', '9917102250',  
                                   '9917102203', '9917102204', 
                                   '9917102231'],  
                   'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA',  
                            'PRAKHAR','ASHISH'],  
                   'Score': [99, 98, 50, 45,97],  
                   'Grade': ['A+', 'A+', 'C+', 'C','A'],  
                   'Subject': ['Operating Systems', 'Operating Systems', 
                               'Operating Systems', 'Operating Systems', 
                               'Operating Systems']})  
    
# Printing the dataframe  
AB

Output:

DATA FRAME CREATED

Method 1: Creating a new Index column

Here we will learn to create a new column in the existing dataframe as an index and add index value of each row(level=0) to that column.

Python3

# importing the pandas library as pd  
import pandas as pd      
  
  
# Creating the dataframe Ab  
AB = pd.DataFrame({'Roll Number': ['9917102206', '9917102250',  
                                   '9917102203', '9917102204', 
                                   '9917102231'],  
                   'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA', 
                            'PRAKHAR','ASHISH'],  
                   'Score': [99, 98, 50, 45,97],  
                   'Grade': ['A+', 'A+', 'C+', 'C','A'],  
                   'Subject': ['Operating Systems', 'Operating Systems',  
                               'Operating Systems', 'Operating Systems', 
                               'Operating Systems']})  
    
      
# Adding a new index column to existing 
# data frame and passing index value    
AB['index'] = AB.index 
  
# Printing the dataframe  
AB

Output:

Here, we added a new column “index” to “AB” dataframe using the index value of each row in the dataframe as an argument value and converted index to the column.

Method 2: Using reset_index() method and to_string() method

Here we will sue reset_index() method to convert the index to a column along with inplace argument to reflect the change continuously and we will use the to_string() method for hiding the index value shown by default whenever a dataframe is printed.

Python3

#CREATING A DATAFRAME FOR STUDENTS PORTFOLIO 
  
# importing the pandas library as pd  
import pandas as pd      
# Creating the dataframe Ab  
AB = pd.DataFrame({'Roll Number': ['9917102206', '9917102250', '9917102203', '9917102204','9917102231'],  
                   'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA', 'PRAKHAR','ASHISH'],  
                   'Score': [99, 98, 50, 45,97],  
                   'Grade': ['A+', 'A+', 'C+', 'C','A'],  
                   'Subject': ['Operating Systems', 'Operating Systems', 'Operating Systems', 'Operating Systems','Operating Systems']})  
    
      
# importing the pandas library as pd  
import pandas as pd      
  
  
# Creating the dataframe Ab  
AB = pd.DataFrame({'Roll Number': ['9917102206', '9917102250',  
                                   '9917102203', '9917102204', 
                                   '9917102231'],  
                   'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA', 
                            'PRAKHAR','ASHISH'],  
                   'Score': [99, 98, 50, 45,97],  
                   'Grade': ['A+', 'A+', 'C+', 'C','A'],  
                   'Subject': ['Operating Systems', 'Operating Systems',  
                               'Operating Systems', 'Operating Systems', 
                               'Operating Systems']})  
    
      
# USING RESET_INDEX METHOD 
# Adding a new index column to AB dataframe 
AB.reset_index(level=0, inplace=True)  
  
# HIDING THE DEFAULT INDEX VALUES AND 
# PRINTING DATAFRAME 
print( AB.to_string(index=False))

Output:

USING RESET_INDEX METHOD

Here we applied reset_index method to given data frame and make the default index value (equal to 0) by passing it as an arguement to to_string method.

Method 3: Using multi_index

Here We will learn to create columns from a dataframe having multi-index.

Example 1: For multi-index to column

Python3

# importing the pandas library as pd  
import pandas as pd      
  
# ADDING MULTI INDEX TO DATA FRAME 
new_index = pd.MultiIndex.from_tuples([('E4','ECE'), 
                                       ('E5','ECE'), 
                                       ('E6','ECE'), 
                                       ('E7','ECE'), 
                                       ('E8','ECE')], 
                                       names=['BATCH','BRANCH']) 
# Creating the dataframe AB 
data =({'Roll Number': ['9917102206', '9917102250',  
                        '9917102203', '9917102204', 
                        '9917102231'],  
                   'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA', 
                            'PRAKHAR','ASHISH'],  
                   'Score': [99, 98, 50, 45,97],  
                   'Grade': ['A+', 'A+', 'C+', 'C','A'],  
                   'Subject': ['Operating Systems', 'Operating Systems', 
                               'Operating Systems', 'Operating Systems', 
                               'Operating Systems']})  
    
# COMBING DATA FRAME AND MULTI INDEX  
# VALUES AND FORMING DATA FRAME 
AB = pd.DataFrame(data, columns = ['Roll Number','Name','Score','Grade','Subject'],  
                  index=new_index) 
  
# MAKING MULTI INDEX NOW A PART OF COLUMN  
# OF DATAFRAME 
AB.reset_index(inplace=True)  
  
AB

Output:

Example 2: For making specific column from multi-index as a column of dataframe

Python3

# importing the pandas library as pd  
import pandas as pd      
  
# ADDING MULTI INDEX TO DATA FRAME 
new_index = pd.MultiIndex.from_tuples([('E4','ECE'), 
                                       ('E5','ECE'), 
                                       ('E6','ECE'), 
                                       ('E7','ECE'), 
                                       ('E8','ECE')], 
                                       names=['BATCH','BRANCH']) 
# Creating the dataframe AB 
data =({'Roll Number': ['9917102206', '9917102250',  
                        '9917102203', '9917102204', 
                        '9917102231'],  
                   'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA', 
                            'PRAKHAR','ASHISH'],  
                   'Score': [99, 98, 50, 45,97],  
                   'Grade': ['A+', 'A+', 'C+', 'C','A'],  
                   'Subject': ['Operating Systems', 'Operating Systems', 
                               'Operating Systems', 'Operating Systems', 
                               'Operating Systems']})  
    
# COMBING DATA FRAME AND MULTI INDEX  
# VALUES AND FORMING DATA FRAME 
AB = pd.DataFrame(data, columns = ['Roll Number','Name','Score','Grade','Subject'], 
                  index=new_index) 
  
# MAKING SPECIFIC COLUMN OF MULTI INDEX  
# NOW A PART OF COLUMN OF DATAFRAME 
AB.reset_index(inplace=True,level=['BATCH'])  
  
# BATCH INDEX IS NOW A COLUMN OF DATAFRAME 
AB

Output:

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