How to Change Index Values in Pandas?

Index is used to uniquely identify a row in Pandas DataFrame. It is nothing but a label to a row. If we didn’t specify index values to the DataFrame while creation then it will take default values i.e. numbers starting from 0 to n-1 where n indicates a number of rows.

Let’s create a dataframe

Example:

Python3

# import necessary packages
import pandas as pd

# create a dataframe
Students = pd.DataFrame({'Admission_id': ['AB101', 'AB102', 'AB103',
                                          'AB104', 'AB105'],
                         'Student_id': ['21GFG1', '21GFG2', '21GFG3',
                                        '21GFG4', '21GFG5'],
                         'Student_Name': ['Akhil', 'Mahesh Babu', 'Warner',
                                          'Virat', 'ABD'],
                         'Height': [5.9, 6.2, 5.6, 5.8, 5.10]})
# display dataframe
Students

Output:

Method  1 : Using set_index()

To change the index values we need to use the set_index method which is available in pandas allows specifying the indexes.

Syntax

DataFrameName.set_index(“column_name_to_setas_Index”,inplace=True/False)

where,

• inplace parameter accepts True or False, which specifies that change in index is permanent or temporary.
• True indicates that change is Permanent.
• False indicates that the change is Temporary.

Example 1:

Changing the index temporarily by specifying inplace=False (or) we can make it without specifying inplace parameter because by default the inplace value is false.

Python3

# import necessary packages
import pandas as pd

# create a dataframe
Students = pd.DataFrame({'Admission_id': ['AB101', 'AB102', 'AB103',
                                          'AB104', 'AB105'],
                         'Student_id': ['21GFG1', '21GFG2', '21GFG3',
                                        '21GFG4', '21GFG5'],
                         'Student_Name': ['Akhil', 'Mahesh Babu', 'Warner',
                                          'Virat', 'ABD'],
                         'Height': [5.9, 6.2, 5.6, 5.8, 5.10]})

# setting admission id as index but temporarily
Students.set_index("Admission_id")

Output:

But when we displayed the data in DataFrame but it still remains as previous because the operation performed was not saved as it is a temporary operation.

Explanation– As we didn’t specify inplace parameter in set_index method, by default it is taken as false and considered as a temporary operation.

Example 2 :

Changing the index permanently by specifying inplace=True in set_index method.

Example:

Python3

# import necessary packages
import pandas as pd

# create a dataframe
Students = pd.DataFrame({'Admission_id': ['AB101', 'AB102', 'AB103',
                                          'AB104', 'AB105'],
                         'Student_id': ['21GFG1', '21GFG2', '21GFG3',
                                        '21GFG4', '21GFG5'],
                         'Student_Name': ['Akhil', 'Mahesh Babu', 'Warner',
                                          'Virat', 'ABD'],
                         'Height': [5.9, 6.2, 5.6, 5.8, 5.10]})

# setting student id as index but permanently
Students.set_index("Student_id", inplace=True)

# display dataframe
Students

Output:

Example 3  :

When we want to retrieve only particular columns instead of all columns follow the below code

Python3

# import necessary packages
import pandas as pd

# create a dataframe
Students = pd.DataFrame({'Admission_id': ['AB101', 'AB102', 'AB103',
                                          'AB104', 'AB105'],
                         'Student_id': ['21GFG1', '21GFG2', '21GFG3',
                                        '21GFG4', '21GFG5'],
                         'Student_Name': ['Akhil', 'Mahesh Babu', 'Warner',
                                          'Virat', 'ABD'],
                         'Height': [5.9, 6.2, 5.6, 5.8, 5.10]})

# setting student id as index but permanently
Students.set_index("Student_id", inplace=True)

# display dataframe with required columns
Students[["Student_Name", "Height"]]

Output