Python | Pandas DataFrame.set_index()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas set_index() is a method to set a List, Series or Data frame as index of a Data Frame. Index column can be set while making a data frame too. But sometimes a data frame is made out of two or more data frames and hence later index can be changed using this method.

Syntax:  DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

Parameters:

keys: Column name or list of column name. 
drop: Boolean value which drops the column used for index if True. 
append: Appends the column to existing index column if True. 
inplace: Makes the changes in the dataframe if True. 
verify_integrity: Checks the new index column for duplicates if True. 

To download the CSV file used, Click Here.
Code #1: Changing Index column 
In this example, First Name column has been made the index column of Data Frame. 

Python3

# importing pandas package
import pandas as pd

# making data frame from csv file
data = pd.read_csv("employees.csv")

# setting first name as index column
data.set_index("First Name", inplace = True)

# display
data.head()

Output:
As shown in the output images, earlier the index column was a series of number but later it has been replaced with First name.
Before operation – 

After operation – 

Code #2: Multiple index Column 
In this example, two columns will be made as index column. Drop parameter is used to Drop the column and append parameter is used to append passed columns to the already existing index column. 

Python3

# importing pandas package
import pandas as pd

# making data frame from csv file
data = pd.read_csv("employees.csv")

# setting first name as index column
data.set_index(["First Name", "Gender"], inplace = True,
                            append = True, drop = False)

# display
data.head()

Output:
As shown in the output Image, the data is having 3 index columns. 

Code #3: Setting a single Float column as Index in Pandas DataFrame

Python3

# importing pandas library
import pandas as pd

# creating and initializing a nested list
students = [['jack', 34, 'Sydeny', 'Australia',85.96],
            ['Riti', 30, 'Delhi', 'India',95.20],
            ['Vansh', 31, 'Delhi', 'India',85.25],
            ['Nanyu', 32, 'Tokyo', 'Japan',74.21],
            ['Maychan', 16, 'New York', 'US',99.63],
            ['Mike', 17, 'las vegas', 'US',47.28]]

# Create a DataFrame object
df = pd.DataFrame(students,
                      columns=['Name', 'Age', 'City', 'Country','Agg_Marks'],
                           index=['a', 'b', 'c', 'd', 'e', 'f'])

# here we set Float column 'Agg_Marks' as index of data frame
# using dataframe.set_index() function
df = df.set_index('Agg_Marks')


# Displaying the Data frame
df

Output :

In the above example, we set the column ‘Agg_Marks‘ as an index of the data frame.

Code #4: Setting three columns as MultiIndex in Pandas DataFrame  

Python3

# importing pandas library
import pandas as pd

# creating and initializing a nested list
students = [['jack', 34, 'Sydeny', 'Australia',85.96,400],
            ['Riti', 30, 'Delhi', 'India',95.20,750],
            ['Vansh', 31, 'Delhi', 'India',85.25,101],
            ['Nanyu', 32, 'Tokyo', 'Japan',74.21,900],
            ['Maychan', 16, 'New York', 'US',99.63,420],
            ['Mike', 17, 'las vegas', 'US',47.28,555]]

# Create a DataFrame object
df = pd.DataFrame(students,
                      columns=['Name', 'Age', 'City', 'Country','Agg_Marks','ID'],
                           index=['a', 'b', 'c', 'd', 'e', 'f'])

# Here we pass list of 3 columns i.e 'Name', 'City' and 'ID'
# to dataframe.set_index() function
# to set them as multiIndex of dataframe
df = df.set_index(['Name','City','ID'])


# Displaying the Data frame
df

Output :

In the above example, we set the columns ‘Name‘, ‘City‘, and ‘ID‘ as multiIndex of the data frame.