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 reset_index()
is a method to reset index of a Data Frame. reset_index() method sets a list of integer ranging from 0 to length of data as index.
Syntax:
DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill=”)Parameters:
level: int, string or a list to select and remove passed column from index.
drop: Boolean value, Adds the replaced index column to the data if False.
inplace: Boolean value, make changes in the original data frame itself if True.
col_level: Select in which column level to insert the labels.
col_fill: Object, to determine how the other levels are named.Return type: DataFrame
To download the CSV file used, Click Here.
Example #1: Resetting index
In this example, to reset index, First name column have been set as index column first and then using reset index a new index have been generated.
# 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 , append = True , drop = True ) # resetting index data.reset_index(inplace = True ) # display data.head() |
Output:
As show in the output images, A new index label named level_0 has been generated.
Before reset –
After reset –
Example #2: Operation on Multi level Index
In this example, 2 columns(First name and Gender) are added to the index column and later one level is removed by using reset_index() method.
# 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 = True ) # resetting index data.reset_index(level = 2 , inplace = True , col_level = 1 ) # display data.head() |
Output:
As shown in the output image, The gender column in the index column was replaced as it’s level was 2.
Before reset –
After reset –
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