In this post, we are going to learn How to Drop index column of Pandas DataFrame Python. Sometimes we may need to drop the index column of the pandas dataframe in Python. The Pandas data frames index can’t be dropped instead can be reset by using pandas.dataframe built-in reset() function.
Pandas reset_index() method
The Pandas dataframe reset_index() of dataframe single or multiple indexes if dataframe is multi-index.
Syntax
df.reset_index(drop=True, inplace=True )
Parameters
- drop: It reset the index of the dataframe to default index.
- inplace :It modified the original dataframe in place does not create object of dataframe
1. reset_index() to Drop index column of Pandas DataFrame Python
The pandas. dataframe reset_index() reset the index column of the dataframe with the default sequential list of indexes. Let us understand with the below Python program how to remove the index of pandas dataframe.
In this python program example, we are dropping the index of the pandas dataframe by using the reset_index() method of dataframe.
Python Program to drop index of pandas DataFrame
import pandas as pd
dfobj = pd.DataFrame({
'Name': ['Jack', 'Rack', 'Max', 'David'],
'Marks':[100,100, 100,100],
'Subject': ['Math', 'Math', 'Music', 'Physic'],
},index =['Row_1','Row_2','Row_3','Row_4'])
print('original df:\n',dfobj)
dfobj.reset_index(drop=True, inplace=True)
print('\ndataframe after rest index:\n',dfobj)
Output
original df:
Name Marks Subject
Row_1 Jack 100 Math
Row_2 Rack 100 Math
Row_3 Max 100 Music
Row_4 David 100 Physic
dataframe after rest index:
Name Marks Subject
0 Jack 100 Math
1 Rack 100 Math
2 Max 100 Music
3 David 100 Physic
- Drop rows by multiple conditions in Pandas Dataframe
- Drop one or multiple rows in Pandas Dataframe
- How to drop rows with NAN values in DataFrame
2. Drop index column of Pandas DataFrame Python using set_index()
The dataframe set_index() method reset the index column of the pandas dataframe with the values of another column that passed as an argument.
In this Python program, we want to reset the dataframe default index with the “name” column. As we can see in the output the dataframe default column index has been overriding with “name” column values.
Python Program to drop index of pandas DataFrame
import pandas as pd
dfobj = pd.DataFrame({
'Name': ['Jack', 'Rack', 'Max', 'David'],
'Marks':[100,100, 100,100],
'Subject': ['Math', 'Math', 'Music', 'Physic'],
},index =['Row_1','Row_2','Row_3','Row_4'])
print('original df:\n',dfobj)
result_df = dfobj.set_index('Name')
print('dataframe after rest index:')
print(result_df)
Output
original df:
Name Marks Subject
Row_1 Jack 100 Math
Row_2 Rack 100 Math
Row_3 Max 100 Music
Row_4 David 100 Physic
dataframe after rest index:
Marks Subject
Name
Jack 100 Math
Rack 100 Math
Max 100 Music
David 100 Physic
Summary
In this post, we have used pandas. dataframe reset_index() and set_index() method to remove or drop index column of pandas dataframe