What does inplace mean in Pandas?
In this article, we will see Inplace in pandas. Inplace is an argument used in different functions. Some functions in which inplace is used as an attributes like, set_index(), dropna(), fillna(), reset_index(), drop(), replace() and many more. The default value of this attribute is False and it returns the copy of the object.
Here we are using fillna() methods.
Syntax: dataframe.fillna(dataframe.mean(), inplace = False)
Let’s understand this method with step-wise implementation:
Step 1. First, we import all the required libraries.
Python3
# import required module import pandas as pd |
Step 2.Creating dataframe.
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# creating dataframe dataframe = pd.DataFrame({ 'Name' :[ 'Shobhit' , 'vaibhav' , 'vimal' , 'Sourabh' ], 'Class' :[ 11 , 12 , 10 , 9 ], 'Age' :[ 18 , 20 , 21 , 17 ]}) # Checking created dataframe display(dataframe) |
Output :
Step 3.To see the inplace use we are going to use the rename function where we are renaming “Name” Column to “FirstName”.
In this step, We will not use inplace in our code.
Python3
# without using inplace renaming the column new_data = dataframe.rename(columns = { 'Name' : 'FirstName' }) # check new_data display(new_data) |
Output :
We can clearly see that there are no changes in the original dataframe. Through this, we conclude that the default value of inplace is False.
Now in this step, we will use inplace with False value.
Python3
# putting inplace=False new_data_2 = dataframe.rename(columns = { 'Name' : 'FirstName' }, inplace = False ) #check new_data_2 display(new_data_2) |
Output :
Again we can clearly see that there are no changes in the original dataset.
At last, we are putting inplace value equal to True.
Python3
# Putting Inplace=True dataframe.rename(columns = { 'Name' : 'FirstName' }, inplace = True ) # check whether dataframe is modified or not print (dataframe) |
Output :
Finally, we can see that the original dataframe columns have been modified from “Name” to “FirstName”.
Below is the complete program based on the above approach :
Python3
# importing pandas import pandas as pd # creating dataframe dataframe = pd.DataFrame({ 'Name' :[ 'Shobhit' , 'Vaibhav' , 'Vimal' , 'Sourabh' ], 'Class' :[ 11 , 12 , 10 , 9 ], 'Age' :[ 18 , 20 , 21 , 17 ]}) # Checking created dataframe # copied dataframe display(dataframe) # without using inplace renaming the column new_data = dataframe.rename(columns = { 'Name' : 'FirstName' }) # Copied dataframe display(new_data) # checking whether dataframe is modified or not # Original dataframe display(dataframe) # putting inplace=False new_data_2 = dataframe.rename(columns = { 'Name' : 'FirstName' }, inplace = False ) # Copied dataframe display(new_data_2) # checking whether dataframe is modified or not # Original dataframe display(dataframe) # Putting Inplace=True dataframe.rename(columns = { 'Name' : 'FirstName' }, inplace = True ) # checking whether dataframe is modified or not # Original dataframe display(dataframe) |
Output :
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