How the object is handled when inplace=True is passed vs. when inplace=False, in Pandas dataframe

Let’s understand inplace with below code block where it returns None df3

df3=df1.set_index('account_no',inplace=True, drop=False)
#df3.set_index('account_no',inplace=False, drop=True)
print("Index value of DF3 : \n", df3)

Set_index: set_index method of dataframe is used to set the index of dataframe using existing column or array.

Syntax: dataframe.set_index(key,append=False, inplace =False,drop=True)

Let play with the datasource of

Let’s understand two important parameter inplace and drop.

When inplace is set to “True”  it tells change need to be done in original dataframe and nothing gets returned as object

Whereas when inplace is set to “False” it changes in the copy of dataframe and returned as object without affecting original dataframe.

Lets check with example

#inplace is set to True
df3=df1.set_index('account_no',inplace=True, drop=False)
#df3.set_index('account_no',inplace=False, drop=True)
print("DF3 with inplace set to True : \n", df3)

print("\nDF1 Data : \n", df1)

Output:

DF3 with inplace set to True :

 None

DF1 Data :

             account_no  branch  city_code customer_name  amount

account_no                                                    

2112              2112  3212.0      321.0       Sidhika   19000

2119              2119     NaN      215.0      Prayansh   12000

2115              2115  4321.0      212.0       Rishika   15000

2435              2435  2312.0        NaN      Sagarika   13000

2356              2356  7548.0      256.0           NaN   15000

So here we can see the output of DF3 is None because df1.set_index uses parameter inplace=”True” which says that changes need to be done in original dataframe and returns nothing.

df3=df1.set_index('account_no',inplace=True, drop=False)

Now lets change the inplace to Flase, by definition it says that it should change in the copy of dataframe object and returns an df object

#inplace is set to True
df3=df1.set_index('account_no',inplace=False, drop=False)
#df3.set_index('account_no',inplace=False, drop=True)
print("DF3 with inplace set to False : \n", df3)

print("\nDF1 Data : \n", df1)

Output:

DF3 with inplace set to False :

             account_no  branch  city_code customer_name  amount

account_no                                                    

2112              2112  3212.0      321.0       Sidhika   19000

2119              2119     NaN      215.0      Prayansh   12000

2115              2115  4321.0      212.0       Rishika   15000

2435              2435  2312.0        NaN      Sagarika   13000

2356              2356  7548.0      256.0           NaN   15000

DF1 Data :

    account_no  branch  city_code customer_name  amount

0        2112  3212.0      321.0       Sidhika   19000

1        2119     NaN      215.0      Prayansh   12000

2        2115  4321.0      212.0       Rishika   15000

3        2435  2312.0        NaN      Sagarika   13000

4        2356  7548.0      256.0           NaN   15000

With the output we can see the changes are done in copy of original dataframe without affecting the original one.

Complete Program:

import pandas as pd
import numpy as np

df1 = pd.read_csv("NullFilterExample.csv")

print('Original DF rows \n',df1 , '\n')

#inplace is set to False
df3=df1.set_index('account_no',inplace=False, drop=False)
#df3.set_index('account_no',inplace=False, drop=True)
print("DF3 with inplace set to False : \n", df3)

print("\nDF1 Data : \n", df1)


#inplace is set to True
df4=df1.set_index('account_no',inplace=True, drop=False)
#df3.set_index('account_no',inplace=False, drop=True)
print("DF3 with inplace set to True : \n", df4)

print("\nDF1 Data : \n", df1)

Data Science with…Python J

Post Reference: Vikram Aristocratic Elfin Share