Replace the column contains the values ‘yes’ and ‘no’ with True and False In Python-Pandas

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Let’s discuss a program To change the values from a column that contains the values ‘YES’ and ‘NO’ with TRUE and FALSE.

First, Let’s see a dataset.

Code:

Python3

# import pandas library
import pandas as pd
   
# load csv file
df = pd.read_csv("supermarkets.csv")
   
# show the dataframe
df

Output :

For downloading the used csv file Click Here.

Now, Let’s see the multiple ways to do this task:

Method 1: Using Series.map().
This method is used to map values from two series having one column the same.

Syntax: Series.map(arg, na_action=None).
Return type: Pandas Series with the same as an index as a caller.

Example: Replace the ‘commissioned’ column contains the values ‘yes’ and ‘no’ with True and False.
Code:

Python3

# import pandas library
import pandas as pd
   
# load csv file
df = pd.read_csv("supermarkets.csv")
   
# replace the ‘commissioned' column contains
# the values 'yes' and 'no'  with 
# True and  False:
df['commissioned'] = df['commissioned'].map(
                   {'yes':True ,'no':False})
  
# show the dataframe
df

Output :

Method 2: Using DataFrame.replace().
This method is used to replace a string, regex, list, dictionary, series, number, etc. from a data frame.

Syntax: DataFrame.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method=’pad’, axis=None)
Return type: Updated Data frame

Example: Replace the ‘commissioned’ column contains the values ‘yes’ and ‘no’ with True and False.
Code:

Python3

# import pandas library
import pandas as pd
  
# load csv file
df = pd.read_csv("supermarkets.csv")
  
# replace the ‘commissioned' column 
# contains the values 'yes' and 'no'
#  with True and  False:
df = df.replace({'commissioned': {'yes': True, 
                                'no': False}})
  
# show the dataframe
df