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

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

Output: