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 libraryimport pandas as pd # load csv filedf = pd.read_csv("supermarkets.csv") # show the dataframedf |
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 libraryimport pandas as pd # load csv filedf = 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 dataframedf |
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 libraryimport pandas as pd # load csv filedf = 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 dataframedf |
Output:
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