Pandas DataFrame: Replace the ‘qualify' column contains the values 'yes' and 'no' with True and False

Last update on December 21 2024 07:41:54 (UTC/GMT +8 hours)

Write a Pandas program to replace the ‘qualify' column contains the values 'yes' and 'no' with True and False.

Sample DataFrame:
exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],
'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],
'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}
labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
Values for each column will be:
name : ‘Suresh’, score: 15.5, attempts: 1, qualify: ‘yes’, label: ‘k’

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
exam_data  = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],
        'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],
        'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
        'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}
labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
df = pd.DataFrame(exam_data , index=labels)
print("Original rows:")
print(df)
print("\nReplace the 'qualify' column contains the values 'yes' and 'no'  with True and  False:")
df['qualify'] = df['qualify'].map({'yes': True, 'no': False})
print(df)

Sample Output:

Original rows:                                                          
   attempts       name qualify  score                                  
a         1  Anastasia     yes   12.5                                  
b         3       Dima      no    9.0                                  
c         2  Katherine     yes   16.5                                  
d         3      James      no    NaN                                  
e         2      Emily      no    9.0                                  
f         3    Michael     yes   20.0                                  
g         1    Matthew     yes   14.5                                  
h         1      Laura      no    NaN                                  
i         2      Kevin      no    8.0                                  
j         1      Jonas     yes   19.0                                  
                                                                       
Replace the 'qualify' column contains the values 'yes' and 'no'  with T
rue and  False:                                                        
   attempts       name  qualify  score                                 
a         1  Anastasia     True   12.5                                 
b         3       Dima    False    9.0                                 
c         2  Katherine     True   16.5 
d         3      James    False    NaN                                 
e         2      Emily    False    9.0                                 
f         3    Michael     True   20.0                                 
g         1    Matthew     True   14.5                                 
h         1      Laura    False    NaN                                 
i         2      Kevin    False    8.0                                 
j         1      Jonas     True   19.0                      

Explanation:

The above code first creates a pandas DataFrame from the ‘exam_data’ dictionary and assigns index labels to each row.

df['qualify'] = df['qualify'].map({'yes': True, 'no': False}): This line maps the values of the 'qualify' column to True and False based on whether the value is 'yes' or 'no', respectively.

Finally print(df) function prints the resulting DataFrame.

Python-Pandas Code Editor:

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