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

Pandas: DataFrame Exercise-17 with Solution

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("Orginal 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:

Orginal 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                      

Python Code Editor:


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Previous: Write a Pandas program to sort the data frame first by 'name' in descending order, then by 'score' in ascending order.
Next: Write a Pandas program to change the name 'James' to 'Suresh' in name column of the data frame.

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Python: Tips of the Day

Creates a dictionary with the same keys as the provided dictionary and values generated by running the provided function for each value

Example:

def tips_map_values(obj, fn):
  ret = {}
  for key in obj.keys():
    ret[key] = fn(obj[key])
  return ret
users = {
  'Owen': { 'user': 'Owen', 'age': 29 },
  'Eddie': { 'user': 'Eddie', 'age': 15 }
}

print(tips_map_values(users, lambda u : u['age'])) # {'Owen': 29, 'Eddie': 15}

Output:

{'Owen': 29, 'Eddie': 15}