﻿ Python Pandas: Replace the 'qualify' column contains the values 'yes' and 'no' with True and False - w3resource

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

## Python Pandas: DateFrame Exercise-17 with Solution

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

Sample DateFrame:
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:

```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)```

Improve this sample solution and post your code through Disqus