(Python Example for Beginners)
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 :
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
Pandas Example – Write a Pandas program to replace the ‘qualify’ column contains the values ‘yes’ and ‘no’ with True and False
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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