Pandas DataFrame: Replace the ‘qualify' column contains the values 'yes' and 'no' with True and False
17. Replacing Column Values (qualify)
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.
For more Practice: Solve these Related Problems:
- Write a Pandas program to replace the 'qualify' column values 'yes' and 'no' with True and False respectively, and then validate the data types.
- Write a Pandas program to map the 'qualify' column from string values to booleans and then count the number of True values.
- Write a Pandas program to update the 'qualify' column using a lambda function that converts 'yes' to True and 'no' to False.
- Write a Pandas program to replace the 'qualify' values with booleans and then create a new column indicating the inverse of these booleans.
Python-Pandas Code Editor:
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Next: Write a Pandas program to change the name 'James' to 'Suresh' in name column of the data frame.
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