w3resource

Pandas DataFrame: Sort the data frame first by 'name' in descending order, then by 'score' in ascending order

Pandas: DataFrame Exercise-16 with Solution

Write a Pandas program to sort the data frame first by 'name' in descending order, then by 'score' in ascending order.

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)
df = df.sort_values(by=['name', 'score'], ascending=[False, True])
print("Sort the data frame first by ‘name’ in descending order, then by ‘score’ in ascending order:")
print(df)

Sample Output:

Orginal rows:
        name  score  attempts qualify
a  Anastasia   12.5         1     yes
b       Dima    9.0         3      no
c  Katherine   16.5         2     yes
d      James    NaN         3      no
e      Emily    9.0         2      no
f    Michael   20.0         3     yes
g    Matthew   14.5         1     yes
h      Laura    NaN         1      no
i      Kevin    8.0         2      no
j      Jonas   19.0         1     yes
Sort the data frame first by ‘name’ in descending order, then by ‘score’ in ascending order:
        name  score  attempts qualify
f    Michael   20.0         3     yes
g    Matthew   14.5         1     yes
h      Laura    NaN         1      no
i      Kevin    8.0         2      no
c  Katherine   16.5         2     yes
j      Jonas   19.0         1     yes
d      James    NaN         3      no
e      Emily    9.0         2      no
b       Dima    9.0         3      no
a  Anastasia   12.5         1     yes   

Explanation:

The above code first creates a Pandas DataFrame ‘df’ from the dictionary ‘exam_data’ using the labels labels as the row index.

df.sort_values(by=['name', 'score'], ascending=[False, True]): This line sorts the DataFrame by the 'name' column in descending order and within each name, it sorts the 'score' column in ascending order.

Python-Pandas Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

Previous: Write a Pandas program to append a new row 'k' to DataFrame with given values for each column. Now delete the new row and return the original data frame.
Next: Write a Pandas program to replace the ‘qualify' column contains the values 'yes' and 'no' with True and False.

What is the difficulty level of this exercise?

Test your Programming skills with w3resource's quiz.



Follow us on Facebook and Twitter for latest update.