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

Last update on December 21 2024 07:41:53 (UTC/GMT +8 hours)

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.