Pandas DataFrame: Select the rows where the score is missing - w3resource
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Pandas DataFrame: Select the rows where the score is missing

Pandas: DataFrame Exercise-9 with Solution

Write a Pandas program to select the rows where the score is missing, i.e. is NaN.

Sample DataFrame:
Sample Python dictionary data and list labels:
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', labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']

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("Rows where score is missing:")
print(df[df['score'].isnull()])

Sample Output:

Rows where score is missing:
   attempts   name qualify  score
d         3  James      no    NaN
h         1  Laura      no    NaN

Python-Pandas Code Editor:

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Next: Write a Pandas program to select the rows the score is between 15 and 20 (inclusive).

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Python: Tips of the Day

Walrus Operator:

>>> a = ['j', 'a', 'k', 'd', 'c']
>>> if (n := len(a))%2 == 1:
...     print(f'The number of letters is {n}, which is odd.')
...
The number of letters is 5, which is odd.