Pandas DataFrame: Select the rows where the score is missing

Last update on December 21 2024 07:42:28 (UTC/GMT +8 hours)

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                              

Explanation:

In the above code -

df = pd.DataFrame(exam_data , index=labels): This line creates a pandas DataFrame called ‘df’ from a dictionary ’exam_data’ with specified index labels. The DataFrame has columns 'name', 'score', 'attempts', and 'qualify' which are created from the corresponding values in the dictionary.

print(df[df['score'].isnull()]): This code filters the rows in the DataFrame where the 'score' column is null using the isnull() method and prints the resulting subset of the DataFrame using boolean indexing with df[df['score'].isnull()]. This will show the rows where the 'score' column has missing or NaN (not a number) values.

Python-Pandas Code Editor:

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