Pandas DataFrame: Select the rows where the score is missing
9. Selecting Rows with Missing Score
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.
For more Practice: Solve these Related Problems:
- Write a Pandas program to select rows where the 'score' column is NaN and then count these rows.
- Write a Pandas program to filter out rows with missing scores and then fill these missing values with the mean score.
- Write a Pandas program to select rows with a missing 'score' and then update the 'qualify' column to 'unknown'.
- Write a Pandas program to extract rows with NaN in 'score' and then display only the 'name' and 'score' columns.
Python-Pandas Code Editor:
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