Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN: 9780133594140
Author: James Kurose, Keith Ross
Publisher: PEARSON
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def drop_uninformative_objects(X, y):
# your code here
return X_subset, y_subset
# TEST drop_uninformative_objects function
A = pd.DataFrame(np.array([
[0, 3, np.nan],
[4, np.nan, np.nan],
[np.nan, 6, 7],
[np.nan, np.nan, np.nan],
[5, 5, 5],
[np.nan, 8, np.nan],
]))
b = pd.Series(np.arange(6))
A_subset, b_subset = drop_uninformative_objects(A, b)
assert A_subset.shape == (3, 3)
assert b_subset.shape == (3,)
Transcribed Image Text:Write a function that removes objects from the dataset, if they have more than 50% of missing values.
• Use pandas isnull() method to find missing (NaN) values. See example above.
• Compute number of missing values in each row. Use argument axis to specify the dimention of summation.
• From both the object matrix X and the target variable y, select a subset of rows with at most 50% of missing values.
• See this pandas tutorial if you struggle to implement this task.
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