def drop_uninformative_objects(X, y):# your code herereturn X_subset, y_subset # TEST drop_uninformative_objects functionA = 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,) 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.

Q: Find and return rows with more than 50% nan values

Answered: Write a function that removes objects…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Question

100%

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,)