ExamAdept

How can you calculate the mean of a NumPy array arr along the second axis?

A:

arr.mean(axis=1)

B:

np.mean(arr, axis=1)

C:

np.mean(axis=1, arr)

D:

arr.mean(axis=0)

Answer: A

The mean() function with axis=1 calculates the mean along the second axis.

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What is the result of the expression np.linspace(0, 1, num=5, endpoint=False)?

A:

[0.0, 0.25, 0.5, 0.75, 1.0]

B:

[0.0, 0.2, 0.4, 0.6, 0.8]

C:

[0.0, 0.2, 0.4, 0.6, 0.8, 1.0]

D:

[0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2]

Answer: B

The np.linspace() function generates evenly spaced numbers over a specified range.

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How can you reshape a NumPy array arr to have 2 rows and 3 columns?

A:

np.reshape(arr, (2, 3))

B:

arr.resize(2, 3)

C:

arr.reshape(2, 3)

D:

np.resize(arr, (2, 3))

Answer: C

The reshape() function is used to reshape the array.

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Given a NumPy array arr = np.array([10, 20, 30, 40]), what does arr[1:3] return?

A:

[10, 20]

B:

[20, 30]

C:

[30, 40]

D:

[20, 30, 40]

Answer: B

The slice arr[1:3] extracts elements from index 1 to (3-1).

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How can you flatten a NumPy array arr in column-major order?

A:

arr.flatten()

B:

np.column_stack(arr)

C:

arr.ravel(order='F')

D:

np.column_major(arr)

Answer: C

The ravel() function with order='F' flattens the array in column-major order.

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Questions for: Arrays