Question bank

Pick an answer to see if you're right — with an explanation.

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NumPyEasy

How many bytes does one `float32` value use?

d16

NumPyEasy

Which is the recommended way to create random numbers in modern NumPy?

arng = np.random.default_rng(42)

bnp.random.seed(42); np.random.rand()

cimport random; random.random()

dnp.rand(42)

NumPyEasy

Which operator performs matrix multiplication of two NumPy arrays?

b**

d//

NumPyEasy

What is the shape of `np.arange(12).reshape(3, -1)`?

a(3, 3)

b(12, 3)

c(3, 12)

d(3, 4)

NumPyMedium

What does this print?

a = np.array([[1, 2, 3],
              [4, 5, 6]])
print(a.sum(axis=0))

a[6 15]

b[5 7 9]

c21

d[[5 7 9]]

NumPyMedium

What is the difference between `np.stack([a, b])` and `np.concatenate([a, b])` for two 1-D arrays of length 3?

astack gives shape (2, 3); concatenate gives shape (6,)

bBoth give shape (6,)

cstack gives shape (6,); concatenate gives (2, 3)

dBoth give shape (2, 3)

NumPyMedium

Why is `np.linalg.solve(A, b)` preferred over `np.linalg.inv(A) @ b`?

ainv() only works on integer matrices

bsolve() also returns eigenvalues

csolve() works on non-square matrices

dsolve() is faster and more numerically accurate

NumPyMedium

To divide each row of `m` (shape (3, 4)) by its row total, which expression is correct?

am / m.sum(axis=0)

bm / m.sum()

cm / m.sum(axis=1, keepdims=True)

dm / m.sum(axis=1)

NumPyHard

What does this code print?

x = np.array([100, 120], dtype=np.int8)
print(x + x)

a[200 240]

b[-56 -16]

cOverflowError is raised

d[127 127]

NumPyHard

After `v = a.reshape(2, 6)` on a contiguous 12-element array `a`, you run `v[0, 0] = 99`. What happens to `a[0]`?

aIt stays unchanged because reshape copies

bIt raises an error because views are read-only

cIt becomes NaN

dIt also becomes 99 because v is a view sharing memory