NumPy Mastery Quiz — Quiz

dtypes, reshaping, ufuncs, axes, random Generators and linear algebra. · 10 questions

Question 1 of 10

How many bytes does one `float32` value use?

a2b4c8d16

Question 2 of 10

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)

Question 3 of 10

Which operator performs matrix multiplication of two NumPy arrays?

a*b**c@d//

Question 4 of 10

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

a(3, 3)b(12, 3)c(3, 12)d(3, 4)

Question 5 of 10

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]c21d[[5 7 9]]

Question 6 of 10

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)

Question 7 of 10

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

ainv() only works on integer matricesbsolve() also returns eigenvaluescsolve() works on non-square matricesdsolve() is faster and more numerically accurate

Question 8 of 10

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)

Question 9 of 10

What does this code print?

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

a[200 240]b[-56 -16]cOverflowError is raisedd[127 127]

Question 10 of 10

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 copiesbIt raises an error because views are read-onlycIt becomes NaNdIt also becomes 99 because v is a view sharing memory