Data Science with Python NumPy Array Manipulation Flashcards
7 cards from real Data Science with Python Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
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What does `np.broadcast_to(a, shape)` do?
Answer: Returns a read-only view of the array broadcast to the given shape
`np.broadcast_to` returns a read-only view of the array stretched to the target shape without copying data.
Which function stacks 1-D arrays as rows into a 2-D array?
Answer: np.vstack
`np.vstack` stacks arrays vertically (row-wise), converting 1-D arrays into rows of a 2-D array.
What is the result of `np.arange(10).reshape(2,5)[1, ::2]`?
Answer: array([5, 7, 9])
Row index 1 gives [5,6,7,8,9], and step-2 slicing starting at 0 yields elements at positions 0,2,4 → [5,7,9].
Which attribute gives the total number of elements in a NumPy array?
Answer: array.size
`array.size` returns the total count of elements (product of all shape dimensions).
What does `np.squeeze(a)` do?
Answer: Removes axes of length one from the array's shape
`np.squeeze` removes dimensions of size 1, reducing unnecessary singleton axes from the shape.
How do you swap axes 0 and 1 of a 2-D NumPy array `a`?
Answer: np.swapaxes(a, 0, 1)
`np.swapaxes(a, 0, 1)` interchanges two specified axes, equivalent to a transpose for 2-D arrays.
What does the `np.tile(a, reps)` function do?
Answer: Constructs an array by repeating `a` the number of times given by `reps`
`np.tile` repeats the entire array `a` along each axis as specified by `reps`, constructing a larger array.
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