Module 1 · Lesson 4

Reshaping & Dimensions

⬇ Download notebook ▶ Open in Colab

.reshape(new_shape) returns a view with the same data laid out under a different shape (total element count must match — use -1 to let NumPy infer one dimension). .ravel() flattens to 1-D (a view when possible), .flatten() always returns a copy. .T (or np.transpose) swaps axes, and np.newaxis inserts a new length-1 axis, which is the standard way to turn a 1-D array into a row or column vector.

arr = np.arange(12)
print(arr.reshape(3, 4))
print(arr.reshape(3, -1).shape)   # -1 means "figure this dimension out"

col = arr[:4][:, np.newaxis]      # turn a 1-D array into a column vector
print(col.shape)
print(arr.reshape(3, 4).T)        # transpose

Output:

[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
(3, 4)
(4, 1)
[[ 0  4  8]
 [ 1  5  9]
 [ 2  6 10]
 [ 3  7 11]]
4.1

Flatten and pair up

Write to_pairs(arr) that takes an array of any shape with an even number of elements and returns it reshaped into shape (-1, 2) — i.e. a sequence of [a, b] pairs, read in row-major (C) order.

💡 Hint

.ravel() first to get a flat 1-D sequence, then .reshape(-1, 2).

✅ Solution
def to_pairs(arr):
    return arr.ravel().reshape(-1, 2)