Reshaping arrays

The reshape() method changes the array's shape while keeping the total element count.

reshape

reshape gives an array a new shape without changing the number of elements. The product of the new shape must equal the old size:

a = np.arange(6)          # [0 1 2 3 4 5]
print(a.reshape(2, 3))    # [[0 1 2] [3 4 5]]
print(a.reshape(3, -1))   # -1: work out the columns -> (3, 2)
# a.reshape(4, 2)  ValueError: 6 elements don't fit into 8

The elements are placed row by row (C order).

flatten and transpose

flatten() reduces the array to one dimension; .T turns rows into columns:

m = np.array([[1, 2, 3],
              [4, 5, 6]])
print(m.flatten())   # [1 2 3 4 5 6]
print(m.T.shape)     # (3, 2)
print(m.T)           # [[1 4] [2 5] [3 6]]

With image data, turning 28×28 pixels into a vector of length 784 before giving it to a neural network is a common step.

Exercise Reshape

Turn the array np.arange(1, 13) into a matrix with 3 rows (use -1). Print the matrix, then the shape of its transpose.

Show example solution

import numpy as np

a = np.arange(1, 13)

m = a.reshape(3, -1)
print(m)
print(m.T.shape)

Test yourself

Which is invalid for an array of 12 elements?
- reshape(2, -1)
- reshape(2, 2, 3)
- reshape(5, -1)
- reshape(3, 4)
Show answer

reshape(5, -1) — 12 isn't divisible by 5, so -1 can't be worked out and a ValueError occurs.
Reduce the array to one dimension.
```
v = m.____()
```
Show answer
```
v = m.flatten()
```
Match the operations with the resulting shapes (a = np.arange(12)).

a.reshape(3, 4)

(3, 4)
a.reshape(3, 4).T

(4, 3)
a.reshape(2, -1)

(2, 6)
a.reshape(2, 6).flatten()

(12,)

Next lesson: Summary - NumPy basics

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