Array properties

NumPy arrays have properties like shape, size, ndim, and dtype.

shape, ndim, size

Every array has attributes describing its structure:

a = np.array([[1, 2, 3],
              [4, 5, 6]])
print(a.shape)   # (2, 3)  rows, columns
print(a.ndim)    # 2       number of dimensions
print(a.size)    # 6       total elements

Most ML bugs are shape mismatches; when something goes wrong, the first thing to do is print shape.

dtype

dtype tells the type of the elements. Integers become int64 and decimals float64; in a mixed list NumPy converts everything to decimals:

print(np.array([1, 2]).dtype)      # int64
print(np.array([1, 2.5]).dtype)    # float64
x = np.array([1, 2]).astype(np.float32)
print(x.dtype)                     # float32

Deep learning often uses float32 to save memory; it is converted with astype.

Exercise Inspect the array

Print the shape, ndim and size of array a in a single print, separated by spaces.

Show example solution

import numpy as np

a = np.ones((3, 4, 2))

print(a.shape, a.ndim, a.size)

Test yourself

What is <code>np.zeros((4, 5)).size</code>?
- 20
- 9
- 2
- (4, 5)
Show answer

20 — size is the product of all dimensions: 4 × 5 = 20.

Convert the array to float32.

x = a.____(np.float32)

Show answer

x = a.astype(np.float32)

Match the attributes.

shape

Tuple of dimensions
ndim

Number of dimensions
size

Total elements
dtype

Element type

Next lesson: Array indexing

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