Creating NumPy Arrays

Most of the time you won’t type numbers by hand. NumPy can build arrays of any size in one line. This lesson shows the ways you’ll use every day, and how to read an array’s shape.

From a list (1-D and 2-D)#

A list gives a 1-D array (one row). A list of lists gives a 2-D array (a table of rows and columns):

import numpy as np

prices = np.array([120, 80, 45])
table = np.array([[1, 2, 3],
                  [4, 5, 6]])

print(prices.shape, prices.ndim)
print(table.shape, table.ndim)
print(table.size)

Output:

(3,) 1
(2, 3) 2
6

• shape is the size along each dimension: (2, 3) means 2 rows and 3 columns.
• ndim is the number of dimensions.
• size is the total number of elements: 2 × 3 = 6.

Ranges: arange and linspace#

np.arange(start, stop, step) works like Python’s range, but returns an array. The stop value is not included:

import numpy as np

print(np.arange(5))
print(np.arange(2, 11, 2))
print(np.linspace(0, 1, 5))

Output:

[0 1 2 3 4]
[ 2  4  6  8 10]
[0.   0.25 0.5  0.75 1.  ]

np.linspace(start, stop, num) is different: you say *how many* numbers you want, and NumPy spaces them evenly, including the stop value. It’s perfect for drawing smooth charts.

Zeros, ones and a constant#

Useful as a starting point that you fill in later:

import numpy as np

print(np.zeros(4))
print(np.ones((2, 3)))
print(np.full((2, 2), 7))

Output:

[0. 0. 0. 0.]
[[1. 1. 1.]
 [1. 1. 1.]]
[[7 7]
 [7 7]]

For 2-D, pass the shape as a tuple: (2, 3), with brackets.

Random numbers#

Simulations, games and machine learning need random data. Create a generator, give it a seed so you get the same numbers every run, then ask for numbers:

import numpy as np

rng = np.random.default_rng(42)
print(rng.integers(1, 7, size=5))
print(rng.random(3).round(2))

Output:

[1 5 4 3 3]
[0.7  0.09 0.98]

rng.integers(1, 7, size=5) rolls a dice 5 times (1 to 6; the 7 is excluded). rng.random(3) gives 3 decimals between 0 and 1.

Choosing the data type#

You can pick the type yourself with dtype, or convert later with astype:

import numpy as np

a = np.array([1, 2, 3], dtype=float)
b = np.array([3.7, 1.2, 9.9]).astype(int)
print(a)
print(b)

Output:

[1. 2. 3.]
[3 1 9]

astype(int) cuts off the decimal part; it doesn’t round. Use np.round(x) first if you want rounding.

Quick check

What is the shape of np.zeros((3, 4))? It has 3 rows and 4 columns, so the shape is (3, 4) and the size is 12.

Practice#

1. Make an array of the even numbers from 0 to 20.
2. Make a 3 × 3 array filled with 5.
3. Simulate 10 dice rolls with a seed of 7 and print them.

Summary#

You want	Use
An array from data	np.array(list)
A range of numbers	np.arange(start, stop, step)
N evenly spaced numbers	np.linspace(start, stop, n)
An empty starting array	np.zeros(shape) / np.ones(shape)
Random numbers	np.random.default_rng(seed)