NumPy Creating Arrays

There are many ways to create a NumPy array, from a list or with built-in helper functions.

From a List or Tuple

Pass a list (or tuple) to np.array():

Example

python

import numpy as np

a = np.array([10, 20, 30])
b = np.array((1.5, 2.5, 3.5))
print(a)
print(b)

Output

[10 20 30]
[1.5 2.5 3.5]

Dimensions

The number of dimensions tells you how many "levels" of nesting an array has.

Dimensions	Looks like	Example
0-D	A single number	`np.array(42)`
1-D	A row of numbers	daily temperatures of one week
2-D	A table with rows and columns	marks of students in subjects
3-D	Several tables stacked	marks of students, per subject, per term

Example

python

import numpy as np

zero_d = np.array(42)
one_d = np.array([1, 2, 3])
two_d = np.array([[1, 2, 3], [4, 5, 6]])
three_d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

print(zero_d.ndim, one_d.ndim, two_d.ndim, three_d.ndim)
print(two_d)

Output

0 1 2 3
[[1 2 3]
 [4 5 6]]

The ndim attribute gives the number of dimensions.

Every row of a 2-D array must have the same length. np.array([[1, 2], [3]]) raises an error, because NumPy needs a proper rectangle.

A 2-D Example: Marks Table

Example

python

import numpy as np

# rows = students, columns = Maths, English, Science
marks = np.array([
    [85, 72, 90],
    [60, 88, 75],
    [92, 95, 89]
])
print(marks)
print("Shape:", marks.shape)

Output

[[85 72 90]
 [60 88 75]
 [92 95 89]]
Shape: (3, 3)

Helper Functions

You often need an array before you have the data. NumPy has functions for that:

Example

python

import numpy as np

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

Output

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

Ranges of Numbers

np.arange() works like Python's range(), but returns an array. np.linspace() gives a fixed count of evenly spaced numbers between two values.

Example

python

import numpy as np

print(np.arange(5))
print(np.arange(2, 20, 4))
print(np.linspace(0, 1, 5))
print(np.linspace(0, 100, 3))

Output

[0 1 2 3 4]
[ 2  6 10 14 18]
[0.   0.25 0.5  0.75 1.  ]
[  0.  50. 100.]

Function	What it makes
`np.array(list)`	Array from your data
`np.zeros(shape)`	All zeros
`np.ones(shape)`	All ones
`np.full(shape, value)`	All the same value
`np.eye(n)`	Identity matrix (1s on the diagonal)
`np.arange(start, stop, step)`	Numbers with a fixed step
`np.linspace(start, stop, num)`	A fixed number of evenly spaced values

arange does not include the stop value, but linspace does include it by default.

Exercise

What is the ndim of np.array([[1, 2], [3, 4], [5, 6]])?

1236