Creating arrays

NumPy arrays are created from Python lists using the np.array() function.

Creating from lists and ranges

The most common ways:

np.array([[1, 2], [3, 4]])   # from a list
np.arange(0, 10, 2)           # [0 2 4 6 8], end excluded
np.linspace(0, 1, 5)          # [0.   0.25 0.5  0.75 1.  ], end included

arange takes a step size, linspace takes an element count. linspace is handy for plot axes or evenly spaced trial values.

Pre-filled arrays

For arrays whose size is known but whose contents will be filled later:

np.zeros((2, 3))     # 2x3 zeros
np.ones(4)           # [1. 1. 1. 1.]
np.full((2, 2), 7)   # 7 everywhere
np.eye(3)            # 3x3 identity matrix

The size is given as a tuple ((rows, columns)). The default type is float64, which is why 1. appears in the output.

Exercise Produce arrays

Create the array from 0 to 20 (exclusive) stepping by five with np.arange, and a 2-row, 2-column zero matrix with np.zeros. Print the array first, then the matrix's shape.

Show example solution

import numpy as np

a = np.arange(0, 20, 5)
z = np.zeros((2, 2))
print(a)
print(z.shape)

Test yourself

What does <code>np.arange(1, 7, 2)</code> produce?
- [1 2 3 4 5 6]
- [1 3 5]
- [1 3 5 7]
- [2 4 6]
Show answer

[1 3 5] — The end value (7) is excluded; it starts at 1 and steps by two.
Produce 11 evenly spaced values between 0 and 1.
```
x = np.____(0, 1, 11)
```
Show answer
```
x = np.linspace(0, 1, 11)
```
Match the functions with their results.

np.zeros(3)

[0. 0. 0.]
np.ones(2)

[1. 1.]
np.arange(3)

[0 1 2]
np.full(2, 5)

[5 5]

Next lesson: Array properties

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