Create NumPy Array

Summary: in this tutorial, you’ll learn how to create NumPy arrays including one-dimensional, two-dimensional, and three-dimensional arrays.

The array is the core data structure of the NumPy library. A NumPy array is a grid of values with the same type and indexed by a tuple of non-negative integers.

All arrays are instances of the ndarray class. To create a new NumPy array, you use the array() function of the NumPy library.

Creating one-dimensional arrays #

The following example uses the array() function to create a one-dimensional (1-D) array:

import numpy as np

a = np.array([1, 2, 3])

print(type(a))
print(a)Code language: Python (python)

Output:

<class 'numpy.ndarray'>
[1 2 3]Code language: Python (python)

How it works.

First, import the numpy library as np:

import numpy as npCode language: Python (python)

Second, create a 1D array by passing a list of three integers:

a = np.array([1, 2, 3])Code language: Python (python)

The array() function returns a new instance of the ndarray type. Therefore, the type(a) returns <class 'numpy.ndarray'>.

A 1-D array is known as a vector.

Getting the dimension of an array #

To get the number of dimensions of an array, you use the ndim property. In NumPy, dimensions are called axes. For example:

import numpy as np

a = np.array([1, 2, 3])

print(a.ndim)Code language: Python (python)

Output:

1Code language: Python (python)

In this example, The ndim property returns one as expected.

Getting the data type of array elements #

To get the data type of the elements of an array, you use the dtype property. For example:

import numpy as np

a = np.array([1, 2, 3])

print(a.dtype)Code language: Python (python)

Output:

int32Code language: Python (python)

In this example, the type of the elements is int32. If you want to set the type of the array’s elements, you can use the dtype argument of the array() function. For example:

import numpy as np

a = np.array([1, 2, 3], dtype=np.float64)

print(a)
print(a.dtype)Code language: Python (python)

Output:

[1. 2. 3.]
float64Code language: Python (python)

In this example, the numbers of the array have the decimal point (.) and the data type of its elements is float64.

Creating two-dimensional arrays #

The following example uses the array() function to create a two-dimensional (2-D) array:

import numpy as np

b = np.array(
    [
        [1, 2, 3],
        [4, 5, 6]
    ]
)

print(b)
print(b.ndim)Code language: Python (python)

Output:

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

2Code language: Python (python)

In this example, we pass a list of a list of integers to the array() function. The ndim property returns 2 as expected.

A good tip to get the number of dimensions of an array is that you count the square brackets ([) until you encounter the first number. The number of square brackets is the number of dimensions or axes.

A two-dimensional array is also called a matrix.

Creating three-dimensional array #

The following example uses the array() function to create a three-dimensional (3-D) array:

import numpy as np

c = np.array(
    [
        [
            [1, 2, 3],
            [4, 5, 6]
        ],
        [
            [7, 8, 9],
            [10, 11, 12]
        ],
    ]
)

print(c.ndim)Code language: Python (python)

Output:

3Code language: Python (python)

Note that a 3-D array is also called a tensor.

Getting shapes of arrays #

To find the number of axes and the number of elements on each axis of an array, you use the shape property. For example:

import numpy as np


a = np.array([1, 2, 3])
print(a.shape)  # (3,)

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

c = np.array(
    [
        [
            [1, 2, 3],
            [4, 5, 6]
        ],
        [
            [7, 8, 9],
            [10, 11, 12]
        ],
    ]
)
print(c.shape)  # (2, 2, 3)Code language: Python (python)

Output:

(3,)
(2, 3)   
(2, 2, 3)Code language: Python (python)

The following picture explains the shape of each array a, b, and c:

The shape property returns a tuple:

• The number of elements in the tuple is the number of axes.
• Each tuple element stores the number of elements of the corresponding axis.

Summary #

• A numpy array is a grid of values with the same type and is indexed by a tuple of non-negative values.
• Numpy arrays have the type of ndarray.
• Use the array() function to create a numpy array.
• Use the dtype property to get the data type of array’s elements.
• Use the ndim property to get the number of dimensions or the number of axes.
• Use the shape property to get the number of dimensions as well as the number of elements in each dimension.