In this tutorial, you will learn how to create NumPy arrays, including one-dimensional, two-dimensional, and three-dimensional arrays, and explore their properties.

Introduction

A NumPy array is the core data structure provided by the NumPy library. It represents a grid of values, all of the same type, indexed by a tuple of non-negative integers. All NumPy arrays are instances of the ndarray class and can be created using the array() function.

Creating One-Dimensional Arrays

To create a 1D array, pass a Python list to the np.array() function:

import numpy as np

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

print(type(a))
print(a)

Output:

<class 'numpy.ndarray'>
[1 2 3]

This creates a 1D NumPy array (also known as a vector).

Getting the Dimension of an Array

Use the ndim property to get the number of dimensions (axes):

print(a.ndim)  # Output: 1

Getting the Data Type of Array Elements

Use the dtype property to get the data type of array elements:

print(a.dtype)  # Output: int32 (may vary based on system)

You can explicitly set the data type using the dtype argument:

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

Output:

[1. 2. 3.]
float64

Creating Two-Dimensional Arrays

A 2D array (matrix) is created by passing a list of lists:

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

print(b)
print(b.ndim)

Output:

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

Creating Three-Dimensional Arrays

A 3D array (tensor) is created by nesting lists within lists:

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

print(c.ndim)  # Output: 3

Getting the Shape of Arrays

Use the shape property to find the number of axes and the number of elements along each axis:

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)

The shape property returns a tuple:

  • The number of elements in the tuple is the number of dimensions (axes).

  • Each element represents the size along that dimension.

Summary

  • A NumPy array is a grid of values with the same data type.

  • Arrays are instances of the ndarray class and are created using np.array().

  • Use ndim to get the number of dimensions.

  • Use dtype to check or specify the data type of elements.

  • Use shape to determine the structure of the array in terms of axes and elements per axis.

 

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