Introduction to NumPy in Python with Simple Example

In this post, we will go through the introduction to NumPy library and see what is numpy library, why we use it, difference between numpy and list, how to install a NumPy library, and more with detailed explanations and examples.

So let’s start with learning with a very basic question: ‘What is Numpy?‘

What is NumPy?

Numpy is a Python external library that helps us perform various scientific Computations and allows us to work with multidimensional arrays more efficiently.

After learning that Numpy is used to working with multidimensional arrays you might wonder why we use this external library if we have a Python list. We can also use List to work with multidimensional arrays. So, this is a very valid question. I will tell you why we use numpy if we have a Python list. let’s explore the difference.

Difference between List and NumPy

List	Numpy
List is slow as compared to Numpy.	Numpy is faster as compared to the Python list.
Basic data structure	Specialized data structure for numerical data
Consumes more memory	Consumes less memory
Type checking when iterating through object	No type checking when iterating through object
store data at non-contiguous memory location	store data at contiguous memory. location.
Slower for numerical operations	Designed for fast numerical operations
Can store elements of any data type	Typically stores elements of the same data type

Install NumPy Library

To Install the numpy library run the below command in your command prompt.

pip install numpy

Import NumPy Library

One of the most popular ways to import the NumPy library is by using the import keyword and creating an alias, typically ‘np‘.

import numpy as np

Now let’s create a simple numpy array and see the output how numpy array look like.

Create NumPy Array

To create a numpy array we use the array() function from the numpy library. This function takes a list (or multi-dimension list) as an input and converts it into a numpy array.

import numpy as np

my_array = np.array([1,3,5,7,9,11])
print(my_array)

Output

[ 1  3  5  7  9 11]

NOTE: In Python, list elements are separated by commas, whereas NumPy arrays store data elements without comma separators. This represents another fundamental difference between lists and NumPy arrays.

Now, let’s explore how to create arrays with various dimensions.

Dimensions in Arrays

• 1D array → [ 1 3 5 7 9 11]
• 2D array → [[1 2 3 4 5 6 7]]
• 3D array → [[[1 2 3 4 5 6 7]]]
• Higher Dimensional arrays

Check Dimentions of an array

To check the dimensions of an array we use a ndim attribute. it returns the dimensions of a numpy array.

import numpy as np

# 1D array
a1 = np.array([1,2,3,4,5])
print(a1.ndim) # Output: 1

# 2D array
a2 = np.array([[1,2,3,4,5,6,7]])
print(a2.ndim) # Output: 2


# 3D array
a3 = np.array([[[1,2,3,4,5,6,7]]])
print(a3.ndim) # Output: 3

Or there’s a simple trick to identify the dimensions of an array: just count the number of brackets on one side. For example, if there are three brackets, it’s a 3D array.

Now let’s create different dimensions of arrays for our better understanding and practice.

1D Array

We’ve already seen in the previous examples how to create a 1-Dimensional array. Simply pass a 1D list inside the array() function, and it will create a 1D array for you.

import numpy as np

ar1 = np.array([1,2,3,5,7,11,13,17])
print(f"1D Array: {ar1}")
print(f"Dimension: {ar1.ndim}")

Output

1D Array: [ 1  2  3  5  7 11 13 17]
Dimension: 1

2D Array

To create a 2D array, pass a 2D list inside the array() function, and it will create a 2D array. However, ensure that the inner lists contain the same number of data elements; otherwise, it will result in an error.

import numpy as np

ar2 = np.array([[1,2,3,4],[5,6,7,8]])
print(ar2)
print(f"Dimension: {ar2.ndim}")

Output

[[1 2 3 4]
 [5 6 7 8]]
Dimension: 2

3D Array

Just like with 2D arrays, the inner lists in a 3D array must contain the same number of data elements; otherwise, it will result in an error.

import numpy as np

ar3 = np.array([[[1,2,3],[4,5,6],[7,8,9]]])
print(ar3)
print(f"Dimension: {ar3.ndim}")

Output

[[[1 2 3]
  [4 5 6]
  [7 8 9]]]
Dimension: 3

Create Higher Dimensional Array

To create higher-dimensional arrays, such as 4D or 5D arrays, you need to specify the ndmin value inside the array() function.

import numpy as np

ar_n = np.array([1,2,3,4,5], ndmin=5) # Create 5D array
print(ar_n)
print(f"Dimension: {ar_n.ndim}")

Output

[[[[[1 2 3 4 5]]]]]
Dimension: 5

This is all about the NumPy introduction. Hope this post adds some good value to your life – thank you.