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NumPy is a Python library for handling complex mathematical operations, data analytics, and machine learning. It is an open-source project that is easily accessible free of cost. In this blog post, we will explore the in-depth understanding of the Python NumPy Array package library. We will also look into the steps to get started with NumPy in Python and the steps to create an application-based introduction to NumPy arrays.

What is NumPy?

NumPy or Numerical Python, is an open-source project Python library for working with arrays. It possesses functions for working with linear algebra, fourier transforms, and matrices. Travis Oliphant created the Python NumPy array in 2005 to include functions for handling tasks. You can access the complete source code on GitHub and refer to this blog post for the best NumPy tutorial.

Why use NumPy?

Python consists of lists that serve the purpose of arrays. But, lists are slower to process. NumPy focuses on providing array object that is almost 50x faster than the default Python lists. NumPy provides an array object called ndarray. The supporting functions provided by NumPy make working with ndarray preferable over traditional Python lists. Arrays are continually used in the domains like Data Science and Machine Learning due to the importance of their requirement for speed and resources.

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Advantages of NumPy

NumPy is one of the most used Python libraries in Data Science. It uses less memory and storage space, which is a significant advantage. There are several other advantages of the NumPy package, which are as follows:

  • Efficient Array Operations: NumPy offers highly effective array operations, making numerical computation faster and memory efficient.
  • Multi-Dimensional Arrays: NumPy supports multi-dimensional arrays, simplifying tasks that require matrices, images, and higher dimensional data structures.
  • Reduced Dependency on Loops: The Python NumPy helps alter dependency on loops, preventing the programmers from getting jumbled in the nest of iteration indices.
  • Cleaner code: When dealing with complex, mathematical, or numerical operations, a code without a loop will look like the complex equation that you intend to calculate.
  • Interoperability:NumPy effortlessly integrates with other Python libraries, such as SciPy, Pandas, and Matplotlib, improving its utility for data analysis and visualization.
  • Mathematical Functions:The Python NumPy library possesses various mathematical functions, including basic arithmetic, linear algebra, and statistics.
  • Extensive Community: The NumPy has a large and active community of users and contributors that help ensure continuous development and support.

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Python with NumPy: Prerequisites & Tech Stack

When you get started with the NumPy tutorial in Python, you must consider several prerequisites. These few aspects are crucial to better understanding the NumPy arrays in Python, starting with the Python in NumPy.

  • Python3 Programming
  • Virtual environments
  • Conda/pip
  • Google Colaboratory/Visual Studio Code Editor

To start with this Python NumPy tutorial, ensure you have the following.

  • Python
  • IDE (Google Colaboratory or Visual Studio Code)

We will be using the following technologies while moving ahead with the tutorial.

  • NumPy
  • Google Colaboratory/Visual Studio Code

There are various other use cases applications of NumPy; here is the system setup.

  • Ubuntu 20.04 OS
  • Python 3.8+

Install NumPy Using pip

Install the NnumPy package using pip (it is recommended to do so inside a virtual environment to avoid polluting your OS).

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pip install numpy

Create New Directory

Create a new directory and navigate to it using the below commands.

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mkdir mynumpyproject
cd mynumpyproject

While the above steps should be all that you need to get started with NumPy Python, there are a couple more tools that you may optionally install to make working with data science libraries more developer-friendly. These tools can be any of the following.

  • IPython
  • Jupyter Notebook
  • VS Code
  • Google Colaboratory

We will use Python notebooks in the VS Code editor for the demonstration.

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Python with NumPy: Getting Started with NumPy

Let’s start by using the basic data type offered by numPy, a numpy array. To do that, let’s import the numPy package.

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import numpy
my_array = numpy.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])


Getting Started with NumPy

Now, for convenience, numPy is usually imported with “np” as its alias.

*Note: Alias is an alternate name given to anything.

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import numpy as np
my_array = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])


imported with “np”

Application-based Introduction to NumPy Arrays

Moving further in Python with NumPy tutorial, let’s start our core application using NumPy arrays.

Create NumPy Array

The major reason for using NumPy in data science and machine learning applications is the need to work efficiently with arrays. Numpy provides array objects called “ndarray”.

A numpy function “array()” can be used to create an array.

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import numpy as np
blank_array = np.array([])
my_array = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])


Create NumPy Array

*Note: A numpy array expects an object to be passed with the “array()” function. It returns a syntax error if no object type argument is passed with the function.

The “array()” function can be used by passing any array-like object into its argument like a list, tuple, etc.

Dimensions in Array

Arrays can be categorized according to their dimensions.

0-D Arrays
0-D arrays can also be called scalars. Every element of an array is itself a 0-D array.

1-D Arrays
1-D arrays can be considered an array consisting of multiple 0-D arrays as their elements. 1-D arrays are also called uni-dimensional arrays, the most basic and commonly used arrays.

2-D Arrays
2-D arrays consist of 1-D arrays as its elements and are often used to represent matrices.

3-D Arrays
3-D arrays are the arrays having matrices (2-D arrays) as its elements.

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import numpy as np
zero_d_array = np.array(23)
print("0-D ARRAY", zero_d_array)
one_d_array = np.array([0, 1, 2, 3])
print("1-D ARRAY", one_d_array)
two_d_array = np.array([[0, 1, 2], [9, 8, 7]])
print("2-D ARRAY",two_d_array)
three_d_array = np.array([[[0, 1, 2], [9, 8, 7]], [[0, 1, 2], [9, 8, 7]]])
print("3-D ARRAY",three_d_array)


Dimensions in Array

Higher Dimensional Arrays

Arrays in Numpy are not limited to having only three dimensions. An array can have any number of dimensions and can be created using the “ndmin” argument to define the number of arguments in the array.

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import numpy as np
my_array = np.array([0, 1, 2, 3, 4, 5, 6], ndmin=4)
print('no. of array dimensions :', my_array.ndim)


Higher Dimensional Arrays

Here, with the “ndmin” argument, it can be seen that it supports arrays with any number of dimensions. In the above case, the 4th dimension, the innermost dimension, has seven elements.

Array Indexing in NumPy

The next section in the Python with NumPy Tutorial is about array indexing in NumPy. Array elements can be accessed using array indexing. Any array element can be accessed by using its index number. Also, it should be kept in mind that, like any other arrays, numPy arrays also have indices starting with 0. Therefore, the first element will always have an index 0, and the second element will have an index 1, etc.

Elements can be accessed using array indexing for various purposes, such as accessing the elements, using the elements for various mathematical operations, etc.

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import numpy as np
one_d_array = np.array([9, 8, 7, 6])
print("First element: ",one_d_array[0])
print("Second element: ",one_d_array[1])
print("Addition of first 2 elements: ",one_d_array[0] + one_d_array[1])
two_d_array = np.array([[8,7,6,5], [4,3,2,1]])
print("3rd element of 1st row: ", two_d_array[0,2])
print("4th element of 2nd row: ", two_d_array[1,3])


Array Indexing in NumPy

Access Elements in 3-D Array or Other Higher Dimensional Arrays

It becomes a little complex when it comes to accessing elements in 3-D arrays or other higher dimensional arrays. One should make sure to understand the structure or the dimensions of the arrays before manipulating them. Take the example of 3-D arrays here:

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import numpy as np
three_d_array = np.array([[[9, 8, 7], [0, 1, 2]], [[7, 8, 9], [5, 4, 3]]])
print(three_d_array[1, 0, 2])


Access Elements in 3-D Array

Let’s try to understand the last line in this piece of code.

three_d_array[1, 0, 2] is printing the value 9.

The reason for the same is that the first number represents the first dimension that contains two arrays:
[[9, 8, 7], [0, 1, 2]] and [[7, 8, 9], [5, 4, 3]]

Since we have selected index 1, we are left with the second array:
[[7, 8, 9], [5, 4, 3]]

The second number represents the second dimension, which also contains two arrays:
[7, 8, 9] and [5, 4, 3]

Since we selected index 0, we are left with the first array at index 0:
[7, 8, 9]

Now, the third number represents the third dimension, which contains these three values:
7, 8, and 9

Since we selected index 2, we end up with the final value:

Negative Indexing

Negative indices can also be used to access array elements from the end.

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import numpy as np
my_array = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print('Last element from 2nd dimension: ', my_array[1, -2])


Negative Indexing

Here, it can be seen that the output is element 9.

The reason for this is that the statement “my_array[1, -2]” accesses the second array of the dimension using the first index value set to 1, that is
[6, 7, 8, 9, 10]

And then, the second index value set to -2 accesses the element at the second index from the end.


This is all that we have on the Python NumPy Arrays tutorial. We hope that this improves your understanding of this Python library. However, if you are a business owner and are still confused about NumPy Python, you can contact a leading Python Development Company like Bacancy and hand over your development hiccups to experts with extensive knowledge of working clients across the globe.

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