Python NumPy Tutorial

Our Python NumPy Tutorial provides the basic and advanced concepts of the NumPy. Our NumPy tutorial is designed for beginners and professionals.

NumPy stands for numeric python which is a python package for the computation and processing of the multidimensional and single dimensional array elements.

What is NumPy?

NumPy stands for numeric python which is a python package for the computation and processing of the multidimensional and single dimensional array elements.

History of NumPy

Travis Oliphant created NumPy package in 2005 by injecting the features of the ancestor module Numeric into another module Numarray.

It is an extension module of Python which is mostly written in C. It provides various functions which are capable of performing the numeric computations with a high speed.

NumPy provides various powerful data structures, implementing multi-dimensional arrays and matrices. These data structures are used for the optimal computations regarding arrays and matrices.

In this tutorial, we will go through the numeric python library NumPy.

Need of NumPy

NumPy provides a convenient and efficient way to handle the vast amount of data. NumPy is also very convenient with Matrix multiplication and data reshaping. NumPy is fast which makes it reasonable to work with a large set of data.

Advantages of NumPy

There are the following advantages of using NumPy for data analysis.

1. NumPy performs array-oriented computing.
2. It efficiently implements the multidimensional arrays.
3. It performs scientific computations.
4. It is capable of performing Fourier Transform and reshaping the data stored in multidimensional arrays.
5. NumPy provides the in-built functions for linear algebra and random number generation.

Nowadays, NumPy in combination with SciPy and Mat-plotlib is used as the replacement to MATLAB as Python is more complete and easier programming language than MATLAB.

NumPy Applications

There are various applications of NumPy as it is used in almost every field where numbers are involved. Let’s learn about them:

• Data Science and Machine Learning

NumPy is popularly and widely used in Data Science in the process of utilizing various techniques and algorithms to find and extract information and conclusions. NumPy arrays are internally used in libraries like Pandas, SciPy, Scikit-Learn, and Machine learning libraries like TensorFlow and PyTorch.

• Numerical Computing & Data Analysis

NumPy is used in Data analysis for examining, processing, and interpreting the raw data into useful insights that can support decision-making. It includes arranging and translating data into an understandable format, like displaying data using graphs and charts, and applying statistical techniques to spot patterns and linkages.

• Image and Signal Processing

NumPy is also used in Image and Signal Processing. Arrays are used as a representation for images and signals. It provides efficient array operations like slicing, which helps in constructing the base for various tasks like image enhancement and filtering. It uses libraries like Scipy, OpenCV, and Scikit-image, which help in performing image and signal filtering.

• Data Visualization

NumPy is also used in data visualization, which includes presenting the results clearly and in an understandable way. This process involves making charts, graphs, and other visual representations of the data. Libraries like as Matplotlib and Seaborn use NumPy arrays as their primary data structure for plotting and visualizing data. 

Why Learn NumPy?

There are multiple reasons to learn NumPy, which could help enhance our productivity and performance. Let’s learn about some of them:

• Performance

NumPy is known to be highly optimized for performance-related tasks. The NumPy arrays are so much faster than the standard Python Libraries. 

• Convenience

NumPy provides a lot of convenience because it provides a wide range of functions for performing various types of numerical operations. It makes complex mathematics quite simple and easier to perform.

• Ecosystem

We have an entire ecosystem where we can use NumPy with other libraries such as SciPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. This allows us to use NumPy tools much more effectively.

• Memory Efficiency

NumPy is known to be memory efficient as Arrays store the homogeneous data in memory blocks. Because of this, it also provides a faster and more efficient way to handle large datasets.

• Vectorization

NumPy provides a large collection of high-level mathematical functions, which include linear algebra, Fourier transforms, and statistical operations. We can apply these mathematical functions to entire arrays with minimal code, which is known as Vectorization.

Getting Started with NumPy

Here, we are going to started Numpy.

Example: Creating a NumPy Array

In the example below, we are creating a NumPy array from a Python list.

Output:

Hello World!

Prerequisite

Before learning Python Numpy, you must have the basic knowledge of Python concepts.

Audience

Our Numpy tutorial is designed to help beginners and professionals.

Problem

We assure you that you will not find any problem in this Python Numpy tutorial. But if there is any mistake, please post the problem in the contact form.

NumPy Source Code Repository & Documentation

The complete source code of NumPy is available on its official GitHub repository:  NumPy Codebase, and its official documentation can be found here: NumPy documentation