Introduction to NumPy

What is NumPy?

This is an introduction to NumPy tutorial.

If you are not familiar with Python, please refer here. We use Jupyter Notebook within this series of tutorials.

NumPy, which stands for Numerical Python, is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is the foundation upon which many other Python scientific computing libraries are operate, such as SciPy, pandas, and scikit-learn.

Why use NumPy?

NumPy is a fundamental tool for numerical computing in Python, offering efficiency, flexibility, and a wide range of mathematical capabilities for scientific and engineering applications. Let’s explore some of the major reasons why use NumPy for numerical computing in Python.

Efficiency

NumPy operations are implemented in highly optimised C and Fortran code, making them much faster than equivalent Python operations using lists. This efficiency is crucial when working with large datasets or performing complex mathematical computations.

Multi-dimensional arrays

NumPy provides powerful data structures for representing multi-dimensional arrays (ndarrays). These arrays allow for efficient storage and manipulation of large datasets, making them ideal for scientific computing tasks.

Broad range of mathematical functions

NumPy includes a comprehensive collection of mathematical functions for array manipulation, linear algebra, Fourier analysis, random number generation, and more. These functions are optimized for performance and provide a convenient way to perform complex calculations.

Broadcasting

NumPy’s broadcasting capability allows for efficient computation on arrays with different shapes and sizes. This feature eliminates the need for explicit looping over array elements, resulting in cleaner and more concise code.

Integration with other libraries

NumPy seamlessly integrates with other Python libraries for scientific computing, such as SciPy, pandas, matplotlib, and scikit-learn. This integration enables a rich ecosystem of tools and resources for data analysis, visualization, machine learning, and more.

Cross-platform compatibility

NumPy is open-source software and runs on multiple platforms, including Windows, macOS, and Linux. This cross-platform compatibility makes it accessible to a wide range of users and environments.

How to install NumPy?

To install NumPy, use Python package managers such as pip or conda. The following is a simple guide of how to install NumPy using both methods:

Using pip

pip install numpy

Using conda

conda install numpy

If you’re using a virtual environment, make sure to activate it before installing NumPy to ensure that the package is installed within the correct environment.

After installation, you can verify that NumPy’s installation is correct by importing it in a Python script or an interactive Python session: