NumPy Tutorial

NumPy (Numerical Python) is an extension library for the Python language that supports a large number of dimensional arrays and matrix operations, and also provides a large number of mathematical function libraries for array operations.

NumPy's predecessor, Numeric, was originally developed by Jim Hugunin and other collaborators. In 2005, Travis Oliphant combined the features of Numarray, another library of similar nature, into Numeric and added other extensions to develop NumPy. NumPy is open source and maintained and developed by many collaborators.

NumPy is a very fast mathematical library, mainly used for array calculations, including:

  • A powerful N-dimensional array object ndarray
  • Broadcasting functions
  • Tools for integrating C/C++/Fortran code
  • Features such as linear algebra, Fourier transform, and random number generation

What you need to know before learning this tutorial

Before starting to learn the NumPy tutorial, we need to have basic Python knowledge. If you are not familiar with Python, you can read our tutorial:

NumPy Applications

NumPy is usually used together with SciPy (Scientific Python) and Matplotlib (plotting library). This combination is widely used as a replacement for MatLab, and is a powerful scientific computing environment that helps us learn data science or machine learning through Python.

SciPy is an open-source Python algorithm library and mathematical toolkit.

The modules included in SciPy cover optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal processing and image processing, ordinary differential equation solving, and other commonly used computations in science and engineering.

Matplotlib is a visualization interface for the Python programming language and its numerical mathematics extension package NumPy. It provides an application programming interface (API) for embedding plots into applications using common GUI toolkits such as Tkinter, wxPython, Qt, or GTK+.

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