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# NumPy Tutorial !(#) NumPy (Numerical Python) is an extension library for the Python language, supporting large-dimensional array and matrix operations. It also provides a rich library of mathematical functions for array operations. The predecessor of NumPy, Numeric, was originally developed by Jim Hugunin and other collaborators. In 2005, Travis Oliphant combined features from another similar library, Numarray, into Numeric and added other extensions to create NumPy. NumPy is open-source and maintained by many contributors. NumPy is a very fast mathematical library, primarily used for array calculations, and includes: * A powerful N-dimensional array object, `ndarray` * Broadcasting functions * Tools for integrating C/C++/Fortran code * Functions for linear algebra, Fourier transforms, random number generation, and more * * * ## What You Need to Know Before Starting This Tutorial Before starting the NumPy tutorial, we need to have a basic understanding of Python. If you are not familiar with Python, you can read our tutorials: * * * ## NumPy Applications NumPy is often used in conjunction with SciPy (Scientific Python) and Matplotlib (a plotting library). This combination is widely used as a replacement for MatLab, providing 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. SciPy includes modules for optimization, linear algebra, integration, interpolation, special functions, Fast Fourier Transform, signal and image processing, ordinary differential equation solvers, and other common computations in science and engineering. Matplotlib is a visualization interface for the Python programming language and its numerical mathematics extension, NumPy. It provides an application programming interface (API) for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. * * * ## Related Links * NumPy Official Website [http://www.numpy.org/](http://www.numpy.org/) * NumPy Source Code: [https://github.com/numpy/numpy](https://github.com/numpy/numpy) * SciPy Official Website: [https://www.scipy.org/](https://www.scipy.org/) * SciPy Source Code: [https://github.com/scipy/scipy](https://github.com/scipy/scipy) * Matplotlib Tutorial: (#) * Matplotlib Official Website: [https://matplotlib.org/](https://matplotlib.org/) * Matplotlib Source Code: [https://github.com/matplotlib/matplotlib](https://github.com/matplotlib/matplotlib)