SciPy Tutorial
SciPy is an open-source Python algorithm library and mathematical toolkit.
SciPy is a scientific computing library based on NumPy, used in fields such as mathematics, science, and engineering. Many higher-level abstractions and physical models require SciPy.
The modules included in SciPy cover optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal and image processing, ordinary differential equation solving, and other commonly used computations in science and engineering.
What you need to know before learning this tutorial
Before starting to learn the SciPy tutorial, you need to have a basic foundation in Python. If you are not yet familiar with Python, you can read our tutorial:
SciPy Applications
SciPy is a commonly used software package for mathematics, science, and engineering. It can handle optimization, linear algebra, integration, interpolation, curve fitting, special functions, fast Fourier transform, signal processing, image processing, ordinary differential equation solvers, and more.
The modules included in SciPy cover optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal and image processing, ordinary differential equation solving, and other commonly used computations in science and engineering.
NumPy and SciPy working together can efficiently solve many problems and have been widely applied in multiple disciplines such as astronomy, biology, meteorology and climate science, as well as materials science.
Related Links
- NumPy Official Websitehttp://www.numpy.org/
- NumPy Source Code:https://github.com/numpy/numpy
- SciPy Official Website:https://www.scipy.org/
- SciPy Source Code:https://github.com/scipy/scipy
- Matplotlib Official Website:https://matplotlib.org/
- Matplotlib Source Code:https://github.com/matplotlib/matplotlib