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SciPy is an open-source library for mathematics, science, and engineering. It builds on NumPy and provides additional functionality for optimization, signal and image processing, linear algebra, statistical analysis, and more. This tutorial will give you an introduction to some of the key features of the SciPy library, along with code examples.
Before you start using SciPy, make sure you have it installed. You can install SciPy using pip:
Let's explore some common use cases of SciPy with code examples:
SciPy provides a comprehensive set of functions for linear algebra operations. Let's solve a system of linear equations:
SciPy includes tools for optimization, such as finding the minimum or maximum of a function. Here's an example using the minimize function:
SciPy provides functions for signal processing, including filtering and spectral analysis. Let's apply a simple low-pass filter to a noisy signal:
SciPy provides a wide range of statistical functions. Let's calculate the mean, standard deviation, and perform a t-test:
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