Python Data Science 简明教程
Python Data Science - SciPy
What is SciPy?
Python 的 SciPy 库是专为与 NumPy 阵列配合使用而设计的,提供了许多用户友好且高效的数值处理方法,例如用于数值积分和优化的方法。它们一起运行在所有流行的操作系统上,安装速度快,且免费。NumPy 和 SciPy 易于使用,但功能强大到足以让世界领先的科学家和工程师依赖它们。
The SciPy library of Python is built to work with NumPy arrays and provides many user-friendly and efficient numerical practices such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install and are free of charge. NumPy and SciPy are easy to use, but powerful enough to depend on by some of the world’s leading scientists and engineers.
SciPy Sub-packages
SciPy 被组织成涵盖不同科学计算领域的子包。它们总结在以下表中 −
SciPy is organized into sub-packages covering different scientific computing domains. These are summarized in the following table −
Physical and mathematical constants |
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Fourier transform |
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Integration routines |
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Interpolation |
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Data input and output |
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Linear algebra routines |
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Optimization |
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Signal processing |
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Sparse matrices |
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Spatial data structures and algorithms |
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Any special mathematical functions |
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Statistics |
Data Structure
SciPy 使用的多维数组是 NumPy 模块提供的一个基本数据结构。NumPy 为线性代数、傅里叶变换和随机数字生成提供了一些功能,但不如 SciPy 中同等功能那样通用。
The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for Linear Algebra, Fourier Transforms and Random Number Generation, but not with the generality of the equivalent functions in SciPy.
我们将在接下来的几章中看到很多关于在数据科学工作中使用 SciPy python 库的示例。
We will see lots of examples on using SciPy library of python in Data science work in the next chapters.