NumPy入门指南：Python中数值计算的核心

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"《NumPy指南》由Travis E. Oliphant博士编写，于2006年12月7日发布，采用市场决定、临时分发限制(MDTDR)系统管理，直至2010年10月31日。该书是关于NumPy的全面教程，旨在帮助读者理解和掌握Python中的数值编程。NumPy是Python中一个重要的科学计算库，它提供了一个强大的N维数组对象以及相关的数学函数，使得处理大量数值数据变得更加高效。在本书的第一部分，"NumPy从Python开始"，作者追溯了NumPy的起源，讲述了它是如何作为Python语言的一个补充，解决早期Python在数值计算上的局限性。这部分可能包括Python标准库在处理大规模数组时的不足，以及NumPy引入的原因和背景。接着，"对象本质"章节涵盖了关键概念。2.1节介绍了数据类型描述符，这些描述符用于定义和识别数组中的数据类型，如整数、浮点数或复数，这对于正确地操作和理解数组数据至关重要。这部分内容可能涵盖了如何查看和转换数组的数据类型，以及不同类型之间的运算规则。 2.2节涉及基础索引和切片，这是NumPy数组操作的基础。读者可以学习如何访问和修改数组的不同部分，以及如何通过索引来实现高效的矩阵和数组操作。这部分可能会详细介绍布尔索引、步长和切片语法的使用。 2.3节可能深入到更复杂的数组操作，如广播规则、数组函数和迭代，这些都是NumPy高效处理数据的重要手段。此外，还可能讨论了数组对象的属性和方法，如形状、维度和复制等。后续章节可能涵盖高级主题，如向量化计算、线性代数支持（如矩阵乘法、求逆和特征值）、随机数生成以及与其他Python科学库（如SciPy）的集成。此外，书中还会讲解如何在实际项目中有效地利用NumPy进行数据分析、机器学习和科学计算。《NumPy指南》是一本详尽的教程，不仅适合初学者快速入门，也适合已有一定Python基础的开发者深化对NumPy的理解和应用。通过阅读这本书，读者将能掌握如何利用NumPy优化Python编程在数值计算领域的性能，并且了解其在现代数据科学和工程中的核心地位。"

to se e what could be done to add the missing features to make SciPy work with

it as a core array object. After a couple of days of studying numarray, I was not

enthusiastic about this approach. My familiarity with the Numeric code base no

doubt biased my opinion, but it seemed to me that the features of Numarray could

be added back to Numeric with a few fundamental changes to the core object. This

would make the transition of SciPy to a more enhanced array object much easier

in my mind.

Therefore, I began to construct this hybrid array object complete with an en-

hanced set of universal (broadcasting) functions that could deal with it. Along the

way, quite a few new features and s igniﬁcant enhancements were added to the arr ay

object and its surrounding infrastructure. This book desc ribes the result of that

year-and-a-half-long eﬀort which culminated with the release of NumPy 0.9.2 in

early 2006 and NumPy 1.0 in late 2006. I ﬁrst named the new package , SciPy Core,

and used the sc ipy namespace. However, after a few months of testing under that

name, it became clear that a separate namespace was nee de d for the new package.

As a result, a rapid search for a new name resulted in actually coming back to the

NumPy name which was the unoﬃcial name of Numerical Python but never the

actual namespace. Because the new package builds on the code-base of and is a

successor to Numeric, I think the NumPy name is ﬁtting a nd hopefully not too

confusing to new users.

This book only brieﬂy outlines some of the infrastructure that sur rounds the

basic objects in NumPy to provide the a dditional functionality co ntained in the older

Numeric package (i.e. LinearAlgebra, RandomArray, FFT). This infrastructure in

NumPy includes basic linear algebra routines, Fourier transform capabilities, and

random number genera tors. In a ddition, the f2py module is described in its own

documentation, and so is only brieﬂy mentioned in the second part of the book.

There are also extensions to the standard P ython distutils and testing frameworks

included with NumPy that are useful in constructing your own packages built on top

of NumPy. The central purpose of this book , however, is to describe and do c ument

the basic NumPy system that is available under the numpy namespace.

NOTE

The numpy namespace includes all names under the numpy.core

and numpy.lib namespaces as well. Thus, import num py will

also import the names fro m numpy.core and numpy.lib. This is the

recommended way to use numpy.

header ...

ndarray

scalar

array

head

data−type

Figure 2.1: Conceptual diagram showing the relationship between the three fun-

damental objects use d to describe the data in an array: 1) the ndarray itself, 2 )

the data-type object that describe s the layout of a single ﬁxed-size element of the

array, 3) the array-scalar Python object that is returned when a single element of

the array is accessed.

2.1 Data-Type Descriptors

In NumPy, an ndarray is an N -dimensio nal array of items where each item takes

up a ﬁxed number of bytes. Typically, this ﬁxed number of bytes represents a

number (e.g. integer or ﬂoating-point). However, this ﬁxed number of bytes could

also repres e nt an arbitrary record made up of any collection of other data types .

NumPy achieves this ﬂexibility through the use of a data-type (dtype) object. E very

array has an associated dtype object which describes the layout of the array data.

Every dtype object, in turn, has an associated Python type-object that deter mines

exactly what type of Python object is returned when an element of the array is

accessed. The dtyp e objects are ﬂexible enoug h to contain references to arrays

of other dtype objects and, therefore, can be used to deﬁne nested records. This

advanced functionality will be described in better detail later as it is mainly useful

for the reca rray (rec ord array) subclass that will also be deﬁned later. However, all

ndarrays can enjoy the ﬂexibility provided by the dtype objects. Figure

2.1 provides

a conceptual diagram showing the relationship betwe e n the ndarray, its associated

data-type object, and an arr ay-scalar that is returned when a single-e le ment of the

array is accessed. Note that the data-type points to the type-object of the array

scalar. An array scalar is returned using the type -object and a particular element

of the ndarray.

Every dtype object is bas e d on one of 21 built-in dtype objects. These built-

in objects allow numeric operations on a wide-variety of integer, ﬂoating-po int,

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