Python科学计算入门指南

科学计算

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"Introduction to Python for Science - Release 0.9.23 by David Pine" 本文档是一份针对科学计算的Python编程入门指南，适合初学者，旨在从基础开始教你如何编写计算机程序。文档涵盖了Python语言的基础知识及其在科学研究中的应用。首先，文档介绍了Python及其在科学计算中的作用。Python是一种强大的、易于学习的编程语言，尤其适合于数据分析和科学计算，因为它有丰富的库支持，如NumPy和SciPy。接下来，文档详细阐述了如何启动Python。包括在个人电脑上安装Python的步骤，以及使用Canopy这款集成开发环境（IDE）的界面，特别是Interactive Python Pane，它是一个交互式的Python控制台，可以作为计算器使用。此外，还介绍了Python模块的概念，它们是组织代码的单元，以及如何导入和使用模块。文档进一步讲解了Python的基础语法，如函数的初步认识，变量的使用，以及脚本文件和程序的创建。通过变量，你可以存储和操作数据；函数则允许你封装可重用的代码块。了解如何编写和调用函数是Python编程的关键。接着，文档深入到数据结构，包括字符串、列表、数组和字典。字符串用于处理文本数据，列表则是一种灵活的数据集合，可以包含不同类型的元素。NumPy数组提供高效的数值计算功能，是进行科学计算的核心工具。字典是一种关联数据的结构，通过键值对进行访问。在输入和输出部分，文档介绍了如何获取键盘输入、屏幕输出，以及文件的读写操作。这些技能对于任何程序来说都是至关重要的，因为它们允许程序与用户交互并保存或加载数据。此外，文档还涉及了绘图，使用Python的matplotlib库进行基本和高级的图形绘制，包括线性图、对数图以及更复杂的图形输出。这对于可视化数据和结果非常有用。最后，文档讲解了条件语句和循环，这是控制程序流程的关键构造，使得程序可以根据特定条件执行不同的操作，或者重复执行某些任务。每个章节末尾都包含练习题，帮助读者巩固所学知识，并鼓励他们实际操作以加深理解。通过这个指南，读者将能够逐步掌握Python编程，为科学计算打下坚实基础。

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Introduction to Python for Science, Release 0.9.23

2.3.3 Tab completion

IPython also incorporates a number of shortcuts that make using the shell more efﬁcient.

One of the most useful is tab completion. Let’s assume you have been following along

and that your are in the directory Documents or My Documents. To switch to the

directory PyProgs, you could type cd PyProgs. Instead of doing that, type cd PyP

and then press the TAB key. This will complete the command, provided there is no ambi-

guity in how to ﬁnish the command. In the present case, that would mean that there was

no other subdirectory beginning with PyP. Tab completion works with any command you

type into the IPython terminal. Try it out! It will make your life more wonderful.

A related shortcut involves the ↑ key. If you type a command, say cd and then to press

the ↑ key, IPython will complete the cd command with the last instance of that command.

Thus, when you launch IPython, you can use this shortcut to take you to the directory you

used when you last ran IPython.

You can also simply press the ↑ key, which will simply recall the most recent command.

Repeated application of the ↑ key scrolls though the most recent commands in reverse

order. The ↓ key can be used to scroll in the other direction.

2.3.4 Recap of commands

Let’s recap the (magic) commands introduced above:

pwd: (print working directory) Prints the path of the current directory.

ls: (list) Lists the names of the ﬁles and directories located in the current

directory.

mkdir ﬁlename: (make directory) Makes a new directory ﬁlename.

cd directoryname: (change directory) Changes the current directory to di-

rectoryname. Note: for this to work, directoryname must be a sub-

directory in the current directory. Typing cd ~ changes to the home

directory of your computer. Typing cd .. moves the console one

directory up in the directory tree.

clear: Clears the IPython screen of previous commands.

run ﬁlename: Runs (executes) a Python script. Described later in the sec-

tion Scripting Example 1

Tab completion: Provides convenient shortcuts, with or without the arrow

keys, for executing commands in the IPython shell.

10 Chapter 2. Launching Python

Introduction to Python for Science, Release 0.9.23

complex number. Python always promotes the result to the most complex of the inputs.

2.4.3 Important note on integer division in Python

One peculiarity of all versions of Python prior to version 3 is that dividing two integers

by each other yields the “ﬂoor division” result—another integer. Therefore 3/2 yields 1

whereas 3./2 or 3/2. or 3./2. all yield 1.5. Starting with version 3 of Python, all of

the above expressions, including 3/2 yield 1.5. Unfortunately, we are using version 2.7

of Python so 3/2 yields 1. You can force versions of Python prior to version 3 to divide

integers like version 3 does by typing from __future__ import division at

the beginning of an IPython session. You only need to type it once and it works for the

entire session.

2.5 Python Modules

The Python computer language consists of a “core” language plus a vast collection of

supplementary software that is contained in modules. Many of these modules come with

the standard Python distribution and provide added functionality for performing computer

system tasks. Other modules provide more specialized capabilities that not every user may

want. You can think of these modules as a kind of library from which you can borrow

according to your needs.

We will need three Python modules that are not part of the core Python distribution, but

are nevertheless widely used for scientiﬁc computing. The three modules are

NumPy is the standard Python package for scientiﬁc computing with

Python. It provides the all-important array data structure, which

is at the very heart of NumPy. In also provides tools for creating

and manipulating arrays, including indexing and sorting, as well as

basic logical operations and element-by-element arithmetic operations

like addition, subtraction, multiplication, division, and exponentiation.

It includes the basic mathematical functions of trigonometry, expo-

nentials, and logarithms, as well vast collection of special functions

(Bessel functions, etc.), statistical functions, and random number gen-

erators. It also includes a large number of linear algebra routines

that overlap with those in SciPy, although the SciPy routines tend to

be more complete. You can ﬁnd more information about NumPy at

http://docs.scipy.org/doc/numpy/reference/index.html.

14 Chapter 2. Launching Python

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