Python编程指南：从入门到实践

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"PythonGuideDocumentation，由KennethReitz编写，发布于2013年10月27日，是一个关于Python编程的指南，旨在帮助用户从入门到精通Python编程，涵盖选择解释器、代码结构、项目管理、测试、常见陷阱、场景应用等多个方面，并涉及代码打包与开发环境的构建。" 《PythonGuide》是一份详尽的Python学习资源，它以清晰的结构和实用的建议指导新手和经验丰富的开发者更好地理解和使用Python。以下是对文档内容的详细解读： 1. **开始编程** - **选择解释器**: Python有多个版本，如Python 2和Python 3，文档推荐使用最新且被广泛支持的Python 3版本。 - **在MacOSX上安装Python**: 提供了安装Python的步骤，包括使用Homebrew或其他包管理器。 - **在Windows上安装Python**: 强调了使用官方Python网站下载安装程序并考虑添加Python到系统路径的重要性。 - **在Linux上安装Python**: 指导用户通过包管理器（如apt或yum）安装Python。 2. **编写优质代码** - **项目结构**: 讨论如何组织代码文件和目录，遵循PEP8等最佳实践。 - **代码风格**: 详细介绍了Python的编码规范，如命名约定、缩进和注释的使用。 - **阅读优秀代码**: 鼓励学习其他开源项目的代码来提升理解能力。 - **文档编写**: 强调了良好的文档对项目的重要性，推荐使用 Sphinx 等工具自动生成文档。 - **测试代码**: 推荐使用单元测试框架如unittest，以及持续集成工具确保代码质量。 - **常见陷阱**: 提醒开发者注意Python中的陷阱，如缩进错误、类型检查等。 - **选择许可证**: 解释了不同类型的开源软件许可证，如MIT、Apache 2.0等，帮助用户为自己的项目选择合适的许可证。 3. **场景应用指南** - **网络应用**: 包括网络编程的基础和高级概念，如HTTP请求和套接字编程。 - **Web应用**: 涉及Web框架（如Django和Flask）的使用，以及Web服务的开发。 - **HTML抓取**: 教授如何使用BeautifulSoup等库进行网页数据提取。 - **命令行应用**: 创建具有交互式界面的CLI工具的方法。 - **图形用户界面**: 讨论使用Tkinter等库创建GUI应用。 - **数据库操作**: 如何连接和查询SQL数据库，如SQLite和MySQL。 - **网络编程**: 网络连接、并发和套接字编程的深入探讨。 - **系统管理**: 使用Python进行自动化运维任务的技巧。 - **持续集成**: CI/CD工具如Jenkins的使用，确保代码质量。 - **性能优化**: 介绍如何分析和提升Python代码的运行速度。 - **科学计算**: 探讨NumPy、SciPy等库在数值计算和数据分析中的应用。 - **图像处理**: 使用PIL或OpenCV等库进行图像处理和分析。 - **XML解析**: 学习如何使用lxml或ElementTree库解析和操作XML数据。 4. **打包和发布代码** - **打包代码**: 如何将项目打包成可分发的Python包，使用setuptools和distutils。 - **冻结代码**: 将Python应用程序打包为可执行文件，以便在没有Python环境的机器上运行。 5. **开发环境** - **开发环境设置**: 建议使用好的文本编辑器或IDE，如VS Code或PyCharm。 - **虚拟环境**: 使用venv或virtualenv创建隔离的Python环境，避免依赖冲突。 6. **附加笔记** - **社区介绍**: 提到了Python社区的重要性，鼓励参与讨论和贡献。 - **学习Python**: 推荐了资源和途径，如在线教程、书籍和课程。 - **索引**: 完整的文档索引方便快速查找信息。这份指南不仅适合初学者，也适用于有一定经验的Python开发者，通过它，你可以全面地了解Python编程，从基础到高级，从理论到实践，一步步提升自己的技能。

Python Guide Documentation, Release 0.0.1

• Spaghetti code: multiple pages of nested if clauses and for loops with a lot of copy-pasted procedural code

and no proper segmentation are known as spaghetti code. Python’s meaningful indentation (one of its most

controversial features) make it very hard to maintain this kind of code. So the good news is that you might not

see too much of it.

• Ravioli code is more likely in Python: it consists of hundreds of similar little pieces of logic, often classes or

objects, without proper structure. If you never can remember if you have to use FurnitureTable, AssetTable or

Table, or even TableNew for your task at hand, you might be swimming in ravioli code.

2.1.2 Modules

Python modules are one of the main abstraction layers available and probably the most natural one. Abstraction layers

allow separating code into parts holding related data and functionality.

For example, a layer of a project can handle interfacing with user actions, while another would handle low-level

manipulation of data. The most natural way to separate these two layers is to regroup all interfacing functionality in

one ﬁle, and all low-level operations in another ﬁle. In this case, the interface ﬁle needs to import the low-level ﬁle.

This is done with the import and from ... import statements.

As soon as you use import statements you use modules. These can be either built-in modules such as os and sys,

third-party modules you have installed in your environment, or your project’s internal modules.

To keep in line with the style guide, keep module names short, lowercase, and be sure to avoid using special symbols

like the dot (.) or question mark (?). So a ﬁle name like my.spam.py is one you should avoid! Naming this way will

interfere with the way python looks for modules.

In this example python expects to ﬁnd a “spam.py” ﬁle in a folder named “my” which is not the case. There is an

example of how the dot notation should be used in the python docs.

If you’d like you could name it as my_spam.py but even our friend the underscore should not be seen often in module

names.

Aside for some naming restrictions, nothing special is required for a Python ﬁle to be a module, but the import

mechanism needs to be understood in order to use this concept properly and avoid some issues.

Concretely, the import modu statement will look for the proper ﬁle, which is modu.py in the same directory as the

caller if it exists. If it is not found, the Python interpreter will search for modu.py in the “path” recursively and raise

an ImportError exception if it is not found.

Once modu.py is found, the Python interpreter will execute the module in an isolated scope. Any top-level statement

in modu.py will be executed, including other imports if any. Function and class deﬁnitions are stored in the module’s

dictionary.

Then, the module’s variables, functions, and classes will be available to the caller through the module’s namespace, a

central concept in programming that is particularly helpful and powerful in Python.

In many languages, an include file directive is used by the preprocessor to take all code found in the ﬁle and

‘copy’ it into the caller’s code. It is different in Python: the included code is isolated in a module namespace, which

means that you generally don’t have to worry that the included code could have unwanted effects, e.g. override an

existing function with the same name.

It is possible to simulate the more standard behavior by using a special syntax of the import statement: from modu

import

. This is generally considered bad practice. Using ‘‘import *‘‘ makes code harder to read and makes

dependencies less compartmentalized.

Using from modu import func is a way to pinpoint the function you want to import and put it in the global

namespace. While much less harmful than import

because it shows explicitly what is imported in the global

namespace, its advantage over a simpler import modu is only that it will save some typing.

Very bad

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[...]

from modu import

[...]

x = sqrt(4) # Is sqrt part of modu? A builtin? Defined above?

Better

from modu import sqrt

[...]

x = sqrt(4) # sqrt may be part of modu, if not redefined in between

Best

import modu

[...]

x = modu.sqrt(4) # sqrt is visibly part of modu’s namespace

As said in the section about style, readability is one of the main features of Python. Readability means to avoid useless

boilerplate text and clutter, therefore some efforts are spent trying to achieve a certain level of brevity. But terseness

and obscurity are the limits where brevity should stop. Being able to tell immediately where a class or function comes

from, as in the modu.func idiom, greatly improves code readability and understandability in all but the simplest

single ﬁle projects.

2.1.3 Packages

Python provides a very straightforward packaging system, which is simply an extension of the module mechanism to

a directory.

Any directory with an __init__.py ﬁle is considered a Python package. The different modules in the package are

imported in a similar manner as plain modules, but with a special behavior for the __init__.py ﬁle, which is used to

gather all package-wide deﬁnitions.

A ﬁle modu.py in the directory pack/ is imported with the statement import pack.modu. This statement will look for

an __init__.py ﬁle in pack, execute all of its top-level statements. Then it will look for a ﬁle pack/modu.py and execute

all of its top-level statements. After these operations, any variable, function, or class deﬁned in modu.py is available

in the pack.modu namespace.

A commonly seen issue is to add too much code to __init__.py ﬁles. When the project complexity grows, there

may be sub-packages and sub-sub-packages in a deep directory structure, and then, importing a single item from a

sub-sub-package will require executing all __init__.py ﬁles met while traversing the tree.

Leaving an __init__.py ﬁle empty is considered normal and even a good practice, if the package’s modules and sub-

packages do not need to share any code.

Lastly, a convenient syntax is available for importing deeply nested packages: import very.deep.module as mod. This

allows you to use mod in place of the verbose repetition of very.deep.module.

2.1.4 Object-oriented programming

Python is sometimes described as an object-oriented programming language. This can be somewhat misleading and

needs to be clariﬁed.

In Python, everything is an object, and can be handled as such. This is what is meant when we say that, for example,

functions are ﬁrst-class objects. Functions, classes, strings, and even types are objects in Python: like any objects, they

have a type, they can be passed as function arguments, they may have methods and properties. In this understanding,

Python is an object-oriented language.

2.1. Structuring Your Project 11

Python Guide Documentation, Release 0.0.1

However, unlike Java, Python does not impose object-oriented programming as the main programming paradigm. It

is perfectly viable for a Python project to not be object-oriented, i.e. to use no or very few class deﬁnitions, class

inheritance, or any other mechanisms that are speciﬁc to object-oriented programming.

Moreover, as seen in the modules section, the way Python handles modules and namespaces gives the developer a

natural way to ensure the encapsulation and separation of abstraction layers, both being the most common reasons to

use object-orientation. Therefore, Python programmers have more latitude to not use object-orientation, when it is not

required by the business model.

There are some reasons to avoid unnecessary object-orientation. Deﬁning custom classes is useful when we want to

glue together some state and some functionality. The problem, as pointed out by the discussions about functional

programming, comes from the “state” part of the equation.

In some architectures, typically web applications, multiple instances of Python processes are spawned to respond

to external requests that can happen at the same time. In this case, holding some state into instantiated objects,

which means keeping some static information about the world, is prone to concurrency problems or race-conditions.

Sometimes, between the initialization of the state of an object (usually done with the __init__() method) and the

actual use of the object state through one of its methods, the world may have changed, and the retained state may be

outdated. For example, a request may load an item in memory and mark it as read by a user. If another request requires

the deletion of this item at the same time, it may happen that the deletion actually occurs after the ﬁrst process loaded

the item, and then we have to mark as read a deleted object.

This and other issues led to the idea that using stateless functions is a better programming paradigm.

Another way to say the same thing is to suggest using functions and procedures with as few implicit contexts and side-

effects as possible. A function’s implicit context is made up of any of the global variables or items in the persistence

layer that are accessed from within the function. Side-effects are the changes that a function makes to its implicit

context. If a function saves or deletes data in a global variable or in the persistence layer, it is said to have a side-effect.

Carefully isolating functions with context and side-effects from functions with logic (called pure functions) allow the

following beneﬁts:

• Pure functions are deterministic: given a ﬁxed input, the output will always be the same.

• Pure functions are much easier to change or replace if they need to be refactored or optimized.

• Pure functions are easier to test with unit-tests: There is less need for complex context setup and data cleaning

afterwards.

• Pure functions are easier to manipulate, decorate, and pass-around.

In summary, pure functions, without any context or side-effects, are more efﬁcient building blocks than classes and

objects for some architectures.

Obviously, object-orientation is useful and even necessary in many cases, for example when developing graphical

desktop applications or games, where the things that are manipulated (windows, buttons, avatars, vehicles) have a

relatively long life of their own in the computer’s memory.

2.1.5 Decorators

The Python language provides a simple yet powerful syntax called ‘decorators’. A decorator is a function or a class that

wraps (or decorates) a function or a method. The ‘decorated’ function or method will replace the original ‘undecorated’

function or method. Because functions are ﬁrst-class objects in Python, it can be done ‘manually’, but using the

@decorator syntax is clearer and thus preferred.

def foo():

# do something

def decorator(func):

# manipulate func

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return func

foo = decorator(foo) # Manually decorate

@decorator

def bar():

# Do something

# bar() is decorated

This mechanism is useful for separating concerns and avoiding external un-related logic ‘polluting’ the core logic

of the function or method. A good example of a piece of functionality that is better handled with decoration is

memoization or caching: you want to store the results of an expensive function in a table and use them directly instead

of recomputing them when they have already been computed. This is clearly not part of the function logic.

2.1.6 Dynamic typing

Python is said to be dynamically typed, which means that variables do not have a ﬁxed type. In fact, in Python,

variables are very different from what they are in many other languages, speciﬁcally strongly-typed languages. Vari-

ables are not a segment of the computer’s memory where some value is written, they are ‘tags’ or ‘names’ pointing

to objects. It is therefore possible for the variable ‘a’ to be set to the value 1, then to the value ‘a string’, then to a

function.

The dynamic typing of Python is often considered to be a weakness, and indeed it can lead to complexities and hard-

to-debug code. Something named ‘a’ can be set to many different things, and the developer or the maintainer needs to

track this name in the code to make sure it has not been set to a completely unrelated object.

Some guidelines help to avoid this issue:

• Avoid using the same variable name for different things.

Bad

a = 1

a = ’a string’

def a():

pass # Do something

Good

count = 1

msg = ’a string’

def func():

pass # Do something

Using short functions or methods helps reduce the risk of using the same name for two unrelated things.

It is better to use different names even for things that are related, when they have a different type:

Bad

items = ’a b c d’ # This is a string...

items = items.split(’ ’) # ...becoming a list

items = set(items) # ...and then a set

There is no efﬁciency gain when reusing names: the assignments will have to create new objects anyway. However,

when the complexity grows and each assignment is separated by other lines of code, including ‘if’ branches and loops,

it becomes harder to ascertain what a given variable’s type is.

Some coding practices, like functional programming, recommend never reassigning a variable. In Java this is done

with the ﬁnal keyword. Python does not have a ﬁnal keyword and it would be against its philosophy anyway. However,

2.1. Structuring Your Project 13

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