Python入门指南：从安装到项目实践

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本资源是一份名为"Python Guide"的入门指南文档，由Kenneth Reitz编写，发布于2014年2月10日，适合初学者快速上手Python编程语言。该文档分为五个主要部分： 1. 入门： - 选择解释器：介绍如何根据操作系统的不同（MacOS X、Windows、Linux）安装Python解释器。 - 编写优质代码： - 项目结构：讲解如何组织项目，包括模块划分和文件布局。 - 编码风格：强调遵循Python的PEP 8编码规范。 - 阅读优秀代码：提升代码阅读理解能力，培养良好的编程习惯。 - 文档编写：指出文档的重要性，包括注释和文档字符串的使用。 - 测试代码：讲解测试方法和工具，如单元测试和持续集成。 - 常见陷阱：列出一些新手可能会遇到的问题及解决方案。 - 选择许可：讨论开源软件的许可证选择。 2. 场景应用： - 网络应用：介绍Python在网络编程中的应用，可能涉及HTTP请求、API调用等。 - Web应用：涵盖Web开发框架和技术，如Flask或Django。 - HTML抓取：讲述如何使用Python进行网页数据抓取。 - 命令行应用：说明如何编写简单的命令行工具。 - GUI应用：讨论图形用户界面（GUI）编程，如Tkinter库。 - 数据库操作：介绍Python与数据库交互的基本概念和工具。 - 网络编程：涉及网络通信、套接字编程等。 - 系统管理：在Python中进行系统监控和自动化运维。 - 持续集成：探讨如何将Python项目集成到CI/CD流程中。 - 性能优化：探讨提高Python程序执行速度的方法。 - 科学计算：讲解Python在科学计算和数据分析中的应用。 - 图像处理：介绍Python图像处理库，如PIL或OpenCV。 - XML解析：讲解如何解析和操作XML数据。 3. 发布和打包： - 包管理：解释如何组织和打包代码，以便于分享和部署。 - 代码冻结：介绍将应用程序打包成独立可执行文件的技术。 4. 开发环境： - 开发环境设置：推荐适合Python开发的工具和环境配置。 - 虚拟环境：解释虚拟环境的概念，以及为何在项目中使用它们。 5. 附加信息： - 社区介绍：提供对Python社区的简短介绍，包括用户组、论坛和贡献机制。 - 学习路径：给出学习Python的建议和资源。这份指南详细且全面地涵盖了Python语言基础、实际应用、项目管理以及开发环境设置等方面，对Python初学者和有一定经验的开发者都具有参考价值。通过阅读和实践文档中的内容，读者可以逐步掌握Python编程技能并构建各种类型的项目。

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 the case of my.spam.py 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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Python Guide Documentation, Release 0.0.1

[...]

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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Python Guide Documentation, Release 0.0.1

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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Python入门指南：从安装到项目实践

python-guide Kenneth Reitz

python-guide, python 最佳实践指南，为人类编写.zip

Python Guide

用yum怎么下载python-setuptools python-imaging python-ldap MySQL-python python-memcached python-urllib

sudo apt install -y ninja-build exiftool python-argparse python-empy python-toml python-numpy python-yaml python-dev python-pip ninja-build protobuf-compiler libeigen3-dev genromfs xmlstarlet libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev

这段代码要求安装哪些库：Required RHEL5 packages: mysql mysql-server memcached python-memcached MySQL-python python-devel gcc python-dpkt atlas atlas-devel lapack lapack-devel blas blas-devel glpk-devel g2clib-devel compat-libf2c-34 compat-gcc-34-g77

python-srpm-macros 被 python-rpm-macros-3-34.el7.noarch 需要

分析以下要求分别要安装哪些库：Required RHEL5 packages: mysql mysql-server memcached python-memcached MySQL-python python-devel gcc python-dpkt atlas atlas-devel lapack lapack-devel blas blas-devel glpk-devel g2clib-devel compat-libf2c-34 compat-gcc-34-g77

服务器使用conda环境下载python-docx 报错没有找到python-docx

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