Guide to Python

前言

成为优秀Python编写者的秘诀是去阅读,理解和领会好的代码。

结构化工程

一个python项目包含

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README.rst
LICENSE
setup.py
Makefile # 常规任务管理
requirements.txt
project/__init__.py
docs/index.rst
tests/test_basic.py

测试上下文文件 tests/context.py

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import os, sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
# or sys.path.append()
import project

# 在tests/test_basic.py 中 from .context import project 导入

创建django项目
django-admin startproject project . 注意末尾的.

代码风格

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for index, item in enumerate(list):
pass
four_nones = [None] * 4
four_list = [[] for __ in range(4)]
word = ''.join(list)

# Python之禅
import this
'''
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

Python之禅 by Tim Peters

优美胜于丑陋(Python以编写优美的代码为目标)
明了胜于晦涩(优美的代码应当是明了的,命名规范,风格相似)
简洁胜于复杂(优美的代码应当是简洁的,不要有复杂的内部实现)
复杂胜于凌乱(如果复杂不可避免,那代码间也不能有难懂的关系,要保持接口简洁)
扁平胜于嵌套(优美的代码应当是扁平的,不能有太多的嵌套)
间隔胜于紧凑(优美的代码有适当的间隔,不要奢望一行代码解决问题)
可读性很重要(优美的代码是可读的)
即便假借特例的实用性之名,也不可违背这些规则(这些规则至高无上)
不要包容所有错误,除非您确定需要这样做(精准地捕获异常,不写 except:pass 风格的代码)
当存在多种可能,不要尝试去猜测
而是尽量找一种,最好是唯一一种明显的解决方案(如果不确定,就用穷举法)
虽然这并不容易,因为您不是 Python 之父(这里的 Dutch 是指 Guido )
做也许好过不做,但不假思索就动手还不如不做(动手之前要细思量)
如果您无法向人描述您的方案,那肯定不是一个好方案;反之亦然(方案测评标准)
命名空间是一种绝妙的理念,我们应当多加利用(倡导与号召)
'''

常见陷阱

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# 迟绑定闭包
def create_multipliers():
return [lambda x: x * i for i in range(4)]

for multiplier in create_multipliers():
print(multiplier(2))

# 得到 8 8 8 8 8
# Python的闭包是迟绑定,这意味着闭包中用到的变量的值,是在内部函数被调用时查询得到的,这里,不论任何返回的函数是如何被调用的,i的值是调用时在周围作用域查询到的。接着,循环完成,i的值最终变成4。

# 解决 --函数默认参数赋值
def create_multipliers():
return [lambda x, i=i: i * x for i in range(4)]

from functools import partial
from operator import mul

def create_multipliers():
return [partial(mul, i) for i in range(4)]

参考

https://pythonguidecn.readthedocs.io/zh/latest/index.html
记得多看几遍。

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