Understanding Python closures should be the best example

Tianyuan prodigal son 2020-11-13 09:06:07
understanding python closures best example


As a programming language feature , Closures are supported by many programming languages ,Python No exception . So called closure , stay Python A function that carries one or more free quantities . The free quantity of a closure function is not a parameter of the function , It's the environment variable when the function is generated . Once the closure is generated , Free variables are bound to functions , Even if you leave the environment that created it , The amount of freedom is still valid . To sum up , The concept of closure has three main points .

  • Closure is a function
  • Closure functions are generated by other code
  • The closure function carries information about the generation environment

There is a good example to help beginners understand closures . We know , Almost all the computing modules , such as Python Built in standard math module math, The logarithmic function provided can only be calculated with 2 Bottom 、 With e At the bottom and with 10 Three logarithms at the base .

>>> import math
>>> math.log(math.e) # Return to e Bottom e The logarithmic 
1.0
>>> math.log2(4) # Return to 2 Bottom 4 The logarithmic 
2.0
>>> math.log10(1000) # Return to 10 Bottom 1000 The logarithmic 
3.0

If you want to calculate with a Bottom b The logarithmic , You need to use the logarithmic bottoming formula .

l o g a b = l o g 10 b l o g 10 a log_ab=\frac{log_{10}b}{log_{10}a} logab=log10alog10b

>>> def glog(b, a): # Return to a Bottom b The logarithmic 
return math.log(b)/math.log(a)
>>> glog(25, 5) # # Return to 5 Bottom 25 The logarithmic 
2.0

Of course, we can define a function like the code above glog(), Calculate logarithms based on any number , But you always have to enter two parameters at a time , and math Modular log()、log2()、log10() Function styles are inconsistent . If you use closures , Can generate and math The style of logarithmic functions is consistent .

>>> def log_factory(n): # Define a closure generating function 
def log_n(x): # Generating closures 
return math.log(x)/math.log(n) # The closure carries the environment parameters n
return log_n # Back to closure 
>>> log5 = log_factory(5) # Generating closures with closure generators 
>>> log7 = log_factory(7) # Generating closures with closure generators 
>>> log5(25) # The free quantity carried by the closure is 5
2.0
>>> log7(49) # The free quantity carried by the closure is 7
2.0

Above code , Firstly, a logarithmic function generator is designed log_factory(), Enter an integer n, Just return one to n Base logarithmic function . The generator is then used to generate two closure functions , A group called log5, A group called log7. Finally, verify , Everything is exactly the same as we thought .


The example cited in this article , From my new book 《Python The way of Master Cultivation 》, There are many similar examples in the book . The book has been officially launched and pre sold in Jingdong and Dangdang , During the period of double 11, the preferential power is unprecedented ( Discount time :11 month 9 Japan ~11 month 11 Japan ). If you think this book is good , Don't miss the moment . Students who want to sign , Please pay attention to the official account. “Python Homework counselor ”, reply “Python The way of Master Cultivation ”, You will receive instructions on how to purchase the signature version .

 Insert picture description here
 Insert picture description here

Channel one : Jingdong self owned books

  • Discount time :11 month 9 Japan ~11 Japan

 Insert picture description here
Channel 2 : Dangdang self owned books

  • Activity time :11 month 9 Japan ~11 Japan

 Insert picture description here
Channel 3 : Signature of the author

  • Official account “Python Homework counselor ”
  • reply “Python The way of Master Cultivation ”, You will receive instructions on how to purchase the signature version

Price and preferential measures are subject to the actual release of each platform .

版权声明
本文为[Tianyuan prodigal son]所创,转载请带上原文链接,感谢

  1. 利用Python爬虫获取招聘网站职位信息
  2. Using Python crawler to obtain job information of recruitment website
  3. Several highly rated Python libraries arrow, jsonpath, psutil and tenacity are recommended
  4. Python装饰器
  5. Python实现LDAP认证
  6. Python decorator
  7. Implementing LDAP authentication with Python
  8. Vscode configures Python development environment!
  9. In Python, how dare you say you can't log module? ️
  10. 我收藏的有关Python的电子书和资料
  11. python 中 lambda的一些tips
  12. python中字典的一些tips
  13. python 用生成器生成斐波那契数列
  14. python脚本转pyc踩了个坑。。。
  15. My collection of e-books and materials about Python
  16. Some tips of lambda in Python
  17. Some tips of dictionary in Python
  18. Using Python generator to generate Fibonacci sequence
  19. The conversion of Python script to PyC stepped on a pit...
  20. Python游戏开发,pygame模块,Python实现扫雷小游戏
  21. Python game development, pyGame module, python implementation of minesweeping games
  22. Python实用工具,email模块,Python实现邮件远程控制自己电脑
  23. Python utility, email module, python realizes mail remote control of its own computer
  24. 毫无头绪的自学Python,你可能连门槛都摸不到!【最佳学习路线】
  25. Python读取二进制文件代码方法解析
  26. Python字典的实现原理
  27. Without a clue, you may not even touch the threshold【 Best learning route]
  28. Parsing method of Python reading binary file code
  29. Implementation principle of Python dictionary
  30. You must know the function of pandas to parse JSON data - JSON_ normalize()
  31. Python实用案例,私人定制,Python自动化生成爱豆专属2021日历
  32. Python practical case, private customization, python automatic generation of Adu exclusive 2021 calendar
  33. 《Python实例》震惊了,用Python这么简单实现了聊天系统的脏话,广告检测
  34. "Python instance" was shocked and realized the dirty words and advertisement detection of the chat system in Python
  35. Convolutional neural network processing sequence for Python deep learning
  36. Python data structure and algorithm (1) -- enum type enum
  37. 超全大厂算法岗百问百答(推荐系统/机器学习/深度学习/C++/Spark/python)
  38. 【Python进阶】你真的明白NumPy中的ndarray吗?
  39. All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)
  40. [advanced Python] do you really understand ndarray in numpy?
  41. 【Python进阶】Python进阶专栏栏主自述:不忘初心,砥砺前行
  42. [advanced Python] Python advanced column main readme: never forget the original intention and forge ahead
  43. python垃圾回收和缓存管理
  44. java调用Python程序
  45. java调用Python程序
  46. Python常用函数有哪些?Python基础入门课程
  47. Python garbage collection and cache management
  48. Java calling Python program
  49. Java calling Python program
  50. What functions are commonly used in Python? Introduction to Python Basics
  51. Python basic knowledge
  52. Anaconda5.2 安装 Python 库(MySQLdb)的方法
  53. Python实现对脑电数据情绪分析
  54. Anaconda 5.2 method of installing Python Library (mysqldb)
  55. Python implements emotion analysis of EEG data
  56. Master some advanced usage of Python in 30 seconds, which makes others envy it
  57. python爬取百度图片并对图片做一系列处理
  58. Python crawls Baidu pictures and does a series of processing on them
  59. python链接mysql数据库
  60. Python link MySQL database