Notes on Python cookbook 3rd (2.12): review clean text strings

Giant ship 2020-11-14 01:02:15
notes python cookbook 3rd rd


Review clean text strings

problem

Some boring naive hackers will put the “python” Change to “pýtĥöñ”, And then you want to clean up the characters .

solution

Text cleaning involves a series of problems, including text parsing and data processing . In very simple cases , You may choose to use string functions ( such as str.upper() and str.lower() ) Convert text to standard format . Use str.replace() perhaps re.sub() Can delete or change the specified character sequence . You can also use it 2.9 Section of the unicodedata.normalize() Function will unicode Text Standardization .

then , Sometimes you may want to go a step further in cleaning operations . such as , You may want to remove the characters from the whole range or remove the diachronic note . In order to do so , You can use the often overlooked str.translate() Method . To demonstrate , Suppose you now have this messy string :

>>> s = 'pýtĥöñ\fis\tawesome\r\n'
>>> s
'pýtĥöñ\x0cis\tawesome\r\n'
>>>

The first step is to clean up white space . In order to do so , First create a small conversion table and then use translate() Method :

>>> remap = {
... ord('\t') : ' ',
... ord('\f') : ' ',
... ord('\r') : None # Deleted
... }
>>> a = s.translate(remap)
>>> a
'pýtĥöñ is awesome\n'
>>>

As you can see , Blank character nt and nf Has been remapped to a space . Carriage return character r Directly deleted .


You can build on this table and build a bigger one . such as , Let's delete all and notes :

>>> import unicodedata
>>> import sys
>>> cmb_chrs = dict.fromkeys(c for c in range(sys.maxunicode)
... if unicodedata.combining(chr(c)))
...
>>> b = unicodedata.normalize('NFD', a)
>>> b
'pýtĥöñ is awesome\n'
>>> b.translate(cmb_chrs)
'python is awesome\n'
>>>

In the example above , By using dict.fromkeys() Method to construct a dictionary , Every Unicode And notes as keys , All values for are None .

And then use unicodedata.normalize() Normalize the original input to decomposed form characters . Then call translate Function to delete all accents . The same technique can also be used to delete other types of characters ( For example, control characters, etc ).


As another example , Here we construct one that will all Unicode Numeric characters are mapped to the corresponding ASCII The table on the character :

>>> digitmap = { c: ord('0') + unicodedata.digit(chr(c))
... for c in range(sys.maxunicode)
... if unicodedata.category(chr(c)) == 'Nd' }
...
>>> len(digitmap)
460
>>> # Arabic digits
>>> x = '\u0661\u0662\u0663'
>>> x.translate(digitmap)
'123'
>>>

Another technique for cleaning up text involves I/O Decoding and encoding functions . The idea here is to do a text first Some preliminary cleaning up , And then combine encode() perhaps decode() Operation to clear or modify it . such as :

>>> a
'pýtĥöñ is awesome\n'
>>> b = unicodedata.normalize('NFD', a)
>>> b.encode('ascii', 'ignore').decode('ascii')
'python is awesome\n'
>>>

The standardization here breaks down the original text into separate and notes . Next ASCII code / Decoding is just a matter of discarding the characters all at once . Of course , The only goal of this method is to get the text correspondence ACSII It takes effect when it is indicated .

Discuss

One of the most important problems with text character cleaning should be the performance of the operation . In general , The simpler the code, the faster it runs . For simple replacement operations , str.replace() The method is usually the fastest , Even when you need to call more than once . such as , To clean up white space , You can do that :

def clean_spaces(s):
s = s.replace('\r', '')
s = s.replace('\t', ' ')
s = s.replace('\f', ' ')
return s

If you go to the test , You'll find that this way is better than using translate() Or regular expressions are much faster .

On the other hand , If you need to perform any complex character to character remapping or deleting operations ,tanslate() The method will be very fast .

In a big way , For your application, performance is something you have to study yourself . Unfortunately , It's impossible for us to suggest a specific technology , Make it adaptable to all situations . So in practice, it's up to you to try different methods and evaluate them .

Although this article discusses text , But similar techniques can be applied to bytes , Including simple replacement , Transformations and regular expressions .

版权声明
本文为[Giant ship]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