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


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


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

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)
>>> # Arabic digits
>>> x = '\u0661\u0662\u0663'
>>> x.translate(digitmap)

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 .


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 .

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