Python: efficient method to replace accents (é to e), remove [^a-zA-Z\d\s], and lower() [duplicate]

Question

Using Python 3.3. I want to do the following:

• replace special alphabetical characters such as e acute (é) and o circumflex (ô) with the base character (ô to o, for example)
• remove all characters except alphanumeric and spaces in between alphanumeric characters
• convert to lowercase

This is what I have so far:

mystring_modified = mystring.replace('\u00E9', 'e').replace('\u00F4', 'o').lower()
alphnumspace = re.compile(r"[^a-zA-Z\d\s]")
mystring_modified = alphnumspace.sub('', mystring_modified)

How can I improve this? Efficiency is a big concern, especially since I am currently performing the operations inside a loop:

# Pseudocode
for mystring in myfile:
    mystring_modified = # operations described above
    mylist.append(mystring_modified)

The files in question are about 200,000 characters each.

Answer 1

>>> import unicodedata
>>> s='éô'
>>> ''.join((c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn'))
'eo'

Also check out unidecode

What Unidecode provides is a middle road: function unidecode() takes Unicode data and tries to represent it in ASCII characters (i.e., the universally displayable characters between 0x00 and 0x7F), where the compromises taken when mapping between two character sets are chosen to be near what a human with a US keyboard would choose.

The quality of resulting ASCII representation varies. For languages of western origin it should be between perfect and good. On the other hand transliteration (i.e., conveying, in Roman letters, the pronunciation expressed by the text in some other writing system) of languages like Chinese, Japanese or Korean is a very complex issue and this library does not even attempt to address it. It draws the line at context-free character-by-character mapping. So a good rule of thumb is that the further the script you are transliterating is from Latin alphabet, the worse the transliteration will be.

Note that this module generally produces better results than simply stripping accents from characters (which can be done in Python with built-in functions). It is based on hand-tuned character mappings that for example also contain ASCII approximations for symbols and non-Latin alphabets.