I have a Unicode string in Python, and I would like to remove all the accents (diacritics).
I found an elegant way to do this (in Java):
Do I need to install a library such as pyICU or is this possible with just the Python standard library? And what about Python 3?
I would like to avoid explicitly mapping characters
Unidecode transliterates any unicode string into the closest possible representation in ascii text:
>>> from unidecode import unidecode >>> unidecode('kožušček') 'kozuscek' >>> unidecode('北亰') 'Bei Jing ' >>> unidecode('François') 'Francois' 
'François' is mapped to 'Francois' as you'd expect.
unidecode replaces ° with deg. It does more than just removing accents.How about this:
import unicodedata def strip_accents(s): return ''.join(c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn') This works on greek letters, too:
>>> strip_accents(u"A \u00c0 \u0394 \u038E") u'A A \u0394 \u03a5' >>> The character category "Mn" stands for Nonspacing_Mark, which is similar to unicodedata.combining in MiniQuark's answer (I didn't think of unicodedata.combining, but it is probably the better solution, because it's more explicit).
And keep in mind, these manipulations may significantly alter the meaning of the text. Accents, Umlauts etc. are not "decoration".
unicodedata.name, or break down and use a look-alike table-- which you'd need for Greek letters anyway (Α is just "GREEK CAPITAL LETTER ALPHA").
ß into ascii ss in example. I would still use unidecode to avoid accidents.
I just found this answer on the Web:
import unicodedata def remove_accents(input_str): nfkd_form = unicodedata.normalize('NFKD', input_str) only_ascii = nfkd_form.encode('ASCII', 'ignore') return only_ascii It works fine (for French, for example), but I think the second step (removing the accents) could be handled better than dropping the non-ASCII characters, because this will fail for some languages (Greek, for example). The best solution would probably be to explicitly remove the unicode characters that are tagged as being diacritics.
Edit: this does the trick:
import unicodedata def remove_accents(input_str): nfkd_form = unicodedata.normalize('NFKD', input_str) return u"".join([c for c in nfkd_form if not unicodedata.combining(c)]) unicodedata.combining(c) will return true if the character c can be combined with the preceding character, that is mainly if it's a diacritic.
Edit 2: remove_accents expects a unicode string, not a byte string. If you have a byte string, then you must decode it into a unicode string like this:
encoding = "utf-8" # or iso-8859-15, or cp1252, or whatever encoding you use byte_string = b"café" # or simply "café" before python 3. unicode_string = byte_string.decode(encoding) 
nkfd_form = unicodedata.normalize('NFKD', unicode(input_str, 'utf8'))
, 'utf8' is a "safety net" needed if you are testing input in terminal (which by default does not use unicode). But usually you don't have to add it, since if you're removing accents then input_str is very likely to be utf8 already. It doesn't hurt to be safe, though.
remove_accents instead of a regular string (u"é" instead of "é"). You passed a regular string to remove_accents, so when trying to convert your string to a unicode string, the default ascii encoding was used. This encoding does not support any byte whose value is >127. When you typed "é" in your shell, your O.S. encoded that, probably with UTF-8 or some Windows Code Page encoding, and that included bytes >127. I'll change my function in order to remove the conversion to unicode: it will bomb more clearly if a non-unicode string is passed.
Actually I work on project compatible python 2.6, 2.7 and 3.4 and I have to create IDs from free user entries.
Thanks to you, I have created this function that works wonders.
import re import unicodedata def strip_accents(text): """ Strip accents from input String. :param text: The input string. :type text: String. :returns: The processed String. :rtype: String. """ try: text = unicode(text, 'utf-8') except (TypeError, NameError): # unicode is a default on python 3 pass text = unicodedata.normalize('NFD', text) text = text.encode('ascii', 'ignore') text = text.decode("utf-8") return str(text) def text_to_id(text): """ Convert input text to id. :param text: The input string. :type text: String. :returns: The processed String. :rtype: String. """ text = strip_accents(text.lower()) text = re.sub('[ ]+', '_', text) text = re.sub('[^0-9a-zA-Z_-]', '', text) return text result:
text_to_id("Montréal, über, 12.89, Mère, Françoise, noël, 889") >>> 'montreal_uber_1289_mere_francoise_noel_889' 
text = unicode(text, 'utf-8'). A workaround for that was to addexcept TypeError: pass
M is large (capitalized) in the input then M is also large (capitalized) in the output? The input string "Montréal" becomes the output string "Montreal".This handles not only accents, but also "strokes" (as in ø etc.):
import unicodedata as ud def rmdiacritics(char): ''' Return the base character of char, by "removing" any diacritics like accents or curls and strokes and the like. ''' desc = ud.name(char) cutoff = desc.find(' WITH ') if cutoff != -1: desc = desc[:cutoff] try: char = ud.lookup(desc) except KeyError: pass # removing "WITH ..." produced an invalid name return char This is the most elegant way I can think of (and it has been mentioned by alexis in a comment on this page), although I don't think it is very elegant indeed. In fact, it's more of a hack, as pointed out in comments, since Unicode names are – really just names, they give no guarantee to be consistent or anything.
There are still special letters that are not handled by this, such as turned and inverted letters, since their unicode name does not contain 'WITH'. It depends on what you want to do anyway. I sometimes needed accent stripping for achieving dictionary sort order.
Incorporated suggestions from the comments (handling lookup errors, Python-3 code).
unicode function call in there with python 3 though? I think a tighter regex in place of the find would avoid all the trouble mentioned in the comment above, and also, memoization would help performance when it's a critical code path.unicode typecast is no longer appropriate in Python 3. In any case, in my experience there is no universal, elegant solution to this problem. Depending on the application, any approach has its pros and cons. Quality-thriving tools like unidecode are based on hand-crafted tables. Some resources (tables, algorithms) are provided by Unicode, eg. for collation.
In my view, the proposed solutions should NOT be accepted answers. The original question is asking for the removal of accents, so the correct answer should only do that, not that plus other, unspecified, changes.
Simply observe the result of this code which is the accepted answer. where I have changed "Málaga" by "Málagueña:
accented_string = u'Málagueña' # accented_string is of type 'unicode' import unidecode unaccented_string = unidecode.unidecode(accented_string) # unaccented_string contains 'Malaguena'and is of type 'str' There is an additional change (ñ -> n), which is not requested in the OQ.
A simple function that does the requested task, in lower form:
def f_remove_accents(old): """ Removes common accent characters, lower form. Uses: regex. """ new = old.lower() new = re.sub(r'[àáâãäå]', 'a', new) new = re.sub(r'[èéêë]', 'e', new) new = re.sub(r'[ìíîï]', 'i', new) new = re.sub(r'[òóôõö]', 'o', new) new = re.sub(r'[ùúûü]', 'u', new) return new 
gensim.utils.deaccent(text) from Gensim - topic modelling for humans:
'Sef chomutovskych komunistu dostal postou bily prasek' Another solution is unidecode.
Note that the suggested solution with unicodedata typically removes accents only in some character (e.g. it turns 'ł' into '', rather than into 'l').
deaccent still gives ł instead of l.
NumPy and SciPy to get accents removed.
In response to @MiniQuark's answer:
I was trying to read in a csv file that was half-French (containing accents) and also some strings which would eventually become integers and floats. As a test, I created a test.txt file that looked like this:
Montréal, über, 12.89, Mère, Françoise, noël, 889
I had to include lines 2 and 3 to get it to work (which I found in a python ticket), as well as incorporate @Jabba's comment:
import sys reload(sys) sys.setdefaultencoding("utf-8") import csv import unicodedata def remove_accents(input_str): nkfd_form = unicodedata.normalize('NFKD', unicode(input_str)) return u"".join([c for c in nkfd_form if not unicodedata.combining(c)]) with open('test.txt') as f: read = csv.reader(f) for row in read: for element in row: print remove_accents(element) The result:
Montreal uber 12.89 Mere Francoise noel 889 (Note: I am on Mac OS X 10.8.4 and using Python 2.7.3)
remove_accents was meant to remove accents from a unicode string. In case it's passed a byte-string, it tries to convert it to a unicode string with unicode(input_str). This uses python's default encoding, which is "ascii". Since your file is encoded with UTF-8, this would fail. Lines 2 and 3 change python's default encoding to UTF-8, so then it works, as you found out. Another option is to pass remove_accents a unicode string: remove lines 2 and 3, and on the last line replace element by element.decode("utf-8"). I tested: it works. I'll update my answer to make this clearer.
iso-8859-1, which I can't get to work with this function, unfortunately!)
reload(sys); sys.setdefaultencoding("utf-8") is a dubious hack sometimes recommended for Windows systems; see stackoverflow.com/questions/28657010/… for details.
import unicodedata from random import choice import perfplot import regex import text_unidecode def remove_accent_chars_regex(x: str): return regex.sub(r'\p{Mn}', '', unicodedata.normalize('NFKD', x)) def remove_accent_chars_join(x: str): # answer by MiniQuark # https://stackoverflow.com/a/517974/7966259 return u"".join([c for c in unicodedata.normalize('NFKD', x) if not unicodedata.combining(c)]) perfplot.show( setup=lambda n: ''.join([choice('Málaga François Phút Hơn 中文') for i in range(n)]), kernels=[ remove_accent_chars_regex, remove_accent_chars_join, text_unidecode.unidecode, ], labels=['regex', 'join', 'unidecode'], n_range=[2 ** k for k in range(22)], equality_check=None, relative_to=0, xlabel='str len' ) 
unidecode actually handles the Chinese characters. And none of the three comes up with the hilarious "FranASSois".Here is a short function which strips the diacritics, but keeps the non-latin characters. Most cases (e.g., "à" -> "a") are handled by unicodedata (standard library), but several (e.g., "æ" -> "ae") rely on the given parallel strings.
from unicodedata import combining, normalize LATIN = "ä æ ǽ đ ð ƒ ħ ı ł ø ǿ ö œ ß ŧ ü " ASCII = "ae ae ae d d f h i l o o oe oe ss t ue" def remove_diacritics(s, outliers=str.maketrans(dict(zip(LATIN.split(), ASCII.split())))): return "".join(c for c in normalize("NFD", s.lower().translate(outliers)) if not combining(c)) NB. The default argument outliers is evaluated once and not meant to be provided by the caller.
As a key to sort a list of strings in a more “natural” order:
sorted(['cote', 'coteau', "crottez", 'crotté', 'côte', 'côté'], key=remove_diacritics) Output:
['cote', 'côte', 'côté', 'coteau', 'crotté', 'crottez'] If your strings mix texts and numbers, you may be interested in composing remove_diacritics() with the function string_to_pairs() I give elsewhere.
To make sure the behavior meets your needs, take a look at the pangrams below:
examples = [ ("hello, world", "hello, world"), ("42", "42"), ("你好，世界", "你好，世界"), ( "Dès Noël, où un zéphyr haï me vêt de glaçons würmiens, je dîne d’exquis rôtis de bœuf au kir, à l’aÿ d’âge mûr, &cætera.", "des noel, ou un zephyr hai me vet de glacons wuermiens, je dine d’exquis rotis de boeuf au kir, a l’ay d’age mur, &caetera.", ), ( "Falsches Üben von Xylophonmusik quält jeden größeren Zwerg.", "falsches ueben von xylophonmusik quaelt jeden groesseren zwerg.", ), ( "Љубазни фењерџија чађавог лица хоће да ми покаже штос.", "љубазни фењерџија чађавог лица хоће да ми покаже штос.", ), ( "Ljubazni fenjerdžija čađavog lica hoće da mi pokaže štos.", "ljubazni fenjerdzija cadavog lica hoce da mi pokaze stos.", ), ( "Quizdeltagerne spiste jordbær med fløde, mens cirkusklovnen Walther spillede på xylofon.", "quizdeltagerne spiste jordbaer med flode, mens cirkusklovnen walther spillede pa xylofon.", ), ( "Kæmi ný öxi hér ykist þjófum nú bæði víl og ádrepa.", "kaemi ny oexi her ykist þjofum nu baedi vil og adrepa.", ), ( "Glāžšķūņa rūķīši dzērumā čiepj Baha koncertflīģeļu vākus.", "glazskuna rukisi dzeruma ciepj baha koncertfligelu vakus.", ) ] for (given, expected) in examples: assert remove_diacritics(given) == expected LATIN = "ä æ ǽ đ ð ƒ ħ ı ł ø ǿ ö œ ß ŧ ü Ä Æ Ǽ Đ Ð Ƒ Ħ I Ł Ø Ǿ Ö Œ ẞ Ŧ Ü " ASCII = "ae ae ae d d f h i l o o oe oe ss t ue AE AE AE D D F H I L O O OE OE SS T UE" def remove_diacritics(s, outliers=str.maketrans(dict(zip(LATIN.split(), ASCII.split())))): return "".join(c for c in normalize("NFD", s.translate(outliers)) if not combining(c)) 
ValueError: string keys in translate table must be of length 1. This limitation is clearly stated in the Python docs: docs.python.org/3/library/stdtypes.html#str.maketrans. Maybe you only tested with Python 2?
"SS" in the (untested) LATIN string for the case-preserving variant. I have replaced it by "ẞ", namely LATIN CAPITAL LETTER SHARP S. Thanks!Some languages have combining diacritics as language letters and accent diacritics to specify accent.
I think it is more safe to specify explicitly what diactrics you want to strip:
def strip_accents(string, accents=('COMBINING ACUTE ACCENT', 'COMBINING GRAVE ACCENT', 'COMBINING TILDE')): accents = set(map(unicodedata.lookup, accents)) chars = [c for c in unicodedata.normalize('NFD', string) if c not in accents] return unicodedata.normalize('NFC', ''.join(chars)) There are already many answers here, but this was not previously considered: using sklearn
from sklearn.feature_extraction.text import strip_accents_ascii, strip_accents_unicode accented_string = u'Málagueña®' print(strip_accents_unicode(accented_string)) # output: Malaguena® print(strip_accents_ascii(accented_string)) # output: Malaguena This is particularly useful if you are already using sklearn to process text. Those are the functions internally called by classes like CountVectorizer to normalize strings: when using strip_accents='ascii' then strip_accents_ascii is called and when strip_accents='unicode' is used, then strip_accents_unicode is called.
More details
Finally, consider those details from its docstring:
Signature: strip_accents_ascii(s) Transform accentuated unicode symbols into ascii or nothing Warning: this solution is only suited for languages that have a direct transliteration to ASCII symbols. and
Signature: strip_accents_unicode(s) Transform accentuated unicode symbols into their simple counterpart Warning: the python-level loop and join operations make this implementation 20 times slower than the strip_accents_ascii basic normalization. If you are hoping to get functionality similar to Elasticsearch's asciifolding filter, you might want to consider fold-to-ascii, which is [itself]...
A Python port of the Apache Lucene ASCII Folding Filter that converts alphabetic, numeric, and symbolic Unicode characters which are not in the first 127 ASCII characters (the "Basic Latin" Unicode block) into ASCII equivalents, if they exist.
Here's an example from the page mentioned above:
from fold_to_ascii import fold s = u'Astroturf® paté' fold(s) > u'Astroturf pate' fold(s, u'?') > u'Astroturf? pate' EDIT: The fold_to_ascii module seems to work well for normalizing Latin-based alphabets; however unmappable characters are removed, which means that this module will reduce Chinese text, for example, to empty strings. If you want to preserve Chinese, Japanese, and other Unicode alphabets, consider using @mo-han's remove_accent_chars_regex implementation, above.
