I am looking into this dataset: https://www.kaggle.com/datasets/stefanoleone992/rotten-tomatoes-movies-and-critic-reviews-dataset?select=rotten_tomatoes_movies.csv
I am interested in scores grouped by production companies, but some companies have subdivisions that are very similar to each other, e.g. 20th Century Fox, 20th Century Fox Distribution, 20th Century Fox Film, 20th Century Fox Film Corp., and so on.
I am searching for a way to collect all the movies produced under subdivision into one category, in this case 20th Century Fox - as I am not interested in their specific division.
I have done some initalization and cleaning of the data based on a Git depository:
import pandas as pd
import numpy as np
df = pd.read_csv('rotten_tomatoes_movies.csv')
cols_of_interest = ['movie_title', 'genres', 'content_rating', 'original_release_date', 'streaming_release_date', 'runtime', 'tomatometer_rating', 'tomatometer_count', 'audience_rating', 'audience_count', 'production_company']
df = df[cols_of_interest]
df.original_release_date.fillna(df.streaming_release_date, inplace=True)
df.drop(columns=['streaming_release_date'], inplace=True)
df.rename(columns={'original_release_date': 'release_date'}, inplace=True)
df = df[(df.tomatometer_count>0) & (df.audience_count>0)]
df.drop_duplicates(subset=['movie_title', 'release_date'], inplace=True)
df.dropna(subset=['genres', 'release_date'], inplace=True)
df = df.sort_values(by='release_date', ascending=True).reset_index(drop=True)
For my specific problem I had the idea to base analysis on the first word using:
df.production_company.astype('|S')
test = df.production_company.split(' ',1)
which gives
UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 1: ordinal not in range(128)
Any ideas on other approaches or help on the current Error would be greatly appreciated!
Maybe some production companies are french ones.
According to Wikipedia : "The unicode string for \xe9 is an accented e - é".
You can try to specify the encoding.
df = pd.read_csv('rotten_tomatoes_movies.csv', encoding='utf-8')
I guess I've found it.
It's still an encoding issue but this time related to the astype() method.
Example below works.
df = pd.DataFrame({'production_company': ["Cinefrance Studios"]})
df.production_company.astype("|S")
Example below doesn't and raises an exception, complaining about a byte value 0xe9 (corresponding to the é accented character) that equals to 233 that indeed exceeds 128 (2^7).
df = pd.DataFrame({'production_company': ["Cinéfrance Studios"]})
df.production_company.astype("|S")
It's due to the presence of the accented character in the name of the company.
It seems that the '|S' action - haven't found any documentation about that - is restricted to ASCII encoding (American Standard Code for Information Interchange) that only take into account the 7 first bits of the byte. This way accents are not supported, their representation require at least extended ascii (the 8 bits).
The better and most universal way would be to use unicode.
If your goal is to obtain bytes from the production company names, I can suggest this solution :
df.production_company = df.production_company.apply(lambda x: x.encode('utf-8'))
Otherwise may be there's a way to indicate to astype() that it has to use an utf-8 codec instead of the ascii codec.
string = "At Donald Trump<U+2019>s Properties, a Showcase for a Brand and a President-Elect"
I want to get rid of the <U + 2019> and replace it with '. Is there a way to do this in python?
Edit : I also have instances of <U + 2014>, <U + 201C> etc. Looking for something which can replace all of this with appropriate values
Replace them all at once with re.sub:
import re
string = "testing<U+2019> <U+2014> <U+201C>testing<U+1F603>"
result = re.sub(r'<U\+([0-9a-fA-F]{4,6})>', lambda x: chr(int(x.group(1),16)), string)
print(result)
Output:
testing’ — “testing😃
The regular expression matches <U+hhhh> where hhhh can be 4-6 hexadecimal characters. Note that Unicode defines code points from U+0000 to U+10FFFF so this accounts for that. The lambda replacement function converts the string hhhh to an integer using base 16 and then converts that number to a Unicode character.
Here's my solution for all code points denoted as U+0000 through U+10FFFF ("U+" followed by the code point value in hexadecimal, which is prepended with leading zeros to a minimum of four digits):
import re
def UniToChar(unicode_notation):
return chr(int(re.findall(r'<U\+([a-hA-H0-9]{4,5})>',unicode_notation)[0],16))
xx= '''
At Donald<U+2019>s <U+2016>Elect<U+2016> in <U+2017>2019<U+2017>
<U+00C0> la Donald<U+2019>s friend <U+1F986>. <U+1F929><U+1F92A><U+1F601>
'''
for x in xx.split('\n'):
abc = re.findall(r'<U\+[a-hA-H0-9]{4,5}>',x)
if len(abc) > 0:
for uniid in set(abc): x=x.replace(uniid, UniToChar(uniid))
print(repr(x).strip("'"))
Output: 71307293.py
At Donald’s ‖Elect‖ in ‗2019‗
À la Donald’s friend 🦆. 🤩🤪😁
In fact, private range from U+100000 to U+10FFFD (Plane 16) isn't detected using above simplified regex… Improved code follows:
import re
def UniToChar(unicode_notation):
aux = int(re.findall(r'<U\+([a-hA-H0-9]{4,6})>',unicode_notation)[0],16)
# circumvent the "ValueError: chr() arg not in range(0x110000)"
if aux <= 0x10FFFD:
return chr(aux)
else:
return chr(0xFFFD) # Replacement Character
xx= '''
At Donald<U+2019>s <U+2016>Elect<U+2016> in <U+2017>2019<U+2017>
<U+00C0> la Donald<U+2019>s friend <U+1F986>. <U+1F929><U+1F92A><U+1F601>
Unassigned: <U+05ff>; out of Unicode range: <U+110000>.
'''
for x in xx.split('\n'):
abc = re.findall(r'<U\+[a-hA-H0-9]{4,6}>',x)
if len(abc) > 0:
for uniid in set(abc): x=x.replace(uniid, UniToChar(uniid))
print(repr(x).strip("'"))
Output: 71307293.py
At Donald’s ‖Elect‖ in ‗2019‗
À la Donald’s friend 🦆. 🤩🤪😁
Unassigned: \u05ff; out of Unicode range: �.
I guess this solves the problem if its just one or two of these characters.
>>> string = "At Donald Trump<U+2019>s Properties, a Showcase for a Brand and a President-Elect"
>>> string.replace("<U+2019>","'")
"At Donald Trump's Properties, a Showcase for a Brand and a President-Elect"
If there are many if these substitutions to be done, consider using 'map()' method.
Source: Removing \u2018 and \u2019 character
You can replace using .replace()
print(string.replace('<U+2019>', "'"))
Or if your year changes, you can use re. But make it more attractive than mine.
import re
string = "At Donald Trump<U+2019>s Properties, a Showcase for a Brand and a President-Elect"
rep = re.search('[<][U][+]\d{4}[>]', string).group()
print(string.replace(rep, "'"))
what version of python are u using?
I edited my answer so it can bee used with multiple code point in the same string
well u need to convert the unicode's code point that is between < >, to unicode char
I used regex to get the unicode's code point and then convert it to the corresponding uniode char
import re
string = "At Donald Trump<U+2019>s Properties, a Showcase for a Brand and a President<U+2014>Elect"
repbool = re.search('[<][U][+]\d{4}[>]', string)
while repbool:
rep = re.search('[<][U][+]\d{4}[>]', string).group()
string=string.replace(rep, chr(int(rep[1:-1][2:], 16)))
repbool = re.search('[<][U][+]\d{4}[>]', string)
print(string)
I have a Unicode string in Python, and I would like to remove all the accents (diacritics).
I found on the web an elegant way to do this (in Java):
convert the Unicode string to its long normalized form (with a separate character for letters and diacritics)
remove all the characters whose Unicode type is "diacritic".
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?
Important note: I would like to avoid code with an explicit mapping from accented characters to their non-accented counterpart.
Unidecode is the correct answer for this. It transliterates any unicode string into the closest possible representation in ascii text.
Example:
>>> from unidecode import unidecode
>>> unidecode('kožušček')
'kozuscek'
>>> unidecode('北亰')
'Bei Jing '
>>> unidecode('François')
'Francois'
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".
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)
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'
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.
EDIT NOTE:
Incorporated suggestions from the comments (handling lookup errors, Python-3 code).
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').
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)
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'
)
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))
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.
Code
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.
Intended usage
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.
Tests
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
Case-preserving variant
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))
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.
I need to find a way to rewrite words(translit) from some languages into English language. For example привет (in Russian) sounds like privet (in English).
Meaning and grammar don't matter, but I'd like it to have a more similar sounding. Everything should be in Python, I have diligently looked up on the internet and haven't found a good approach.
For example, something similar to this:
translit("юу со беутифул", "ru") = juu so beutiful
translit("кар", "ru") = kar
Maybe you should give unidecode a try:
>>> import unidecode
>>> unidecode.unidecode("юу со беутифул")
'iuu so beutiful'
>>> unidecode.unidecode("die größten Probleme")
'die grossten Probleme'
>>> unidecode.unidecode("Avec Éloïse, ils président à l'assemblée")
"Avec Eloise, ils president a l'assemblee"
Install it with pip:
pip3 install unidecode
Maybe you are already using it; but you can use transliterate package.
Basic install with pip:
pip install transliterate
Then the code
# coding: utf-8
from transliterate import translit
print translit(u"юу со беутифул", 'ru', reversed=True) # juu so beutiful
WITH CUSTOM CLASS
As #Schmuddi propose, you can create your own custom class to handle german special characters, (works only with python 3.X though).
pip3 install transliterate
Then the code:
# coding: utf-8
from transliterate import translit
from transliterate.base import TranslitLanguagePack, registry
class GermanLanguagePack(TranslitLanguagePack):
language_code = "de"
language_name = "Deutsch"
pre_processor_mapping = {
u"ß": u"ss",
}
mapping = (
u"ÄÖÜäöü",
u"AOUaou",
)
registry.register(GermanLanguagePack)
print(translit(u"Die größten Katzenrassen der Welt", "de"))
#Die grossten Katzenrassen der Welt
Bonus, the French one:
class FrenchLanguagePack(TranslitLanguagePack):
language_code = "fr"
language_name = "French"
pre_processor_mapping = {
u"œ": u"oe",
u"Œ": u"oe",
u"æ": u"ae",
u"Æ": "AE"
}
mapping = (
u"àâçéèêëïîôùûüÿÀÂÇÉÈÊËÏÎÔÙÛÜŸ",
u"aaceeeeiiouuuyAACEEEEIIOUUUY"
)
registry.register(FrenchLanguagePack)
print(translit(u"Avec Éloïse, ils président à l'assemblée", 'fr'))
#Avec Eloise, ils president a l'assemblee
OTHER POSSIBLE SOLUTION
Since transliterate doesn't cover the german langage (yet?), you can use another package to directly translate sentences: py-translate but it uses google translate so you do need an internet connexion.
Basic install with pip:
pip install py-translate
Then your code:
# coding: utf-8
from translate import translator
print translator('ru', 'en', u"юу со беутифул")
print translator('de', 'en', u"Die größten Katzenrassen der Welt")
Here's an alternative solution to #lenz. But I do like #lenz's suggestion of unidecode better =)
From
Python - Replace non-ascii character in string (») and Can somone explain how unicodedata.normalize(form, unistr) work with examples?
To resolve umlauts and accent and graves:
>>> re.sub(r'[^\x00-\x7f]',r'', normalize('NFD', u"Avec Éloïse, ils président à l'assemblée"))
u"Avec Eloise, ils president a l'assemblee"
But it doesn't solve sharp-S character and Cyrillic though:
>>> re.sub(r'[^\x00-\x7f]',r'', normalize('NFD', u"die größten Probleme"))
u'die groten Probleme'
>>> re.sub(r'[^\x00-\x7f]',r'', normalize('NFD', u"юу со беутифул"))
u' '
This is another possible solution using regex, you can configure this function to replace the special characters for the characters you want:
import re
def remove_accents(string):
if type(string) is not unicode:
string = unicode(string, encoding='utf-8')
string = re.sub(u"[àáâãäå]", 'a', string)
string = re.sub(u"[èéêë]", 'e', string)
string = re.sub(u"[ìíîï]", 'i', string)
string = re.sub(u"[òóôõö]", 'o', string)
string = re.sub(u"[ùúûü]", 'u', string)
string = re.sub(u"[ýÿ]", 'y', string)
return string