chardet

Character encoding auto-detection in Python. As smart as your browser. Open source.

Documentation

Frequently asked questions

What is character encoding?

When you think of “text”, you probably think of “characters and symbols I see on my computer screen”. But computers don’t deal in characters and symbols; they deal in bits and bytes. Every piece of text you’ve ever seen on a computer screen is actually stored in a particular character encoding. There are many different character encodings, some optimized for particular languages like Russian or Chinese or English, and others that can be used for multiple languages. Very roughly speaking, the character encoding provides a mapping between the stuff you see on your screen and the stuff your computer actually stores in memory and on disk.

In reality, it’s more complicated than that. Many characters are common to multiple encodings, but each encoding may use a different sequence of bytes to actually store those characters in memory or on disk. So you can think of the character encoding as a kind of decryption key for the text. Whenever someone gives you a sequence of bytes and claims it’s “text”, you need to know what character encoding they used so you can decode the bytes into characters and display them (or process them, or whatever).

What is character encoding auto-detection?

It means taking a sequence of bytes in an unknown character encoding, and attempting to determine the encoding so you can read the text. It’s like cracking a code when you don’t have the decryption key.

Isn’t that impossible?

In general, yes. However, some encodings are optimized for specific languages, and languages are not random. Some character sequences pop up all the time, while other sequences make no sense. A person fluent in English who opens a newspaper and finds “txzqJv 2!dasd0a QqdKjvz” will instantly recognize that that isn’t English (even though it is composed entirely of English letters). By studying lots of “typical” text, a computer algorithm can simulate this kind of fluency and make an educated guess about a text’s language.

In other words, encoding detection is really language detection, combined with knowledge of which languages tend to use which character encodings.

Who wrote this detection algorithm?

This library is a port of the auto-detection code in Mozilla. I have attempted to maintain as much of the original structure as possible (mostly for selfish reasons, to make it easier to maintain the port as the original code evolves). I have also retained the original authors’ comments, which are quite extensive and informative.

You may also be interested in the research paper which led to the Mozilla implementation, A composite approach to language/encoding detection.

Yippie! Screw the standards, I’ll just auto-detect everything!

Don’t do that. Virtually every format and protocol contains a method for specifying character encoding.

  • HTTP can define a charset parameter in the Content-type header.
  • HTML documents can define a <meta http-equiv="content-type"> element in the <head> of a web page.
  • XML documents can define an encoding attribute in the XML prolog.

If text comes with explicit character encoding information, you should use it. If the text has no explicit information, but the relevant standard defines a default encoding, you should use that. (This is harder than it sounds, because standards can overlap. If you fetch an XML document over HTTP, you need to support both standards and figure out which one wins if they give you conflicting information.)

Despite the complexity, it’s worthwhile to follow standards and respect explicit character encoding information. It will almost certainly be faster and more accurate than trying to auto-detect the encoding. It will also make the world a better place, since your program will interoperate with other programs that follow the same standards.

Why bother with auto-detection if it’s slow, inaccurate, and non-standard?

Sometimes you receive text with verifiably inaccurate encoding information. Or text without any encoding information, and the specified default encoding doesn’t work. There are also some poorly designed standards that have no way to specify encoding at all.

If following the relevant standards gets you nowhere, and you decide that processing the text is more important than maintaining interoperability, then you can try to auto-detect the character encoding as a last resort. An example is my Universal Feed Parser, which calls this auto-detection library only after exhausting all other options.

Supported encodings

Universal Encoding Detector currently supports over two dozen character encodings.

  • Big5, GB2312/GB18030, EUC-TW, HZ-GB-2312, and ISO-2022-CN (Traditional and Simplified Chinese)
  • EUC-JP, SHIFT_JIS, and ISO-2022-JP (Japanese)
  • EUC-KR and ISO-2022-KR (Korean)
  • KOI8-R, MacCyrillic, IBM855, IBM866, ISO-8859-5, and windows-1251 (Russian)
  • ISO-8859-2 and windows-1250 (Hungarian)
  • ISO-8859-5 and windows-1251 (Bulgarian)
  • ISO-8859-1 and windows-1252 (Western European languages)
  • ISO-8859-7 and windows-1253 (Greek)
  • ISO-8859-8 and windows-1255 (Visual and Logical Hebrew)
  • TIS-620 (Thai)
  • UTF-32 BE, LE, 3412-ordered, or 2143-ordered (with a BOM)
  • UTF-16 BE or LE (with a BOM)
  • UTF-8 (with or without a BOM)
  • ASCII

Warning

Due to inherent similarities between certain encodings, some encodings may be detected incorrectly. In my tests, the most problematic case was Hungarian text encoded as ISO-8859-2 or windows-1250 (encoded as one but reported as the other). Also, Greek text encoded as ISO-8859-7 was often mis-reported as ISO-8859-2. Your mileage may vary.

Usage

Basic usage

The easiest way to use the Universal Encoding Detector library is with the detect function.

Example: Using the detect function

The detect function takes one argument, a non-Unicode string. It returns a dictionary containing the auto-detected character encoding and a confidence level from 0 to 1.

>>> import urllib.request
>>> rawdata = urllib.request.urlopen('http://yahoo.co.jp/').read()
>>> import chardet
>>> chardet.detect(rawdata)
{'encoding': 'EUC-JP', 'confidence': 0.99}

Advanced usage

If you’re dealing with a large amount of text, you can call the Universal Encoding Detector library incrementally, and it will stop as soon as it is confident enough to report its results.

Create a UniversalDetector object, then call its feed method repeatedly with each block of text. If the detector reaches a minimum threshold of confidence, it will set detector.done to True.

Once you’ve exhausted the source text, call detector.close(), which will do some final calculations in case the detector didn’t hit its minimum confidence threshold earlier. Then detector.result will be a dictionary containing the auto-detected character encoding and confidence level (the same as the chardet.detect function returns).

Example: Detecting encoding incrementally

import urllib.request
from chardet.universaldetector import UniversalDetector

usock = urllib.request.urlopen('http://yahoo.co.jp/')
detector = UniversalDetector()
for line in usock.readlines():
    detector.feed(line)
    if detector.done: break
detector.close()
usock.close()
print(detector.result)
{'encoding': 'EUC-JP', 'confidence': 0.99}

If you want to detect the encoding of multiple texts (such as separate files), you can re-use a single UniversalDetector object. Just call detector.reset() at the start of each file, call detector.feed as many times as you like, and then call detector.close() and check the detector.result dictionary for the file’s results.

Example: Detecting encodings of multiple files

import glob
from chardet.universaldetector import UniversalDetector

detector = UniversalDetector()
for filename in glob.glob('*.xml'):
    print(filename.ljust(60), end='')
    detector.reset()
    for line in open(filename, 'rb'):
        detector.feed(line)
        if detector.done: break
    detector.close()
    print(detector.result)

How it works

This is a brief guide to navigating the code itself.

First, you should read A composite approach to language/encoding detection, which explains the detection algorithm and how it was derived. This will help you later when you stumble across the huge character frequency distribution tables like big5freq.py and language models like langcyrillicmodel.py.

The main entry point for the detection algorithm is universaldetector.py, which has one class, UniversalDetector. (You might think the main entry point is the detect function in chardet/__init__.py, but that’s really just a convenience function that creates a UniversalDetector object, calls it, and returns its result.)

There are 5 categories of encodings that UniversalDetector handles:

  1. UTF-n with a BOM. This includes UTF-8, both BE and LE variants of UTF-16, and all 4 byte-order variants of UTF-32.
  2. Escaped encodings, which are entirely 7-bit ASCII compatible, where non-ASCII characters start with an escape sequence. Examples: ISO-2022-JP (Japanese) and HZ-GB-2312 (Chinese).
  3. Multi-byte encodings, where each character is represented by a variable number of bytes. Examples: Big5 (Chinese), SHIFT_JIS (Japanese), EUC-KR (Korean), and UTF-8 without a BOM.
  4. Single-byte encodings, where each character is represented by one byte. Examples: KOI8-R (Russian), windows-1255 (Hebrew), and TIS-620 (Thai).
  5. windows-1252, which is used primarily on Microsoft Windows; its subset, ISO-8859-1 is widely used for legacy 8-bit-encoded text. chardet, like many encoding detectors, defaults to guessing this encoding when no other can be reliably established.

UTF-n with a BOM

If the text starts with a BOM, we can reasonably assume that the text is encoded in UTF-8, UTF-16, or UTF-32. (The BOM will tell us exactly which one; that’s what it’s for.) This is handled inline in UniversalDetector, which returns the result immediately without any further processing.

Escaped encodings

If the text contains a recognizable escape sequence that might indicate an escaped encoding, UniversalDetector creates an EscCharSetProber (defined in escprober.py) and feeds it the text.

EscCharSetProber creates a series of state machines, based on models of HZ-GB-2312, ISO-2022-CN, ISO-2022-JP, and ISO-2022-KR (defined in escsm.py). EscCharSetProber feeds the text to each of these state machines, one byte at a time. If any state machine ends up uniquely identifying the encoding, EscCharSetProber immediately returns the positive result to UniversalDetector, which returns it to the caller. If any state machine hits an illegal sequence, it is dropped and processing continues with the other state machines.

Multi-byte encodings

Assuming no BOM, UniversalDetector checks whether the text contains any high-bit characters. If so, it creates a series of “probers” for detecting multi-byte encodings, single-byte encodings, and as a last resort, windows-1252.

The multi-byte encoding prober, MBCSGroupProber (defined in mbcsgroupprober.py), is really just a shell that manages a group of other probers, one for each multi-byte encoding: Big5, GB2312, EUC-TW, EUC-KR, EUC-JP, SHIFT_JIS, and UTF-8. MBCSGroupProber feeds the text to each of these encoding-specific probers and checks the results. If a prober reports that it has found an illegal byte sequence, it is dropped from further processing (so that, for instance, any subsequent calls to UniversalDetector.feed will skip that prober). If a prober reports that it is reasonably confident that it has detected the encoding, MBCSGroupProber reports this positive result to UniversalDetector, which reports the result to the caller.

Most of the multi-byte encoding probers are inherited from MultiByteCharSetProber (defined in mbcharsetprober.py), and simply hook up the appropriate state machine and distribution analyzer and let MultiByteCharSetProber do the rest of the work. MultiByteCharSetProber runs the text through the encoding-specific state machine, one byte at a time, to look for byte sequences that would indicate a conclusive positive or negative result. At the same time, MultiByteCharSetProber feeds the text to an encoding-specific distribution analyzer.

The distribution analyzers (each defined in chardistribution.py) use language-specific models of which characters are used most frequently. Once MultiByteCharSetProber has fed enough text to the distribution analyzer, it calculates a confidence rating based on the number of frequently-used characters, the total number of characters, and a language-specific distribution ratio. If the confidence is high enough, MultiByteCharSetProber returns the result to MBCSGroupProber, which returns it to UniversalDetector, which returns it to the caller.

The case of Japanese is more difficult. Single-character distribution analysis is not always sufficient to distinguish between EUC-JP and SHIFT_JIS, so the SJISProber (defined in sjisprober.py) also uses 2-character distribution analysis. SJISContextAnalysis and EUCJPContextAnalysis (both defined in jpcntx.py and both inheriting from a common JapaneseContextAnalysis class) check the frequency of Hiragana syllabary characters within the text. Once enough text has been processed, they return a confidence level to SJISProber, which checks both analyzers and returns the higher confidence level to MBCSGroupProber.

Single-byte encodings

The single-byte encoding prober, SBCSGroupProber (defined in sbcsgroupprober.py), is also just a shell that manages a group of other probers, one for each combination of single-byte encoding and language: windows-1251, KOI8-R, ISO-8859-5, MacCyrillic, IBM855, and IBM866 (Russian); ISO-8859-7 and windows-1253 (Greek); ISO-8859-5 and windows-1251 (Bulgarian); ISO-8859-2 and windows-1250 (Hungarian); TIS-620 (Thai); windows-1255 and ISO-8859-8 (Hebrew).

SBCSGroupProber feeds the text to each of these encoding+language-specific probers and checks the results. These probers are all implemented as a single class, SingleByteCharSetProber (defined in sbcharsetprober.py), which takes a language model as an argument. The language model defines how frequently different 2-character sequences appear in typical text. SingleByteCharSetProber processes the text and tallies the most frequently used 2-character sequences. Once enough text has been processed, it calculates a confidence level based on the number of frequently-used sequences, the total number of characters, and a language-specific distribution ratio.

Hebrew is handled as a special case. If the text appears to be Hebrew based on 2-character distribution analysis, HebrewProber (defined in hebrewprober.py) tries to distinguish between Visual Hebrew (where the source text actually stored “backwards” line-by-line, and then displayed verbatim so it can be read from right to left) and Logical Hebrew (where the source text is stored in reading order and then rendered right-to-left by the client). Because certain characters are encoded differently based on whether they appear in the middle of or at the end of a word, we can make a reasonable guess about direction of the source text, and return the appropriate encoding (windows-1255 for Logical Hebrew, or ISO-8859-8 for Visual Hebrew).

windows-1252

If UniversalDetector detects a high-bit character in the text, but none of the other multi-byte or single-byte encoding probers return a confident result, it creates a Latin1Prober (defined in latin1prober.py) to try to detect English text in a windows-1252 encoding. This detection is inherently unreliable, because English letters are encoded in the same way in many different encodings. The only way to distinguish windows-1252 is through commonly used symbols like smart quotes, curly apostrophes, copyright symbols, and the like. Latin1Prober automatically reduces its confidence rating to allow more accurate probers to win if at all possible.

chardet

chardet package

Submodules

chardet.big5freq module

chardet.big5prober module

class chardet.big5prober.Big5Prober[source]

Bases: chardet.mbcharsetprober.MultiByteCharSetProber

charset_name
language

chardet.chardetect module

chardet.chardistribution module

class chardet.chardistribution.Big5DistributionAnalysis[source]

Bases: chardet.chardistribution.CharDistributionAnalysis

get_order(byte_str)[source]
class chardet.chardistribution.CharDistributionAnalysis[source]

Bases: object

ENOUGH_DATA_THRESHOLD = 1024
MINIMUM_DATA_THRESHOLD = 3
SURE_NO = 0.01
SURE_YES = 0.99
feed(char, char_len)[source]

feed a character with known length

get_confidence()[source]

return confidence based on existing data

get_order(_)[source]
got_enough_data()[source]
reset()[source]

reset analyser, clear any state

class chardet.chardistribution.EUCJPDistributionAnalysis[source]

Bases: chardet.chardistribution.CharDistributionAnalysis

get_order(byte_str)[source]
class chardet.chardistribution.EUCKRDistributionAnalysis[source]

Bases: chardet.chardistribution.CharDistributionAnalysis

get_order(byte_str)[source]
class chardet.chardistribution.EUCTWDistributionAnalysis[source]

Bases: chardet.chardistribution.CharDistributionAnalysis

get_order(byte_str)[source]
class chardet.chardistribution.GB2312DistributionAnalysis[source]

Bases: chardet.chardistribution.CharDistributionAnalysis

get_order(byte_str)[source]
class chardet.chardistribution.JOHABDistributionAnalysis[source]

Bases: chardet.chardistribution.CharDistributionAnalysis

get_order(byte_str)[source]
class chardet.chardistribution.SJISDistributionAnalysis[source]

Bases: chardet.chardistribution.CharDistributionAnalysis

get_order(byte_str)[source]

chardet.charsetgroupprober module

class chardet.charsetgroupprober.CharSetGroupProber(lang_filter=None)[source]

Bases: chardet.charsetprober.CharSetProber

charset_name
feed(byte_str)[source]
get_confidence()[source]
language
reset()[source]

chardet.charsetprober module

class chardet.charsetprober.CharSetProber(lang_filter=None)[source]

Bases: object

SHORTCUT_THRESHOLD = 0.95
charset_name
feed(byte_str)[source]
static filter_high_byte_only(buf)[source]
static filter_international_words(buf)[source]

We define three types of bytes: alphabet: english alphabets [a-zA-Z] international: international characters [€-ÿ] marker: everything else [^a-zA-Z€-ÿ] The input buffer can be thought to contain a series of words delimited by markers. This function works to filter all words that contain at least one international character. All contiguous sequences of markers are replaced by a single space ascii character. This filter applies to all scripts which do not use English characters.

get_confidence()[source]
static remove_xml_tags(buf)[source]

Returns a copy of buf that retains only the sequences of English alphabet and high byte characters that are not between <> characters. This filter can be applied to all scripts which contain both English characters and extended ASCII characters, but is currently only used by Latin1Prober.

reset()[source]
state

chardet.codingstatemachine module

class chardet.codingstatemachine.CodingStateMachine(sm)[source]

Bases: object

A state machine to verify a byte sequence for a particular encoding. For each byte the detector receives, it will feed that byte to every active state machine available, one byte at a time. The state machine changes its state based on its previous state and the byte it receives. There are 3 states in a state machine that are of interest to an auto-detector:

START state: This is the state to start with, or a legal byte sequence
(i.e. a valid code point) for character has been identified.
ME state: This indicates that the state machine identified a byte sequence
that is specific to the charset it is designed for and that there is no other possible encoding which can contain this byte sequence. This will to lead to an immediate positive answer for the detector.
ERROR state: This indicates the state machine identified an illegal byte
sequence for that encoding. This will lead to an immediate negative answer for this encoding. Detector will exclude this encoding from consideration from here on.
get_coding_state_machine()[source]
get_current_charlen()[source]
language
next_state(c)[source]
reset()[source]

chardet.compat module

chardet.constants module

chardet.cp949prober module

class chardet.cp949prober.CP949Prober[source]

Bases: chardet.mbcharsetprober.MultiByteCharSetProber

charset_name
language

chardet.escprober module

class chardet.escprober.EscCharSetProber(lang_filter=None)[source]

Bases: chardet.charsetprober.CharSetProber

This CharSetProber uses a “code scheme” approach for detecting encodings, whereby easily recognizable escape or shift sequences are relied on to identify these encodings.

charset_name
feed(byte_str)[source]
get_confidence()[source]
language
reset()[source]

chardet.escsm module

chardet.eucjpprober module

class chardet.eucjpprober.EUCJPProber[source]

Bases: chardet.mbcharsetprober.MultiByteCharSetProber

charset_name
feed(byte_str)[source]
get_confidence()[source]
language
reset()[source]

chardet.euckrfreq module

chardet.euckrprober module

class chardet.euckrprober.EUCKRProber[source]

Bases: chardet.mbcharsetprober.MultiByteCharSetProber

charset_name
language

chardet.euctwfreq module

chardet.euctwprober module

class chardet.euctwprober.EUCTWProber[source]

Bases: chardet.mbcharsetprober.MultiByteCharSetProber

charset_name
language

chardet.gb2312freq module

chardet.gb2312prober module

class chardet.gb2312prober.GB2312Prober[source]

Bases: chardet.mbcharsetprober.MultiByteCharSetProber

charset_name
language

chardet.hebrewprober module

class chardet.hebrewprober.HebrewProber[source]

Bases: chardet.charsetprober.CharSetProber

FINAL_KAF = 234
FINAL_MEM = 237
FINAL_NUN = 239
FINAL_PE = 243
FINAL_TSADI = 245
LOGICAL_HEBREW_NAME = 'windows-1255'
MIN_FINAL_CHAR_DISTANCE = 5
MIN_MODEL_DISTANCE = 0.01
NORMAL_KAF = 235
NORMAL_MEM = 238
NORMAL_NUN = 240
NORMAL_PE = 244
NORMAL_TSADI = 246
VISUAL_HEBREW_NAME = 'ISO-8859-8'
charset_name
feed(byte_str)[source]
is_final(c)[source]
is_non_final(c)[source]
language
reset()[source]
set_model_probers(logical_prober, visual_prober)[source]
state

chardet.jisfreq module

chardet.jpcntx module

class chardet.jpcntx.EUCJPContextAnalysis[source]

Bases: chardet.jpcntx.JapaneseContextAnalysis

get_order(byte_str)[source]
class chardet.jpcntx.JapaneseContextAnalysis[source]

Bases: object

DONT_KNOW = -1
ENOUGH_REL_THRESHOLD = 100
MAX_REL_THRESHOLD = 1000
MINIMUM_DATA_THRESHOLD = 4
NUM_OF_CATEGORY = 6
feed(byte_str, num_bytes)[source]
get_confidence()[source]
get_order(_)[source]
got_enough_data()[source]
reset()[source]
class chardet.jpcntx.SJISContextAnalysis[source]

Bases: chardet.jpcntx.JapaneseContextAnalysis

charset_name
get_order(byte_str)[source]

chardet.langbulgarianmodel module

chardet.langcyrillicmodel module

chardet.langgreekmodel module

chardet.langhebrewmodel module

chardet.langhungarianmodel module

chardet.langthaimodel module

chardet.latin1prober module

class chardet.latin1prober.Latin1Prober[source]

Bases: chardet.charsetprober.CharSetProber

charset_name
feed(byte_str)[source]
get_confidence()[source]
language
reset()[source]

chardet.mbcharsetprober module

class chardet.mbcharsetprober.MultiByteCharSetProber(lang_filter=None)[source]

Bases: chardet.charsetprober.CharSetProber

charset_name
feed(byte_str)[source]
get_confidence()[source]
language
reset()[source]

chardet.mbcsgroupprober module

class chardet.mbcsgroupprober.MBCSGroupProber(lang_filter=None)[source]

Bases: chardet.charsetgroupprober.CharSetGroupProber

chardet.mbcssm module

chardet.sbcharsetprober module

class chardet.sbcharsetprober.SingleByteCharSetModel(charset_name, language, char_to_order_map, language_model, typical_positive_ratio, keep_ascii_letters, alphabet)

Bases: tuple

alphabet

Alias for field number 6

char_to_order_map

Alias for field number 2

charset_name

Alias for field number 0

keep_ascii_letters

Alias for field number 5

language

Alias for field number 1

language_model

Alias for field number 3

typical_positive_ratio

Alias for field number 4

class chardet.sbcharsetprober.SingleByteCharSetProber(model, is_reversed=False, name_prober=None)[source]

Bases: chardet.charsetprober.CharSetProber

NEGATIVE_SHORTCUT_THRESHOLD = 0.05
POSITIVE_SHORTCUT_THRESHOLD = 0.95
SAMPLE_SIZE = 64
SB_ENOUGH_REL_THRESHOLD = 1024
charset_name
feed(byte_str)[source]
get_confidence()[source]
language
reset()[source]

chardet.sbcsgroupprober module

class chardet.sbcsgroupprober.SBCSGroupProber[source]

Bases: chardet.charsetgroupprober.CharSetGroupProber

chardet.sjisprober module

class chardet.sjisprober.SJISProber[source]

Bases: chardet.mbcharsetprober.MultiByteCharSetProber

charset_name
feed(byte_str)[source]
get_confidence()[source]
language
reset()[source]

chardet.universaldetector module

Module containing the UniversalDetector detector class, which is the primary class a user of chardet should use.

author:Mark Pilgrim (initial port to Python)
author:Shy Shalom (original C code)
author:Dan Blanchard (major refactoring for 3.0)
author:Ian Cordasco
class chardet.universaldetector.UniversalDetector(lang_filter=31)[source]

Bases: object

The UniversalDetector class underlies the chardet.detect function and coordinates all of the different charset probers.

To get a dict containing an encoding and its confidence, you can simply run:

u = UniversalDetector()
u.feed(some_bytes)
u.close()
detected = u.result
ESC_DETECTOR = re.compile(b'(\x1b|~{)')
HIGH_BYTE_DETECTOR = re.compile(b'[\x80-\xff]')
ISO_WIN_MAP = {'iso-8859-1': 'Windows-1252', 'iso-8859-13': 'Windows-1257', 'iso-8859-2': 'Windows-1250', 'iso-8859-5': 'Windows-1251', 'iso-8859-6': 'Windows-1256', 'iso-8859-7': 'Windows-1253', 'iso-8859-8': 'Windows-1255', 'iso-8859-9': 'Windows-1254'}
MINIMUM_THRESHOLD = 0.2
WIN_BYTE_DETECTOR = re.compile(b'[\x80-\x9f]')
charset_probers
close()[source]

Stop analyzing the current document and come up with a final prediction.

Returns:The result attribute, a dict with the keys encoding, confidence, and language.
feed(byte_str)[source]

Takes a chunk of a document and feeds it through all of the relevant charset probers.

After calling feed, you can check the value of the done attribute to see if you need to continue feeding the UniversalDetector more data, or if it has made a prediction (in the result attribute).

Note

You should always call close when you’re done feeding in your document if done is not already True.

has_win_bytes
input_state
reset()[source]

Reset the UniversalDetector and all of its probers back to their initial states. This is called by __init__, so you only need to call this directly in between analyses of different documents.

chardet.utf8prober module

class chardet.utf8prober.UTF8Prober[source]

Bases: chardet.charsetprober.CharSetProber

ONE_CHAR_PROB = 0.5
charset_name
feed(byte_str)[source]
get_confidence()[source]
language
reset()[source]

Module contents

class chardet.UniversalDetector(lang_filter=31)[source]

Bases: object

The UniversalDetector class underlies the chardet.detect function and coordinates all of the different charset probers.

To get a dict containing an encoding and its confidence, you can simply run:

u = UniversalDetector()
u.feed(some_bytes)
u.close()
detected = u.result
ESC_DETECTOR = re.compile(b'(\x1b|~{)')
HIGH_BYTE_DETECTOR = re.compile(b'[\x80-\xff]')
ISO_WIN_MAP = {'iso-8859-1': 'Windows-1252', 'iso-8859-13': 'Windows-1257', 'iso-8859-2': 'Windows-1250', 'iso-8859-5': 'Windows-1251', 'iso-8859-6': 'Windows-1256', 'iso-8859-7': 'Windows-1253', 'iso-8859-8': 'Windows-1255', 'iso-8859-9': 'Windows-1254'}
MINIMUM_THRESHOLD = 0.2
WIN_BYTE_DETECTOR = re.compile(b'[\x80-\x9f]')
charset_probers
close()[source]

Stop analyzing the current document and come up with a final prediction.

Returns:The result attribute, a dict with the keys encoding, confidence, and language.
feed(byte_str)[source]

Takes a chunk of a document and feeds it through all of the relevant charset probers.

After calling feed, you can check the value of the done attribute to see if you need to continue feeding the UniversalDetector more data, or if it has made a prediction (in the result attribute).

Note

You should always call close when you’re done feeding in your document if done is not already True.

has_win_bytes
input_state
reset()[source]

Reset the UniversalDetector and all of its probers back to their initial states. This is called by __init__, so you only need to call this directly in between analyses of different documents.

chardet.detect(byte_str)[source]

Detect the encoding of the given byte string.

Parameters:byte_str (bytes or bytearray) – The byte sequence to examine.
chardet.detect_all(byte_str, ignore_threshold=False)[source]

Detect all the possible encodings of the given byte string.

Parameters:
  • byte_str (bytes or bytearray) – The byte sequence to examine.
  • ignore_threshold (bool) – Include encodings that are below UniversalDetector.MINIMUM_THRESHOLD in results.

Indices and tables