textblob-de

Release 0.4.4a1 (Changelog)

TextBlob is a Python (2 and 3) library for processing textual data. It is being developed by Steven Loria. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

`textblob-de` is the German language extension for TextBlob.

from textblob_de import TextBlobDE

text = '''
"Der Blob" macht in seiner unbekümmert-naiven Weise einfach nur Spass.
Er hat eben den gewissen Charme, bei dem auch die eher hölzerne Regie und
das konfuse Drehbuch nicht weiter stören.
'''

blob = TextBlobDE(text)
blob.tags           # [('Der', 'DT'), ('Blob', 'NN'), ('macht', 'VB'),
                    #  ('in', 'IN'), ('seiner', 'PRP$'), ...]

blob.noun_phrases   # WordList(['Der Blob', 'seiner unbekümmert-naiven Weise',
                    #           'den gewissen Charme', 'hölzerne Regie',
                    #           'konfuse Drehbuch'])


for sentence in blob.sentences:
    print(sentence.sentiment.polarity)
# 1.0
# 0.0

blob.translate(to="es")  # '" The Blob " hace a su manera ingenua...'

For a complete overview of TextBlob ‘s features, see documentation of the main TextBlob library.

The docs of the German language extension focus on additions/differences to TextBlob and provide a detailed API reference.

Guide

textblob-de README

textblob_de - latest PyPI version Travis-CI Documentation Status Number of PyPI downloads LICENSE info

German language support for TextBlob by Steven Loria.

This python package is being developed as a TextBlob Language Extension. See Extension Guidelines for details.

Features

  • NEW: Works with Python3.7
  • All directly accessible textblob_de classes (e.g. Sentence() or Word()) are initialized with default models for German
  • Properties or methods that do not yet work for German raise a NotImplementedError
  • German sentence boundary detection and tokenization (NLTKPunktTokenizer)
  • Consistent use of specified tokenizer for all tools (NLTKPunktTokenizer or PatternTokenizer)
  • Part-of-speech tagging (PatternTagger) with keyword include_punc=True (defaults to False)
  • Tagset conversion in PatternTagger with keyword tagset='penn'|'universal'|'stts' (defaults to penn)
  • Parsing (PatternParser) with all pattern keywords, plus pprint=True (defaults to False)
  • Noun Phrase Extraction (PatternParserNPExtractor)
  • Lemmatization (PatternParserLemmatizer)
  • Polarity detection (PatternAnalyzer) - Still EXPERIMENTAL, does not yet have information on subjectivity
  • Full pattern.text.de API support on Python3
  • Supports Python 2 and 3
  • See working features overview for details

Installing/Upgrading

$ pip install -U textblob-de
$ python -m textblob.download_corpora

Or the latest development release (apparently this does not always work on Windows see issues #1744/5 for details):

$ pip install -U git+https://github.com/markuskiller/textblob-de.git@dev
$ python -m textblob.download_corpora

Note

TextBlob will be installed/upgraded automatically when running pip install. The second line (python -m textblob.download_corpora) downloads/updates nltk corpora and language models used in TextBlob.

Usage

>>> from textblob_de import TextBlobDE as TextBlob
>>> text = '''Heute ist der 3. Mai 2014 und Dr. Meier feiert seinen 43. Geburtstag.
Ich muss unbedingt daran denken, Mehl, usw. für einen Kuchen einzukaufen. Aber leider
habe ich nur noch EUR 3.50 in meiner Brieftasche.'''
>>> blob = TextBlob(text)
>>> blob.sentences
[Sentence("Heute ist der 3. Mai 2014 und Dr. Meier feiert seinen 43. Geburtstag."),
 Sentence("Ich muss unbedingt daran denken, Mehl, usw. für einen Kuchen einzukaufen."),
 Sentence("Aber leider habe ich nur noch EUR 3.50 in meiner Brieftasche.")]
>>> blob.tokens
WordList(['Heute', 'ist', 'der', '3.', 'Mai', ...]
>>> blob.tags
[('Heute', 'RB'), ('ist', 'VB'), ('der', 'DT'), ('3.', 'LS'), ('Mai', 'NN'),
('2014', 'CD'), ...]
# Default: Only noun_phrases that consist of two or more meaningful parts are displayed.
# Not perfect, but a start (relies heavily on parser accuracy)
>>> blob.noun_phrases
WordList(['Mai 2014', 'Dr. Meier', 'seinen 43. Geburtstag', 'Kuchen einzukaufen',
'meiner Brieftasche'])
>>> blob = TextBlob("Das Auto ist sehr schön.")
>>> blob.parse()
'Das/DT/B-NP/O Auto/NN/I-NP/O ist/VB/B-VP/O sehr/RB/B-ADJP/O schön/JJ/I-ADJP/O'
>>> from textblob_de import PatternParser
>>> blob = TextBlobDE("Das ist ein schönes Auto.", parser=PatternParser(pprint=True, lemmata=True))
>>> blob.parse()
      WORD   TAG    CHUNK   ROLE   ID     PNP    LEMMA

       Das   DT     -       -      -      -      das
       ist   VB     VP      -      -      -      sein
       ein   DT     NP      -      -      -      ein
   schönes   JJ     NP ^    -      -      -      schön
      Auto   NN     NP ^    -      -      -      auto
         .   .      -       -      -      -      .
>>> from textblob_de import PatternTagger
>>> blob = TextBlob(text, pos_tagger=PatternTagger(include_punc=True))
[('Das', 'DT'), ('Auto', 'NN'), ('ist', 'VB'), ('sehr', 'RB'), ('schön', 'JJ'), ('.', '.')]
>>> blob = TextBlob("Das Auto ist sehr schön.")
>>> blob.sentiment
Sentiment(polarity=1.0, subjectivity=0.0)
>>> blob = TextBlob("Das ist ein hässliches Auto.")
>>> blob.sentiment
Sentiment(polarity=-1.0, subjectivity=0.0)

Warning

WORK IN PROGRESS: The German polarity lexicon contains only uninflected forms and there are no subjectivity scores yet. As of version 0.2.3, lemmatized word forms are submitted to the PatternAnalyzer, increasing the accuracy of polarity values. New in version 0.2.7: return type of .sentiment is now adapted to the main TextBlob library (:rtype: namedtuple).

>>> blob.words.lemmatize()
WordList(['das', 'sein', 'ein', 'hässlich', 'Auto'])
>>> from textblob_de.lemmatizers import PatternParserLemmatizer
>>> _lemmatizer = PatternParserLemmatizer()
>>> _lemmatizer.lemmatize("Das ist ein hässliches Auto.")
[('das', 'DT'), ('sein', 'VB'), ('ein', 'DT'), ('hässlich', 'JJ'), ('Auto', 'NN')]

Note

Make sure that you use unicode strings on Python2 if your input contains non-ascii characters (e.g. word = u"schön").

Access to pattern API in Python3

>>> from textblob_de.packages import pattern_de as pd
>>> print(pd.attributive("neugierig", gender=pd.FEMALE, role=pd.INDIRECT, article="die"))
neugierigen

Note

Alternatively, the path to textblob_de/ext can be added to the PYTHONPATH, which allows the use of pattern.de in almost the same way as described in its Documentation. The only difference is that you will have to prepend an underscore: from _pattern.de import .... This is a precautionary measure in case the pattern library gets native Python3 support in the future.

Documentation and API Reference

Requirements

  • Python >= 2.6 or >= 3.3

TODO

  • Planned Extensions
  • Additional PoS tagging options, e.g. NLTK tagging (NLTKTagger)
  • Improve noun phrase extraction (e.g. based on RFTagger output)
  • Improve sentiment analysis (find suitable subjectivity scores)
  • Improve functionality of Sentence() and Word() objects
  • Adapt more tests from the main TextBlob library (esp. for TextBlobDE() in test_blob.py)

License

MIT licensed. See the bundled LICENSE file for more details.

Thanks

Coded with Wing IDE (free open source developer license)

Python IDE for Python - wingware.com

Tutorial: Quickstart

Use the following line as your first import …

from textblob_de import TextBlobDE as TextBlob

… and follow the quickstart guide in the documentation of the main package (using German examples and starting with “Let’s create our first TextBlob”).

Advanced Usage: Overriding Models and the Blobber Class

Follow the Advanced Usage guide in the documentation of the main package (using German examples). The following minimal replacements are necessary in order to enable the use of the German default models:

Instead of: Use:
textblob textblob_de
TextBlob TextBlobDE
Blobber BlobberDE

Extensions

Planned extensions
Extension Purpose Status (in private repo)
textblob-rftagger wrapper class for RFTagger 95% completed
textblob-cmd command-line wrapper for TextBlob 50% completed
textblob-stanfordparser wrapper class for StanfordParser 25% completed
textblob-berkeleyparser wrapper class for BerkeleyParser 0% completed
textblob-sent-align sentence alignment for parallel TextBlobs 40% completed
textblob-converters various input and output conversions 20% completed

See also notes on Extensions in the documentation of the main package.

API Reference

Blob Classes

Wrappers for various units of text.

This includes the main TextBlobDE, Word, and WordList classes.

Whenever possible, classes are inherited from the main TextBlob library, but in many cases, the models for German have to be initialised here in textblob_de.blob, resulting in a lot of duplicate code. The main reason are the Word objects. If they are generated from an inherited class, they will use the English models (e.g. for pluralize/singularize) used in the main library.

Example usage:

>>> from textblob_de import TextBlobDE
>>> b = TextBlobDE("Einfach ist besser als kompliziert.")
>>> b.tags
[('Einfach', 'RB'), ('ist', 'VB'), ('besser', 'RB'), ('als', 'IN'), ('kompliziert', 'JJ')]
>>> b.noun_phrases
WordList([])
>>> b.words
WordList(['Einfach', 'ist', 'besser', 'als', 'kompliziert'])
class textblob_de.blob.BaseBlob(text, tokenizer=None, pos_tagger=None, np_extractor=None, analyzer=None, parser=None, classifier=None, clean_html=False)[source]

BaseBlob class initialised with German default models:

An abstract base class that all textblob classes will inherit from. Includes words, POS tag, NP, and word count properties. Also includes basic dunder and string methods for making objects like Python strings.

Parameters:
  • text (str) – A string.
  • tokenizer – (optional) A tokenizer instance. If None, defaults to NLTKPunktTokenizer().
  • np_extractor – (optional) An NPExtractor instance. If None, defaults to PatternParserNPExtractor().
  • pos_tagger – (optional) A Tagger instance. If None, defaults to PatternTagger.
  • analyzer – (optional) A sentiment analyzer. If None, defaults to PatternAnalyzer.
  • classifier – (optional) A classifier.

Changed in version 0.6.0: clean_html parameter deprecated, as it was in NLTK.

classify()[source]

Classify the blob using the blob’s classifier.

correct()[source]

Attempt to correct the spelling of a blob.

New in version 0.6.0: (textblob)

Return type:BaseBlob
detect_language()[source]

Detect the blob’s language using the Google Translate API.

Requires an internet connection.

Usage:

>>> b = TextBlob("bonjour")
>>> b.detect_language()
u'fr'
Language code reference:
https://developers.google.com/translate/v2/using_rest#language-params

New in version 0.5.0.

Return type:str
ends_with(suffix, start=0, end=9223372036854775807)

Returns True if the blob ends with the given suffix.

endswith(suffix, start=0, end=9223372036854775807)

Returns True if the blob ends with the given suffix.

find(sub, start=0, end=9223372036854775807)

Behaves like the built-in str.find() method. Returns an integer, the index of the first occurrence of the substring argument sub in the sub-string given by [start:end].

format(*args, **kwargs)

Perform a string formatting operation, like the built-in str.format(*args, **kwargs). Returns a blob object.

index(sub, start=0, end=9223372036854775807)

Like blob.find() but raise ValueError when the substring is not found.

join(iterable)

Behaves like the built-in str.join(iterable) method, except returns a blob object.

Returns a blob which is the concatenation of the strings or blobs in the iterable.

lower()

Like str.lower(), returns new object with all lower-cased characters.

ngrams(n=3)[source]

Return a list of n-grams (tuples of n successive words) for this blob.

Return type:List of WordLists
noun_phrases

Returns a list of noun phrases for this blob.

np_counts

Dictionary of noun phrase frequencies in this text.

parse(parser=None)[source]

Parse the text.

Parameters:parser – (optional) A parser instance. If None, defaults to this blob’s default parser.

New in version 0.6.0.

polarity

Return the polarity score as a float within the range [-1.0, 1.0]

Return type:float
pos_tags

Returns an list of tuples of the form (word, POS tag).

Example:

[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
        ('Thursday', 'NNP'), ('morning', 'NN')]
Return type:list of tuples
replace(old, new, count=9223372036854775807)

Return a new blob object with all the occurence of old replaced by new.

rfind(sub, start=0, end=9223372036854775807)

Behaves like the built-in str.rfind() method. Returns an integer, the index of he last (right-most) occurence of the substring argument sub in the sub-sequence given by [start:end].

rindex(sub, start=0, end=9223372036854775807)

Like blob.rfind() but raise ValueError when substring is not found.

sentiment

Return a tuple of form (polarity, subjectivity ) where polarity is a float within the range [-1.0, 1.0] and subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.

Return type:namedtuple of the form Sentiment(polarity, subjectivity)
sentiment_assessments

Return a tuple of form (polarity, subjectivity, assessments ) where polarity is a float within the range [-1.0, 1.0], subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective, and assessments is a list of polarity and subjectivity scores for the assessed tokens.

Return type:namedtuple of the form Sentiment(polarity, subjectivity, assessments)
split(sep=None, maxsplit=9223372036854775807)[source]

Behaves like the built-in str.split() except returns a WordList.

Return type:WordList
starts_with(prefix, start=0, end=9223372036854775807)

Returns True if the blob starts with the given prefix.

startswith(prefix, start=0, end=9223372036854775807)

Returns True if the blob starts with the given prefix.

strip(chars=None)

Behaves like the built-in str.strip([chars]) method. Returns an object with leading and trailing whitespace removed.

subjectivity

Return the subjectivity score as a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.

Return type:float
tags

Returns an list of tuples of the form (word, POS tag).

Example:

[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
        ('Thursday', 'NNP'), ('morning', 'NN')]
Return type:list of tuples
title()

Returns a blob object with the text in title-case.

tokenize(tokenizer=None)[source]

Return a list of tokens, using tokenizer.

Parameters:tokenizer – (optional) A tokenizer object. If None, defaults to this blob’s default tokenizer.
tokens

Return a list of tokens, using this blob’s tokenizer object (defaults to WordTokenizer).

translate(from_lang=None, to='de')[source]

Translate the blob to another language.

upper()

Like str.upper(), returns new object with all upper-cased characters.

word_counts

Dictionary of word frequencies in this text.

words

Return a list of word tokens. This excludes punctuation characters. If you want to include punctuation characters, access the tokens property.

Returns:A WordList of word tokens.
class textblob_de.blob.BlobberDE(tokenizer=None, pos_tagger=None, np_extractor=None, analyzer=None, parser=None, classifier=None)[source]

A factory for TextBlobs that all share the same tagger, tokenizer, parser, classifier, and np_extractor.

Usage:

>>> from textblob_de import BlobberDE
>>> from textblob_de.taggers import PatternTagger
>>> from textblob.tokenizers import PatternTokenizer
>>> tb = Blobber(pos_tagger=PatternTagger(), tokenizer=PatternTokenizer())
>>> blob1 = tb("Das ist ein Blob.")
>>> blob2 = tb("Dieser Blob benutzt die selben Tagger und Tokenizer.")
>>> blob1.pos_tagger is blob2.pos_tagger
True
Parameters:
  • text (str) – A string.
  • tokenizer – (optional) A tokenizer instance. If None, defaults to NLTKPunktTokenizer().
  • np_extractor – (optional) An NPExtractor instance. If None, defaults to PatternParserNPExtractor().
  • pos_tagger – (optional) A Tagger instance. If None, defaults to PatternTagger.
  • analyzer – (optional) A sentiment analyzer. If None, defaults to PatternAnalyzer.
  • classifier – (optional) A classifier.

New in version 0.4.0: (textblob)

class textblob_de.blob.Sentence(sentence, start_index=0, end_index=None, *args, **kwargs)[source]

A sentence within a TextBlob. Inherits from BaseBlob.

Parameters:
  • sentence – A string, the raw sentence.
  • start_index – An int, the index where this sentence begins in a TextBlob. If not given, defaults to 0.
  • end_index – An int, the index where this sentence ends in a TextBlob. If not given, defaults to the length of the sentence - 1.
classify()

Classify the blob using the blob’s classifier.

correct()

Attempt to correct the spelling of a blob.

New in version 0.6.0: (textblob)

Return type:BaseBlob
detect_language()

Detect the blob’s language using the Google Translate API.

Requires an internet connection.

Usage:

>>> b = TextBlob("bonjour")
>>> b.detect_language()
u'fr'
Language code reference:
https://developers.google.com/translate/v2/using_rest#language-params

New in version 0.5.0.

Return type:str
dict

The dict representation of this sentence.

end = None

The end index within a textBlob

end_index = None

The end index within a textBlob

ends_with(suffix, start=0, end=9223372036854775807)

Returns True if the blob ends with the given suffix.

endswith(suffix, start=0, end=9223372036854775807)

Returns True if the blob ends with the given suffix.

find(sub, start=0, end=9223372036854775807)

Behaves like the built-in str.find() method. Returns an integer, the index of the first occurrence of the substring argument sub in the sub-string given by [start:end].

format(*args, **kwargs)

Perform a string formatting operation, like the built-in str.format(*args, **kwargs). Returns a blob object.

index(sub, start=0, end=9223372036854775807)

Like blob.find() but raise ValueError when the substring is not found.

join(iterable)

Behaves like the built-in str.join(iterable) method, except returns a blob object.

Returns a blob which is the concatenation of the strings or blobs in the iterable.

lower()

Like str.lower(), returns new object with all lower-cased characters.

ngrams(n=3)

Return a list of n-grams (tuples of n successive words) for this blob.

Return type:List of WordLists
noun_phrases

Returns a list of noun phrases for this blob.

np_counts

Dictionary of noun phrase frequencies in this text.

parse(parser=None)

Parse the text.

Parameters:parser – (optional) A parser instance. If None, defaults to this blob’s default parser.

New in version 0.6.0.

polarity

Return the polarity score as a float within the range [-1.0, 1.0]

Return type:float
pos_tags

Returns an list of tuples of the form (word, POS tag).

Example:

[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
        ('Thursday', 'NNP'), ('morning', 'NN')]
Return type:list of tuples
replace(old, new, count=9223372036854775807)

Return a new blob object with all the occurence of old replaced by new.

rfind(sub, start=0, end=9223372036854775807)

Behaves like the built-in str.rfind() method. Returns an integer, the index of he last (right-most) occurence of the substring argument sub in the sub-sequence given by [start:end].

rindex(sub, start=0, end=9223372036854775807)

Like blob.rfind() but raise ValueError when substring is not found.

sentiment

Return a tuple of form (polarity, subjectivity ) where polarity is a float within the range [-1.0, 1.0] and subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.

Return type:namedtuple of the form Sentiment(polarity, subjectivity)
sentiment_assessments

Return a tuple of form (polarity, subjectivity, assessments ) where polarity is a float within the range [-1.0, 1.0], subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective, and assessments is a list of polarity and subjectivity scores for the assessed tokens.

Return type:namedtuple of the form Sentiment(polarity, subjectivity, assessments)
split(sep=None, maxsplit=9223372036854775807)

Behaves like the built-in str.split() except returns a WordList.

Return type:WordList
start = None

The start index within a TextBlob

start_index = None

The start index within a TextBlob

starts_with(prefix, start=0, end=9223372036854775807)

Returns True if the blob starts with the given prefix.

startswith(prefix, start=0, end=9223372036854775807)

Returns True if the blob starts with the given prefix.

strip(chars=None)

Behaves like the built-in str.strip([chars]) method. Returns an object with leading and trailing whitespace removed.

subjectivity

Return the subjectivity score as a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.

Return type:float
tags

Returns an list of tuples of the form (word, POS tag).

Example:

[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
        ('Thursday', 'NNP'), ('morning', 'NN')]
Return type:list of tuples
title()

Returns a blob object with the text in title-case.

tokenize(tokenizer=None)

Return a list of tokens, using tokenizer.

Parameters:tokenizer – (optional) A tokenizer object. If None, defaults to this blob’s default tokenizer.
tokens

Return a list of tokens, using this blob’s tokenizer object (defaults to WordTokenizer).

translate(from_lang=None, to='de')

Translate the blob to another language.

upper()

Like str.upper(), returns new object with all upper-cased characters.

word_counts

Dictionary of word frequencies in this text.

words

Return a list of word tokens. This excludes punctuation characters. If you want to include punctuation characters, access the tokens property.

Returns:A WordList of word tokens.
class textblob_de.blob.TextBlobDE(text, tokenizer=None, pos_tagger=None, np_extractor=None, analyzer=None, parser=None, classifier=None, clean_html=False)[source]

TextBlob class initialised with German default models:

Parameters:
  • text (str) – A string.
  • tokenizer – (optional) A tokenizer instance. If None, defaults to NLTKPunktTokenizer().
  • np_extractor – (optional) An NPExtractor instance. If None, defaults to PatternParserNPExtractor().
  • pos_tagger – (optional) A Tagger instance. If None, defaults to PatternTagger.
  • analyzer – (optional) A sentiment analyzer. If None, defaults to PatternAnalyzer.
  • classifier – (optional) A classifier.
classify()

Classify the blob using the blob’s classifier.

correct()

Attempt to correct the spelling of a blob.

New in version 0.6.0: (textblob)

Return type:BaseBlob
detect_language()

Detect the blob’s language using the Google Translate API.

Requires an internet connection.

Usage:

>>> b = TextBlob("bonjour")
>>> b.detect_language()
u'fr'
Language code reference:
https://developers.google.com/translate/v2/using_rest#language-params

New in version 0.5.0.

Return type:str
ends_with(suffix, start=0, end=9223372036854775807)

Returns True if the blob ends with the given suffix.

endswith(suffix, start=0, end=9223372036854775807)

Returns True if the blob ends with the given suffix.

find(sub, start=0, end=9223372036854775807)

Behaves like the built-in str.find() method. Returns an integer, the index of the first occurrence of the substring argument sub in the sub-string given by [start:end].

format(*args, **kwargs)

Perform a string formatting operation, like the built-in str.format(*args, **kwargs). Returns a blob object.

index(sub, start=0, end=9223372036854775807)

Like blob.find() but raise ValueError when the substring is not found.

join(iterable)

Behaves like the built-in str.join(iterable) method, except returns a blob object.

Returns a blob which is the concatenation of the strings or blobs in the iterable.

json

The json representation of this blob.

Changed in version 0.5.1: Made json a property instead of a method to restore backwards compatibility that was broken after version 0.4.0.

lower()

Like str.lower(), returns new object with all lower-cased characters.

ngrams(n=3)

Return a list of n-grams (tuples of n successive words) for this blob.

Return type:List of WordLists
noun_phrases

Returns a list of noun phrases for this blob.

np_counts

Dictionary of noun phrase frequencies in this text.

parse(parser=None)

Parse the text.

Parameters:parser – (optional) A parser instance. If None, defaults to this blob’s default parser.

New in version 0.6.0.

polarity

Return the polarity score as a float within the range [-1.0, 1.0]

Return type:float
pos_tags

Returns an list of tuples of the form (word, POS tag).

Example:

[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
        ('Thursday', 'NNP'), ('morning', 'NN')]
Return type:list of tuples
raw_sentences

List of strings, the raw sentences in the blob.

replace(old, new, count=9223372036854775807)

Return a new blob object with all the occurence of old replaced by new.

rfind(sub, start=0, end=9223372036854775807)

Behaves like the built-in str.rfind() method. Returns an integer, the index of he last (right-most) occurence of the substring argument sub in the sub-sequence given by [start:end].

rindex(sub, start=0, end=9223372036854775807)

Like blob.rfind() but raise ValueError when substring is not found.

sentences

Return list of Sentence objects.

sentiment

Return a tuple of form (polarity, subjectivity ) where polarity is a float within the range [-1.0, 1.0] and subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.

Return type:named tuple of the form Sentiment(polarity=0.0, subjectivity=0.0)
sentiment_assessments

Return a tuple of form (polarity, subjectivity, assessments ) where polarity is a float within the range [-1.0, 1.0], subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective, and assessments is a list of polarity and subjectivity scores for the assessed tokens.

Return type:namedtuple of the form Sentiment(polarity, subjectivity, assessments)
serialized

Returns a list of each sentence’s dict representation.

split(sep=None, maxsplit=9223372036854775807)

Behaves like the built-in str.split() except returns a WordList.

Return type:WordList
starts_with(prefix, start=0, end=9223372036854775807)

Returns True if the blob starts with the given prefix.

startswith(prefix, start=0, end=9223372036854775807)

Returns True if the blob starts with the given prefix.

strip(chars=None)

Behaves like the built-in str.strip([chars]) method. Returns an object with leading and trailing whitespace removed.

subjectivity

Return the subjectivity score as a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.

Return type:float
tags

Returns an list of tuples of the form (word, POS tag).

Example:

[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
        ('Thursday', 'NNP'), ('morning', 'NN')]
Return type:list of tuples
title()

Returns a blob object with the text in title-case.

to_json(*args, **kwargs)[source]

Return a json representation (str) of this blob. Takes the same arguments as json.dumps.

New in version 0.5.1: (textblob)

tokenize(tokenizer=None)

Return a list of tokens, using tokenizer.

Parameters:tokenizer – (optional) A tokenizer object. If None, defaults to this blob’s default tokenizer.
tokens

Return a list of tokens, using this blob’s tokenizer object (defaults to WordTokenizer).

translate(from_lang=None, to='de')

Translate the blob to another language.

upper()

Like str.upper(), returns new object with all upper-cased characters.

word_counts

Dictionary of word frequencies in this text.

words

Return a list of word tokens. This excludes punctuation characters. If you want to include punctuation characters, access the tokens property.

Returns:A WordList of word tokens.
class textblob_de.blob.Word(string, pos_tag=None)[source]

A simple word representation.

Includes methods for inflection, translation, and WordNet integration.

capitalize() → unicode

Return a capitalized version of S, i.e. make the first character have upper case and the rest lower case.

center(width[, fillchar]) → unicode

Return S centered in a Unicode string of length width. Padding is done using the specified fill character (default is a space)

correct()[source]

Correct the spelling of the word. Returns the word with the highest confidence using the spelling corrector.

New in version 0.6.0: (textblob)

count(sub[, start[, end]]) → int

Return the number of non-overlapping occurrences of substring sub in Unicode string S[start:end]. Optional arguments start and end are interpreted as in slice notation.

decode([encoding[, errors]]) → string or unicode

Decodes S using the codec registered for encoding. encoding defaults to the default encoding. errors may be given to set a different error handling scheme. Default is ‘strict’ meaning that encoding errors raise a UnicodeDecodeError. Other possible values are ‘ignore’ and ‘replace’ as well as any other name registered with codecs.register_error that is able to handle UnicodeDecodeErrors.

define(pos=None)[source]

Return a list of definitions for this word. Each definition corresponds to a synset for this word.

Parameters:pos – A part-of-speech tag to filter upon. If None, definitions for all parts of speech will be loaded.
Return type:List of strings

New in version 0.7.0: (textblob)

definitions

The list of definitions for this word. Each definition corresponds to a synset.

New in version 0.7.0: (textblob)

detect_language()[source]

Detect the word’s language using Google’s Translate API.

New in version 0.5.0: (textblob)

encode([encoding[, errors]]) → string or unicode

Encodes S using the codec registered for encoding. encoding defaults to the default encoding. errors may be given to set a different error handling scheme. Default is ‘strict’ meaning that encoding errors raise a UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors.

endswith(suffix[, start[, end]]) → bool

Return True if S ends with the specified suffix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. suffix can also be a tuple of strings to try.

expandtabs([tabsize]) → unicode

Return a copy of S where all tab characters are expanded using spaces. If tabsize is not given, a tab size of 8 characters is assumed.

find(sub[, start[, end]]) → int

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

format(*args, **kwargs) → unicode

Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified by braces (‘{‘ and ‘}’).

get_synsets(pos=None)[source]

Return a list of Synset objects for this word.

Parameters:pos – A part-of-speech tag to filter upon. If None, all synsets for all parts of speech will be loaded.
Return type:list of Synsets

New in version 0.7.0: (textblob)

index(sub[, start[, end]]) → int

Like S.find() but raise ValueError when the substring is not found.

isalnum() → bool

Return True if all characters in S are alphanumeric and there is at least one character in S, False otherwise.

isalpha() → bool

Return True if all characters in S are alphabetic and there is at least one character in S, False otherwise.

isdecimal() → bool

Return True if there are only decimal characters in S, False otherwise.

isdigit() → bool

Return True if all characters in S are digits and there is at least one character in S, False otherwise.

islower() → bool

Return True if all cased characters in S are lowercase and there is at least one cased character in S, False otherwise.

isnumeric() → bool

Return True if there are only numeric characters in S, False otherwise.

isspace() → bool

Return True if all characters in S are whitespace and there is at least one character in S, False otherwise.

istitle() → bool

Return True if S is a titlecased string and there is at least one character in S, i.e. upper- and titlecase characters may only follow uncased characters and lowercase characters only cased ones. Return False otherwise.

isupper() → bool

Return True if all cased characters in S are uppercase and there is at least one cased character in S, False otherwise.

join(iterable) → unicode

Return a string which is the concatenation of the strings in the iterable. The separator between elements is S.

lemma

Return the lemma of this word using Wordnet’s morphy function.

lemmatize(**kwargs)[source]

Return the lemma for a word using WordNet’s morphy function.

Parameters:pos – Part of speech to filter upon. If None, defaults to _wordnet.NOUN.

New in version 0.8.1: (textblob)

ljust(width[, fillchar]) → int

Return S left-justified in a Unicode string of length width. Padding is done using the specified fill character (default is a space).

lower() → unicode

Return a copy of the string S converted to lowercase.

lstrip([chars]) → unicode

Return a copy of the string S with leading whitespace removed. If chars is given and not None, remove characters in chars instead. If chars is a str, it will be converted to unicode before stripping

partition(sep) -> (head, sep, tail)

Search for the separator sep in S, and return the part before it, the separator itself, and the part after it. If the separator is not found, return S and two empty strings.

pluralize()[source]

Return the plural version of the word as a string.

replace(old, new[, count]) → unicode

Return a copy of S with all occurrences of substring old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.

rfind(sub[, start[, end]]) → int

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

rindex(sub[, start[, end]]) → int

Like S.rfind() but raise ValueError when the substring is not found.

rjust(width[, fillchar]) → unicode

Return S right-justified in a Unicode string of length width. Padding is done using the specified fill character (default is a space).

rpartition(sep) -> (head, sep, tail)

Search for the separator sep in S, starting at the end of S, and return the part before it, the separator itself, and the part after it. If the separator is not found, return two empty strings and S.

rsplit([sep[, maxsplit]]) → list of strings

Return a list of the words in S, using sep as the delimiter string, starting at the end of the string and working to the front. If maxsplit is given, at most maxsplit splits are done. If sep is not specified, any whitespace string is a separator.

rstrip([chars]) → unicode

Return a copy of the string S with trailing whitespace removed. If chars is given and not None, remove characters in chars instead. If chars is a str, it will be converted to unicode before stripping

singularize()[source]

Return the singular version of the word as a string.

spellcheck()[source]

Return a list of (word, confidence) tuples of spelling corrections.

Based on: Peter Norvig, “How to Write a Spelling Corrector” (http://norvig.com/spell-correct.html) as implemented in the pattern library.

New in version 0.6.0: (textblob)

split([sep[, maxsplit]]) → list of strings

Return a list of the words in S, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done. If sep is not specified or is None, any whitespace string is a separator and empty strings are removed from the result.

splitlines(keepends=False) → list of strings

Return a list of the lines in S, breaking at line boundaries. Line breaks are not included in the resulting list unless keepends is given and true.

startswith(prefix[, start[, end]]) → bool

Return True if S starts with the specified prefix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. prefix can also be a tuple of strings to try.

strip([chars]) → unicode

Return a copy of the string S with leading and trailing whitespace removed. If chars is given and not None, remove characters in chars instead. If chars is a str, it will be converted to unicode before stripping

swapcase() → unicode

Return a copy of S with uppercase characters converted to lowercase and vice versa.

synsets

The list of Synset objects for this Word.

Return type:list of Synsets

New in version 0.7.0: (textblob)

title() → unicode

Return a titlecased version of S, i.e. words start with title case characters, all remaining cased characters have lower case.

translate(from_lang=None, to='de')[source]

Translate the word to another language using Google’s Translate API.

New in version 0.5.0: (textblob)

upper() → unicode

Return a copy of S converted to uppercase.

zfill(width) → unicode

Pad a numeric string S with zeros on the left, to fill a field of the specified width. The string S is never truncated.

class textblob_de.blob.WordList(collection)[source]

A list-like collection of words.

append(obj)[source]

Append an object to end. If the object is a string, appends a.

Word object.

count(strg, case_sensitive=False, *args, **kwargs)[source]

Get the count of a word or phrase s within this WordList.

Parameters:
  • strg – The string to count.
  • case_sensitive – A boolean, whether or not the search is case-sensitive.
extend(iterable)[source]

Extend WordList by appending elements from iterable.

If an element is a string, appends a Word object.

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

insert()

L.insert(index, object) – insert object before index

lemmatize()[source]

Return the lemma of each word in this WordList.

Currently using NLTKPunktTokenizer() for all lemmatization tasks. This might cause slightly different tokenization results compared to the TextBlob.words property.

lower()[source]

Return a new WordList with each word lower-cased.

pluralize()[source]

Return the plural version of each word in this WordList.

pop([index]) → item -- remove and return item at index (default last).

Raises IndexError if list is empty or index is out of range.

remove()

L.remove(value) – remove first occurrence of value. Raises ValueError if the value is not present.

reverse()

L.reverse() – reverse IN PLACE

singularize()[source]

Return the single version of each word in this WordList.

sort()

L.sort(cmp=None, key=None, reverse=False) – stable sort IN PLACE; cmp(x, y) -> -1, 0, 1

upper()[source]

Return a new WordList with each word upper-cased.

Base Classes

Extensions to Abstract base classes in textblob.base

class textblob_de.base.BaseLemmatizer[source]

Abstract base class from which all Lemmatizer classes inherit. Descendant classes must implement a lemmatize(text) method that returns a WordList of Word object with updated lemma properties.

New in version 0.2.3: (textblob_de)

lemmatize(text)[source]

Return a list of (lemma, tag) tuples.

Tokenizers

Various tokenizer implementations.

class textblob_de.tokenizers.NLTKPunktTokenizer[source]

Tokenizer included in nltk.tokenize.punkt package.

This is the default tokenizer in textblob-de

PROs:

  • trained model available for German
  • deals with many abbreviations and common German tokenization problems oob

CONs:

  • not very flexible (model has to be re-trained on your own corpus)
itokenize(text, *args, **kwargs)

Return a generator that generates tokens “on-demand”.

New in version 0.6.0.

Return type:generator
sent_tokenize(**kwargs)[source]

NLTK’s sentence tokenizer (currently PunktSentenceTokenizer).

Uses an unsupervised algorithm to build a model for abbreviation words, collocations, and words that start sentences, then uses that to find sentence boundaries.

tokenize(text, include_punc=True, nested=False)[source]

Return a list of word tokens.

Parameters:
  • text – string of text.
  • include_punc – (optional) whether to include punctuation as separate tokens. Default to True.
  • nested – (optional) whether to return tokens as nested lists of sentences. Default to False.
word_tokenize(text, include_punc=True)[source]

The Treebank tokenizer uses regular expressions to tokenize text as in Penn Treebank.

It assumes that the text has already been segmented into sentences, e.g. using self.sent_tokenize().

This tokenizer performs the following steps:

  • split standard contractions, e.g. don't -> do n't and they'll -> they 'll
  • treat most punctuation characters as separate tokens
  • split off commas and single quotes, when followed by whitespace
  • separate periods that appear at the end of line

Source: NLTK’s docstring of TreebankWordTokenizer (accessed: 02/10/2014)

class textblob_de.tokenizers.PatternTokenizer[source]

Tokenizer included in pattern.de package.

PROs:

  • handling of emoticons
  • flexible implementations of abbreviations
  • can be adapted very easily

CONs:

  • ordinal numbers cause sentence breaks
  • indices of Sentence() objects cannot be computed
itokenize(text, *args, **kwargs)

Return a generator that generates tokens “on-demand”.

New in version 0.6.0.

Return type:generator
sent_tokenize(text, **kwargs)[source]

Returns a list of sentences.

Each sentence is a space-separated string of tokens (words). Handles common cases of abbreviations (e.g., etc., …). Punctuation marks are split from other words. Periods (or ?!) mark the end of a sentence. Headings without an ending period are inferred by line breaks.

tokenize(text, include_punc=True, nested=False)[source]

Return a list of word tokens.

Parameters:
  • text – string of text.
  • include_punc – (optional) whether to include punctuation as separate tokens. Default to True.
class textblob_de.tokenizers.SentenceTokenizer(tokenizer=None, *args, **kwargs)[source]

Generic sentence tokenization class, using tokenizer specified in TextBlobDE() instance.

Enables SentenceTokenizer().itokenize generator that would be lost otherwise.

Aim: Not to break core API of the main TextBlob library.

Parameters:tokenizer – (optional) A tokenizer instance. If None, defaults to NLTKPunktTokenizer().
itokenize(text, *args, **kwargs)

Return a generator that generates tokens “on-demand”.

New in version 0.6.0.

Return type:generator
sent_tokenize(text, **kwargs)[source]

Compatibility method to tokenizers included in textblob-de

tokenize(text, **kwargs)[source]

Return a list of word tokens.

Parameters:
  • text – string of text.
  • include_punc – (optional) whether to include punctuation as separate tokens. Default to True.
class textblob_de.tokenizers.WordTokenizer(tokenizer=None, *args, **kwargs)[source]

Generic word tokenization class, using tokenizer specified in TextBlobDE() instance.

You can also submit the tokenizer as keyword argument: WordTokenizer(tokenizer=NLTKPunktTokenizer())

Enables WordTokenizer().itokenize generator that would be lost otherwise.

Default: NLTKPunktTokenizer().word_tokenize(text, include_punc=True)

Aim: Not to break core API of the main TextBlob library.

Parameters:tokenizer – (optional) A tokenizer instance. If None, defaults to NLTKPunktTokenizer().
itokenize(text, *args, **kwargs)

Return a generator that generates tokens “on-demand”.

New in version 0.6.0.

Return type:generator
tokenize(text, include_punc=True, **kwargs)[source]

Return a list of word tokens.

Parameters:
  • text – string of text.
  • include_punc – (optional) whether to include punctuation as separate tokens. Default to True.
word_tokenize(text, include_punc=True)[source]

Compatibility method to tokenizers included in textblob-de

textblob_de.tokenizers.sent_tokenize(text, tokenizer=None)[source]

Convenience function for tokenizing sentences (not iterable).

If tokenizer is not specified, the default tokenizer NLTKPunktTokenizer() is used (same behaviour as in the main TextBlob library).

This function returns the sentences as a generator object.

textblob_de.tokenizers.word_tokenize(text, tokenizer=None, include_punc=True, *args, **kwargs)[source]

Convenience function for tokenizing text into words.

NOTE: NLTK’s word tokenizer expects sentences as input, so the text will be tokenized to sentences before being tokenized to words.

This function returns an itertools chain object (generator).

POS Taggers

Default taggers for German.

>>> from textblob_de.taggers import PatternTagger

or

>>> from textblob_de import PatternTagger
class textblob_de.taggers.PatternTagger(tokenizer=None, include_punc=False, encoding='utf-8', tagset=None)[source]

Tagger that uses the implementation in Tom de Smedt’s pattern library (http://www.clips.ua.ac.be/pattern).

Parameters:
  • tokenizer – (optional) A tokenizer instance. If None, defaults to PatternTokenizer().
  • include_punc – (optional) whether to include punctuation as separate tokens. Default to False.
  • encoding – (optional) Input string encoding. (Default utf-8)
  • tagset – (optional) Penn Treebank II (default) or (‘penn’|’universal’|’stts’).
tag(sentence, tokenize=True)[source]

Tag a string sentence.

Parameters:
  • or list sentence (str) – A string or a list of sentence strings.
  • tokenize – (optional) If False string has to be tokenized before (space separated string).

Noun Phrase Extractors

Various noun phrase extractor implementations.

# PatternParserNPExtractor().

class textblob_de.np_extractors.PatternParserNPExtractor(tokenizer=None)[source]

Extract noun phrases (NP) from PatternParser() output.

Very naïve and resource hungry approach:

  • get parser output
  • try to correct as many obvious parser errors as you can (e.g. eliminate wrongly tagged verbs)
  • filter insignificant words
Parameters:tokenizer – (optional) A tokenizer instance. If None, defaults to PatternTokenizer().
extract(text)[source]

Return a list of noun phrases (strings) for a body of text.

Parameters:text (str) – A string.

Sentiment Analyzers

German sentiment analysis implementations.

Main resource for de-sentiment.xml:

class textblob_de.sentiments.PatternAnalyzer(tokenizer=None, lemmatizer=None, lemmatize=True)[source]

Sentiment analyzer that uses the same implementation as the pattern library. Returns results as a tuple of the form:

(polarity, subjectivity)

RETURN_TYPE

Return type declaration

alias of Sentiment

analyze(text)[source]

Return the sentiment as a tuple of the form: (polarity, subjectivity)

Parameters:text (str) – A string.
kind = 'co'

Enhancement Issue #2 adapted from ‘textblob.en.sentiments.py’

class textblob_de.sentiments.Sentiment(path=u'', language=None, synset=None, confidence=None, **kwargs)[source]
annotate(word, pos=None, polarity=0.0, subjectivity=0.0, intensity=1.0, label=None)[source]

Annotates the given word with polarity, subjectivity and intensity scores, and optionally a semantic label (e.g., MOOD for emoticons, IRONY for “(!)”).

assessments(words=[], negation=True)[source]

Returns a list of (chunk, polarity, subjectivity, label)-tuples for the given list of words: where chunk is a list of successive words: a known word optionally preceded by a modifier (“very good”) or a negation (“not good”).

clear() → None. Remove all items from D.
copy() → a shallow copy of D
fromkeys(S[, v]) → New dict with keys from S and values equal to v.

v defaults to None.

get(k[, d]) → D[k] if k in D, else d. d defaults to None.
has_key(k) → True if D has a key k, else False
items() → list of D's (key, value) pairs, as 2-tuples
iteritems() → an iterator over the (key, value) items of D
iterkeys() → an iterator over the keys of D
itervalues() → an iterator over the values of D
keys() → list of D's keys
pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised

popitem() → (k, v), remove and return some (key, value) pair as a

2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
synset(id, pos=u'JJ')[source]

Returns a (polarity, subjectivity)-tuple for the given synset id. For example, the adjective “horrible” has id 193480 in WordNet: Sentiment.synset(193480, pos=”JJ”) => (-0.6, 1.0, 1.0).

update([E, ]**F) → None. Update D from dict/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → list of D's values
viewitems() → a set-like object providing a view on D's items
viewkeys() → a set-like object providing a view on D's keys
viewvalues() → an object providing a view on D's values

Parsers

Default parsers for German.

>>> from textblob_de.parsers import PatternParser

or

>>> from textblob_de import PatternParser
class textblob_de.parsers.PatternParser(tokenizer=None, tokenize=True, pprint=False, tags=True, chunks=True, relations=False, lemmata=False, encoding='utf-8', tagset=None)[source]

Parser that uses the implementation in Tom de Smedt’s pattern library. http://www.clips.ua.ac.be/pages/pattern-de#parser

Parameters:
  • tokenizer – (optional) A tokenizer instance. If None, defaults to PatternTokenizer().
  • tokenize – (optional) Split punctuation marks from words? (Default True)
  • pprint – (optional) Use pattern’s pprint function to display parse trees (Default False)
  • tags – (optional) Parse part-of-speech tags? (NN, JJ, …) (Default True)
  • chunks – (optional) Parse chunks? (NP, VP, PNP, …) (Default True)
  • relations – (optional) Parse chunk relations? (-SBJ, -OBJ, …) (Default False)
  • lemmata – (optional) Parse lemmata? (schönes => schön) (Default False)
  • encoding – (optional) Input string encoding. (Default utf-8)
  • tagset – (optional) Penn Treebank II (default) or (‘penn’|’universal’|’stts’).
parse(text)[source]

Parses the text.

pattern.de.parse(**kwargs) can be passed to the parser instance and are documented in the main docstring of PatternParser().

Parameters:text (str) – A string.
parsetree(text)[source]

Returns a parsed pattern Text object from the given string.

Classifiers (from TextBlob main package)

Various classifier implementations. Also includes basic feature extractor methods.

Example Usage:

>>> from textblob import TextBlob
>>> from textblob.classifiers import NaiveBayesClassifier
>>> train = [
...     ('I love this sandwich.', 'pos'),
...     ('This is an amazing place!', 'pos'),
...     ('I feel very good about these beers.', 'pos'),
...     ('I do not like this restaurant', 'neg'),
...     ('I am tired of this stuff.', 'neg'),
...     ("I can't deal with this", 'neg'),
...     ("My boss is horrible.", "neg")
... ]
>>> cl = NaiveBayesClassifier(train)
>>> cl.classify("I feel amazing!")
'pos'
>>> blob = TextBlob("The beer is good. But the hangover is horrible.", classifier=cl)
>>> for s in blob.sentences:
...     print(s)
...     print(s.classify())
...
The beer is good.
pos
But the hangover is horrible.
neg

New in version 0.6.0.

class textblob.classifiers.BaseClassifier(train_set, feature_extractor=<function basic_extractor>, format=None, **kwargs)[source]

Abstract classifier class from which all classifers inherit. At a minimum, descendant classes must implement a classify method and have a classifier property.

Parameters:
  • train_set – The training set, either a list of tuples of the form (text, classification) or a file-like object. text may be either a string or an iterable.
  • feature_extractor (callable) – A feature extractor function that takes one or two arguments: document and train_set.
  • format (str) – If train_set is a filename, the file format, e.g. "csv" or "json". If None, will attempt to detect the file format.
  • kwargs – Additional keyword arguments are passed to the constructor of the Format class used to read the data. Only applies when a file-like object is passed as train_set.

New in version 0.6.0.

classifier

The classifier object.

classify(text)[source]

Classifies a string of text.

extract_features(text)[source]

Extracts features from a body of text.

Return type:dictionary of features
labels()[source]

Returns an iterable containing the possible labels.

train(labeled_featureset)[source]

Trains the classifier.

class textblob.classifiers.DecisionTreeClassifier(train_set, feature_extractor=<function basic_extractor>, format=None, **kwargs)[source]

A classifier based on the decision tree algorithm, as implemented in NLTK.

Parameters:
  • train_set – The training set, either a list of tuples of the form (text, classification) or a filename. text may be either a string or an iterable.
  • feature_extractor – A feature extractor function that takes one or two arguments: document and train_set.
  • format – If train_set is a filename, the file format, e.g. "csv" or "json". If None, will attempt to detect the file format.

New in version 0.6.2.

accuracy(test_set, format=None)

Compute the accuracy on a test set.

Parameters:
  • test_set – A list of tuples of the form (text, label), or a file pointer.
  • format – If test_set is a filename, the file format, e.g. "csv" or "json". If None, will attempt to detect the file format.
classifier

The classifier.

classify(text)

Classifies the text.

Parameters:text (str) – A string of text.
extract_features(text)

Extracts features from a body of text.

Return type:dictionary of features
labels()

Return an iterable of possible labels.

nltk_class

alias of nltk.classify.decisiontree.DecisionTreeClassifier

pprint(*args, **kwargs)

Return a string containing a pretty-printed version of this decision tree. Each line in the string corresponds to a single decision tree node or leaf, and indentation is used to display the structure of the tree.

Return type:str
pretty_format(*args, **kwargs)[source]

Return a string containing a pretty-printed version of this decision tree. Each line in the string corresponds to a single decision tree node or leaf, and indentation is used to display the structure of the tree.

Return type:str
pseudocode(*args, **kwargs)[source]

Return a string representation of this decision tree that expresses the decisions it makes as a nested set of pseudocode if statements.

Return type:str
train(*args, **kwargs)

Train the classifier with a labeled feature set and return the classifier. Takes the same arguments as the wrapped NLTK class. This method is implicitly called when calling classify or accuracy methods and is included only to allow passing in arguments to the train method of the wrapped NLTK class.

New in version 0.6.2.

Return type:A classifier
update(new_data, *args, **kwargs)

Update the classifier with new training data and re-trains the classifier.

Parameters:new_data – New data as a list of tuples of the form (text, label).
class textblob.classifiers.MaxEntClassifier(train_set, feature_extractor=<function basic_extractor>, format=None, **kwargs)[source]

A maximum entropy classifier (also known as a “conditional exponential classifier”). This classifier is parameterized by a set of “weights”, which are used to combine the joint-features that are generated from a featureset by an “encoding”. In particular, the encoding maps each (featureset, label) pair to a vector. The probability of each label is then computed using the following equation:

                          dotprod(weights, encode(fs,label))
prob(fs|label) = ---------------------------------------------------
                 sum(dotprod(weights, encode(fs,l)) for l in labels)

Where dotprod is the dot product:

dotprod(a,b) = sum(x*y for (x,y) in zip(a,b))
accuracy(test_set, format=None)

Compute the accuracy on a test set.

Parameters:
  • test_set – A list of tuples of the form (text, label), or a file pointer.
  • format – If test_set is a filename, the file format, e.g. "csv" or "json". If None, will attempt to detect the file format.
classifier

The classifier.

classify(text)

Classifies the text.

Parameters:text (str) – A string of text.
extract_features(text)

Extracts features from a body of text.

Return type:dictionary of features
labels()

Return an iterable of possible labels.

nltk_class

alias of nltk.classify.maxent.MaxentClassifier

prob_classify(text)[source]

Return the label probability distribution for classifying a string of text.

Example:

>>> classifier = MaxEntClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> prob_dist.max()
'positive'
>>> prob_dist.prob("positive")
0.7
Return type:nltk.probability.DictionaryProbDist
train(*args, **kwargs)

Train the classifier with a labeled feature set and return the classifier. Takes the same arguments as the wrapped NLTK class. This method is implicitly called when calling classify or accuracy methods and is included only to allow passing in arguments to the train method of the wrapped NLTK class.

New in version 0.6.2.

Return type:A classifier
update(new_data, *args, **kwargs)

Update the classifier with new training data and re-trains the classifier.

Parameters:new_data – New data as a list of tuples of the form (text, label).
class textblob.classifiers.NLTKClassifier(train_set, feature_extractor=<function basic_extractor>, format=None, **kwargs)[source]

An abstract class that wraps around the nltk.classify module.

Expects that descendant classes include a class variable nltk_class which is the class in the nltk.classify module to be wrapped.

Example:

class MyClassifier(NLTKClassifier):
    nltk_class = nltk.classify.svm.SvmClassifier
accuracy(test_set, format=None)[source]

Compute the accuracy on a test set.

Parameters:
  • test_set – A list of tuples of the form (text, label), or a file pointer.
  • format – If test_set is a filename, the file format, e.g. "csv" or "json". If None, will attempt to detect the file format.
classifier

The classifier.

classify(text)[source]

Classifies the text.

Parameters:text (str) – A string of text.
extract_features(text)

Extracts features from a body of text.

Return type:dictionary of features
labels()[source]

Return an iterable of possible labels.

nltk_class = None

The NLTK class to be wrapped. Must be a class within nltk.classify

train(*args, **kwargs)[source]

Train the classifier with a labeled feature set and return the classifier. Takes the same arguments as the wrapped NLTK class. This method is implicitly called when calling classify or accuracy methods and is included only to allow passing in arguments to the train method of the wrapped NLTK class.

New in version 0.6.2.

Return type:A classifier
update(new_data, *args, **kwargs)[source]

Update the classifier with new training data and re-trains the classifier.

Parameters:new_data – New data as a list of tuples of the form (text, label).
class textblob.classifiers.NaiveBayesClassifier(train_set, feature_extractor=<function basic_extractor>, format=None, **kwargs)[source]

A classifier based on the Naive Bayes algorithm, as implemented in NLTK.

Parameters:
  • train_set – The training set, either a list of tuples of the form (text, classification) or a filename. text may be either a string or an iterable.
  • feature_extractor – A feature extractor function that takes one or two arguments: document and train_set.
  • format – If train_set is a filename, the file format, e.g. "csv" or "json". If None, will attempt to detect the file format.

New in version 0.6.0.

accuracy(test_set, format=None)

Compute the accuracy on a test set.

Parameters:
  • test_set – A list of tuples of the form (text, label), or a file pointer.
  • format – If test_set is a filename, the file format, e.g. "csv" or "json". If None, will attempt to detect the file format.
classifier

The classifier.

classify(text)

Classifies the text.

Parameters:text (str) – A string of text.
extract_features(text)

Extracts features from a body of text.

Return type:dictionary of features
informative_features(*args, **kwargs)[source]

Return the most informative features as a list of tuples of the form (feature_name, feature_value).

Return type:list
labels()

Return an iterable of possible labels.

nltk_class

alias of nltk.classify.naivebayes.NaiveBayesClassifier

prob_classify(text)[source]

Return the label probability distribution for classifying a string of text.

Example:

>>> classifier = NaiveBayesClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> prob_dist.max()
'positive'
>>> prob_dist.prob("positive")
0.7
Return type:nltk.probability.DictionaryProbDist
show_informative_features(*args, **kwargs)[source]

Displays a listing of the most informative features for this classifier.

Return type:None
train(*args, **kwargs)

Train the classifier with a labeled feature set and return the classifier. Takes the same arguments as the wrapped NLTK class. This method is implicitly called when calling classify or accuracy methods and is included only to allow passing in arguments to the train method of the wrapped NLTK class.

New in version 0.6.2.

Return type:A classifier
update(new_data, *args, **kwargs)

Update the classifier with new training data and re-trains the classifier.

Parameters:new_data – New data as a list of tuples of the form (text, label).
class textblob.classifiers.PositiveNaiveBayesClassifier(positive_set, unlabeled_set, feature_extractor=<function contains_extractor>, positive_prob_prior=0.5, **kwargs)[source]

A variant of the Naive Bayes Classifier that performs binary classification with partially-labeled training sets, i.e. when only one class is labeled and the other is not. Assuming a prior distribution on the two labels, uses the unlabeled set to estimate the frequencies of the features.

Example usage:

>>> from text.classifiers import PositiveNaiveBayesClassifier
>>> sports_sentences = ['The team dominated the game',
...                   'They lost the ball',
...                   'The game was intense',
...                   'The goalkeeper catched the ball',
...                   'The other team controlled the ball']
>>> various_sentences = ['The President did not comment',
...                        'I lost the keys',
...                        'The team won the game',
...                        'Sara has two kids',
...                        'The ball went off the court',
...                        'They had the ball for the whole game',
...                        'The show is over']
>>> classifier = PositiveNaiveBayesClassifier(positive_set=sports_sentences,
...                                           unlabeled_set=various_sentences)
>>> classifier.classify("My team lost the game")
True
>>> classifier.classify("And now for something completely different.")
False
Parameters:
  • positive_set – A collection of strings that have the positive label.
  • unlabeled_set – A collection of unlabeled strings.
  • feature_extractor – A feature extractor function.
  • positive_prob_prior – A prior estimate of the probability of the label True.

New in version 0.7.0.

accuracy(test_set, format=None)

Compute the accuracy on a test set.

Parameters:
  • test_set – A list of tuples of the form (text, label), or a file pointer.
  • format – If test_set is a filename, the file format, e.g. "csv" or "json". If None, will attempt to detect the file format.
classifier

The classifier.

classify(text)

Classifies the text.

Parameters:text (str) – A string of text.
extract_features(text)

Extracts features from a body of text.

Return type:dictionary of features
labels()

Return an iterable of possible labels.

train(*args, **kwargs)[source]

Train the classifier with a labeled and unlabeled feature sets and return the classifier. Takes the same arguments as the wrapped NLTK class. This method is implicitly called when calling classify or accuracy methods and is included only to allow passing in arguments to the train method of the wrapped NLTK class.

Return type:A classifier
update(new_positive_data=None, new_unlabeled_data=None, positive_prob_prior=0.5, *args, **kwargs)[source]

Update the classifier with new data and re-trains the classifier.

Parameters:
  • new_positive_data – List of new, labeled strings.
  • new_unlabeled_data – List of new, unlabeled strings.
textblob.classifiers.basic_extractor(document, train_set)[source]

A basic document feature extractor that returns a dict indicating what words in train_set are contained in document.

Parameters:
  • document – The text to extract features from. Can be a string or an iterable.
  • train_set (list) – Training data set, a list of tuples of the form (words, label) OR an iterable of strings.
textblob.classifiers.contains_extractor(document)[source]

A basic document feature extractor that returns a dict of words that the document contains.

Blobber

class textblob_de.blob.BlobberDE(tokenizer=None, pos_tagger=None, np_extractor=None, analyzer=None, parser=None, classifier=None)[source]

A factory for TextBlobs that all share the same tagger, tokenizer, parser, classifier, and np_extractor.

Usage:

>>> from textblob_de import BlobberDE
>>> from textblob_de.taggers import PatternTagger
>>> from textblob.tokenizers import PatternTokenizer
>>> tb = Blobber(pos_tagger=PatternTagger(), tokenizer=PatternTokenizer())
>>> blob1 = tb("Das ist ein Blob.")
>>> blob2 = tb("Dieser Blob benutzt die selben Tagger und Tokenizer.")
>>> blob1.pos_tagger is blob2.pos_tagger
True
Parameters:
  • text (str) – A string.
  • tokenizer – (optional) A tokenizer instance. If None, defaults to NLTKPunktTokenizer().
  • np_extractor – (optional) An NPExtractor instance. If None, defaults to PatternParserNPExtractor().
  • pos_tagger – (optional) A Tagger instance. If None, defaults to PatternTagger.
  • analyzer – (optional) A sentiment analyzer. If None, defaults to PatternAnalyzer.
  • classifier – (optional) A classifier.

New in version 0.4.0: (textblob)

__call__(text)[source]

Return a new TextBlob object with this Blobber’s np_extractor, pos_tagger, tokenizer, analyzer, and classifier.

Returns:A new TextBlob.
__init__(tokenizer=None, pos_tagger=None, np_extractor=None, analyzer=None, parser=None, classifier=None)[source]

x.__init__(…) initializes x; see help(type(x)) for signature

__repr__() <==> repr(x)[source]
__str__()

x.__repr__() <==> repr(x)

File Formats (from TextBlob main package)

File formats for training and testing data.

Includes a registry of valid file formats. New file formats can be added to the registry like so:

from textblob import formats

class PipeDelimitedFormat(formats.DelimitedFormat):
    delimiter = '|'

formats.register('psv', PipeDelimitedFormat)

Once a format has been registered, classifiers will be able to read data files with that format.

from textblob.classifiers import NaiveBayesAnalyzer

with open('training_data.psv', 'r') as fp:
    cl = NaiveBayesAnalyzer(fp, format='psv')
class textblob.formats.BaseFormat(fp, **kwargs)[source]

Interface for format classes. Individual formats can decide on the composition and meaning of **kwargs.

Parameters:fp (File) – A file-like object.

Changed in version 0.9.0: Constructor receives a file pointer rather than a file path.

classmethod detect(stream)[source]

Detect the file format given a filename. Return True if a stream is this file format.

Changed in version 0.9.0: Changed from a static method to a class method.

to_iterable()[source]

Return an iterable object from the data.

class textblob.formats.CSV(fp, **kwargs)[source]

CSV format. Assumes each row is of the form text,label.

Today is a good day,pos
I hate this car.,pos
classmethod detect(stream)

Return True if stream is valid.

to_iterable()

Return an iterable object from the data.

class textblob.formats.DelimitedFormat(fp, **kwargs)[source]

A general character-delimited format.

classmethod detect(stream)[source]

Return True if stream is valid.

to_iterable()[source]

Return an iterable object from the data.

class textblob.formats.JSON(fp, **kwargs)[source]

JSON format.

Assumes that JSON is formatted as an array of objects with text and label properties.

[
    {"text": "Today is a good day.", "label": "pos"},
    {"text": "I hate this car.", "label": "neg"}
]
classmethod detect(stream)[source]

Return True if stream is valid JSON.

to_iterable()[source]

Return an iterable object from the JSON data.

class textblob.formats.TSV(fp, **kwargs)[source]

TSV format. Assumes each row is of the form text      label.

classmethod detect(stream)

Return True if stream is valid.

to_iterable()

Return an iterable object from the data.

textblob.formats.detect(fp, max_read=1024)[source]

Attempt to detect a file’s format, trying each of the supported formats. Return the format class that was detected. If no format is detected, return None.

textblob.formats.get_registry()[source]

Return a dictionary of registered formats.

textblob.formats.register(name, format_class)[source]

Register a new format.

Parameters:
  • name (str) – The name that will be used to refer to the format, e.g. ‘csv’
  • format_class (type) – The format class to register.

Exceptions (from TextBlob main package)

textblob.exceptions.MissingCorpusException

alias of textblob.exceptions.MissingCorpusError

Project info

Changelog

0.4.4 (unreleased)

0.4.3 (03/01/2019)

  • Added support for Python3.7 (StopIteration --> return) Pull Request #18 (thanks @andrewmfiorillo)
  • Fixed tests for Google translation examples
  • Updated tox/Travis-CI config files to include latest Python & pypy versions
  • Updated sphinx_rtd_theme to version 0.4.2 to fix rendering problems on RTD
  • Updated setup.py publish commands, Makefile & Manifest.in to new PyPI (using twine)

0.4.2 (02/05/2015)

  • Removed dependency on NLTK, as it already is a TextBlob dependency
  • Temporary workaround for NLTK Issue #824 for tox/Travis-CI
  • (update 13/01/2015) NLTK Issue #824 fixed, workaround removed
  • Enabled pattern tagset conversion ('penn'|'universal'|'stts') for PatternTagger
  • Added tests for tagset conversion
  • Fixed test for Arabic translation example (Google translation has changed)
  • Added tests for lemmatizer
  • Bugfix: PatternAnalyzer no longer breaks on subsequent ocurrences of the same (word, tag) pairs on Python3 see comments to Pull Request #11
  • Bugfix/performance enhancement: Sentiment dictionary in PatternAnalyzer no longer reloaded for every sentence Pull Request #11 (thanks @Arttii)

0.4.1 (03/10/2014)

  • Docs hosted on RTD
  • Removed dependency on nltk’s depricated PunktWordTokenizer and replaced it with TreebankWordTokenizer see nltk/nltk#746 (comment) for details

0.4.0 (17/09/2014)

  • Fixed Issue #7 (restore textblob>=0.9.0 compatibility)
  • Depend on nltk3. Vendorized nltk was removed in textblob>=0.9.0
  • Fixed ImportError on Python2 (unicodecsv)

0.3.1 (29/08/2014)

  • Improved PatternParserNPExtractor (less false positives in verb filter)
  • Made sure that all keyword arguments with default None are checked with is not None
  • Fixed shortcut to _pattern.de in vendorized library
  • Added Makefile to facilitate development process
  • Added docs and API reference

0.3.0 (14/08/2014)

  • Fixed Issue #5 (text + space + period)

0.2.9 (14/08/2014)

  • Fixed tokenization in PatternParser (if initialized manually, punctuation was not always separated from words)
  • Improved handling of empty strings (Issue #3) and of strings containing single punctuation marks (Issue #4) in PatternTagger and PatternParser
  • Added tests for empty strings and for strings containing single punctuation marks

0.2.8 (14/08/2014)

0.2.7 (13/08/2014)

  • Fixed Issue #1 lemmatization of strings containing a forward slash (/)
  • Enhancement Issue #2 use the same rtype as textblob for sentiment detection.
  • Fixed tokenization in PatternParserLemmatizer

0.2.6 (04/08/2014)

  • Fixed MANIFEST.in for package data in sdist

0.2.5 (04/08/2014)

  • sdist is non-functional as important files are missing due to a misconfiguration in MANIFEST.in - does not affect wheels
  • Major internal refactoring (but no backwards-incompatible API changes) with the aim of restoring complete compatibility to original pattern>=2.6 library on Python2
  • Separation of textblob and pattern code
  • On Python2 the vendorized version of pattern.text.de is only used if original is not installed (same as nltk)
  • Made pattern.de.pprint function and all parser keywords accessible to customise parser output
  • Access to complete pattern.text.de API on Python2 and Python3 from textblob_de.packages import pattern_de as pd
  • tox passed on all major platforms (Win/Linux/OSX)

0.2.3 (26/07/2014)

  • Lemmatizer: PatternParserLemmatizer() extracts lemmata from Parser output
  • Improved polarity analysis through look-up of lemmatised word forms

0.2.2 (22/07/2014)

  • Option: Include punctuation in tags/pos_tags properties (b = TextBlobDE(text, tagger=PatternTagger(include_punc=True)))
  • Added BlobberDE() class initialized with German models
  • TextBlobDE(), Sentence(), WordList() and Word() classes are now all initialized with German models
  • Restored complete API compatibility with textblob.tokenizers module of the main TextBlob library

0.2.1 (20/07/2014)

  • Noun Phrase Extraction: PatternParserNPExtractor() extracts NPs from Parser output
  • Refactored the way TextBlobDE() passes on arguments and keyword arguments to individual tools
  • Backwards-incompatible: Deprecate parser_show_lemmata=True keyword in TextBlob(). Use parser=PatternParser(lemmata=True) instead.

0.2.0 (18/07/2014)

  • vastly improved tokenization (NLTKPunktTokenizer and PatternTokenizer with tests)
  • consistent use of specified tokenizer for all tools
  • TextBlobDE with initialized default models for German
  • Parsing (PatternParser) plus test_parsers.py
  • EXPERIMENTAL implementation of Polarity detection (PatternAnalyzer)
  • first attempt at extracting German Polarity clues into de-sentiment.xml
  • tox tests passing for py26, py27, py33 and py34

0.1.3 (09/07/2014)

  • First release on PyPI

0.1.0 - 0.1.2 (09/07/2014)

  • First release on github
  • A number of experimental releases for testing purposes
  • Adapted version badges, tests & travis-ci config
  • Code adapted from sample extension textblob-fr
  • Language specific linguistic resources copied from pattern-de

Credits

TextBlob Development Lead

textblob-de Author/Maintainer

Contributors

  • Hocdoc (Issues #1 - #5)
  • ups1974 (Issue #7)
  • caspar2d (Issue #8)
  • CJAnti (Issue #9)
  • retresco (Feature Request: enable tagset conversion in PatternTagger)
  • Arttii (Pull Request #11)
  • andrewmfiorillo (Pull Request #18, Support for Python 3.7)

LICENSE

Human readable generic MIT License

Copyright 2014-2019 Markus Killer

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.

Contributing guidelines

In General

  • PEP 8, when sensible.
  • Test ruthlessly. Write docs for new features.
  • Even more important than Test-Driven Development–Human-Driven Development.

In Particular

Questions, Feature Requests, Bug Reports, and Feedback…

…should all be reported on the Github Issue Tracker.

Setting Up for Local Development

  1. Fork textblob-de on Github.

    $ git clone https://github.com/markuskiller/textblob-de.git
    $ cd textblob-de
    
  2. (recommended) Create and activate virtual python environment.

    $ pip install -U virtualenv
    $ virtualenv tb-de
    $ <activate virtual environment>
    
  3. Install development requirements and run setup.py develop. (see Makefile help for overview of available make targets):

    $ make develop
    

make command

This project adopts the Makefile approach, proposed by Jeff Knupp in his blog post Open Sourcing a Python Project the Right Way .

On Linux/OSX the make command should work out-of-the-box:

$ make help

Shows all available tasks.

Using make on Windows

The two Makefile s in this project should work on all three major platforms. On Windows, make.exe included in the MinGW/msys distribution has been successfully tested. Once msys is installed on a Windows system, the path/to/msys/1.0/bin needs to be added to the PATH environment variable.

A good place to update the PATH variable are the Activate.ps1 or activate.bat scripts of a virtual python build environment, created using virtualenv (pip install virtualenv) or pyvenv (added to Python3.3’s standard library).

Windows PowerShell

Add the following line at the end of path\to\virtual\python\env\Scripts\Activate.ps1:

# Add msys binaries to PATH
$env:PATH = "path\to\MinGW\msys\1.0\bin;$env:PATH"

Windows cmd.exe

Add the following line at the end of path\to\virtual\python\env\Scripts\activate.bat:

# Add msys binaries to PATH
set "PATH=path\to\MinGW\msys\1.0\bin;%PATH%"

Now the make command should work as documented in $ make help.

Project Makefile

generated: 03 January 2019 - 23:17


   Please use 'make <target>' where where <target> is one of

   SETUP & CLEAN
   -------------

   install            run 'python setup.py install'
   uninstall          run 'pip uninstall <package>'
   develop            install links to source files in current Python environment
   reset-dev          uninstall all links and console scripts and make clean
   clean              remove all artifacts
   clean-build        remove build artifacts
   clean-docs         remove documentation build artifacts
   clean-pyc          remove Python file artifacts (except in 'ext')
   clean-test         remove test artifacts (e.g. 'htmlcov')
   clean-logs         remove log artifacts and place empty file in 'log_dir'

   TESTING
   -------

   autopep8           automatically correct 'pep8' violations
   lint               check style with 'flake8'
   test               run tests quickly with the default Python
   test-all           run tests on every Python version with tox
   coverage           check code coverage quickly with the default Python

   PUBLISHING
   ----------

   docs               generate Sphinx HTML documentation, including API docs
   docs-pdf           generate Sphinx HTML and PDF documentation, including API docs
   sdist              package
   publish            package and upload sdist and universal wheel to PyPI
   publish-test       package and upload sdist and universal wheel to TestPyPI
   register           update README.rst on PyPI
   push-github        push all changes to git repository on github.com
   push-bitbucket     push all changes to git repository on bitbucket.org
                           --> include commit message as M='your message'

   VARIABLES ACCESSIBLE FROM COMMAND-LINE
   --------------------------------------

   M='your message'   mandatory git commit message
   N='package name'   specify python package name (optional)
   O='open|xdg-open|start'
                           --> specify platform specific 'open' cmd (optional)
   P='path/to/python' specify python executable (optional)

Documentation Makefile

generated: 03 January 2019 - 23:17

   Please use `make <target>' where <target> is one of

   html       to make standalone HTML files
   dirhtml    to make HTML files named index.html in directories
   singlehtml to make a single large HTML file
   pickle     to make pickle files
   json       to make JSON files
   htmlhelp   to make HTML files and a HTML help project
   qthelp     to make HTML files and a qthelp project
   devhelp    to make HTML files and a Devhelp project
   epub       to make an epub
   latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter
   latexpdf   to make LaTeX files and run them through pdflatex
   latexpdfja to make LaTeX files and run them through platex/dvipdfmx
   text       to make text files
   man        to make manual pages
   texinfo    to make Texinfo files
   info       to make Texinfo files and run them through makeinfo
   gettext    to make PO message catalogs
   changes    to make an overview of all changed/added/deprecated items
   xml        to make Docutils-native XML files
   pseudoxml  to make pseudoxml-XML files for display purposes
   linkcheck  to check all external links for integrity
   doctest    to run all doctests embedded in the documentation (if enabled)