#FORMAT HTML TEXT TO CLEAN TEXT PYTHON LICENSE#

For example, different stopwords removal, stemming and lemmization might have huge impact on the accuracy of your models. When building NLP models, pre-processing your data is extremely important. In Data Science, 80% of time spent prepare data, 20% of time spent complain about need for prepare data.- Big Data Borat February 27, 2013 Tags=,Ġ File: html.py, Project: airy, View licenseįrom, I think it all start with one of my favorite tweets from 2013: If name = 'src' and (value.startswith('http:') or value.startswith('https:')): If name = 'href' and (value.startswith('http:') or value.startswith('https:') or value.startswith('#')): New_classes = ' '.join(sorted(set((' ')) & allowed_classes))Ĭontent = (content, encoding=str, method='html') # Sanitize CSS classes using the whitelist above. If cls in ('Heading1', 'Heading2', 'Heading3', 'Heading4', 'Heading5'): # demote the one we just made above for the title.

These seem to occur around the table of contents only. If 'href' in tag.attrib and ().startswith('mailto:'): # as well as email addresses of other people mentioned in the reports. # Scrub mailto: links, which have author emails, which we want to scrub,

If tag.tag = 'a' and tag.text = 'Print Version': # Older reports had a 'Print Version' link, which we can remove. # Older reports had an 'authorline' with author names, which we scrub by # Older reports had a 'titleline' class for the title.Ĭss_classes.add('Title') # so the h1 doesn't get demoted below # Some reports are invalid HTML with a whole doctype and html node inside Extract just the report content, dropping the CRS.gov header/footer. # The HTML file contains the entire HTML page from CRS.gov that the report was Uses: google.appengine._.force_unicodeįrom google.appengine._ import normalize_newlinesĠ File: process_incoming.py, Project: crs-reports-website, View licenseĭef clean_html(content_fn, out_fn, author_names):

#FORMAT HTML TEXT TO CLEAN TEXT PYTHON LICENSE#

S = HTMLSerializer(omit_optional_tags=False, **serializer_kwargs)Ġ File: html.py, Project: simian, View license Walker = treewalkers.getTreeWalker('dom') P = HTMLParser(tree=treebuilders.getTreeBuilder('dom'), **parser_kwargs) > clean_html('Foobar& oopsThis is a link') :param sanitize: Remove unwanted HTML tags and attributes. Takes an HTML fragment and processes it using html5lib to ensure that the HTML is well-formed. Warnings.warn('The clean_html function is deprecated and will be removed in Django 1.8.',Ġ File: html.py, Project: django-fluent-contents, View license # As clean_html is wrapped in allow_lazy, stacklevel 3 is more useful than 2. Text = normalize_newlines(force_text(text))Ġ File: html.py, Project: P圜lassLessons, View license Text = trailing_empty_content_re.sub('', text)Ġ File: html.py, Project: GAE-Bulk-Mailer, View license # Remove stuff like ' ', but only if it's at the bottom Text = hard_coded_bullets_re.sub(replace_p_tags, text) # Convert hard-coded bullets into HTML unordered lists. Text = link_target_attribute_re.sub('\1', text) # Remove all target='' attributes from tags. Text = normalize_newlines(force_unicode(text)) * Remove stuff like ' ', but only if it's at theįrom import normalize_newlines

* Convert hard-coded bullets into HTML unordered lists. * Remove extraneous HTML, such as presentational tags that open and * Remove all 'target' attributes from tags.

Format html text to clean text python