forked from varia/varia.website
133 lines
4.1 KiB
Plaintext
133 lines
4.1 KiB
Plaintext
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Metadata-Version: 2.1
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Name: beautifulsoup4
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Version: 4.9.3
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Summary: Screen-scraping library
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Home-page: http://www.crummy.com/software/BeautifulSoup/bs4/
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Author: Leonard Richardson
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Author-email: leonardr@segfault.org
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License: MIT
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Download-URL: http://www.crummy.com/software/BeautifulSoup/bs4/download/
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Platform: UNKNOWN
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Intended Audience :: Developers
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 2.7
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Classifier: Programming Language :: Python :: 3
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Classifier: Topic :: Text Processing :: Markup :: HTML
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Classifier: Topic :: Text Processing :: Markup :: XML
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Classifier: Topic :: Text Processing :: Markup :: SGML
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Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Description-Content-Type: text/markdown
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Requires-Dist: soupsieve (<2.0,>1.2) ; python_version < "3.0"
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Requires-Dist: soupsieve (>1.2) ; python_version >= "3.0"
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Provides-Extra: html5lib
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Requires-Dist: html5lib ; extra == 'html5lib'
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Provides-Extra: lxml
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Requires-Dist: lxml ; extra == 'lxml'
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Beautiful Soup is a library that makes it easy to scrape information
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from web pages. It sits atop an HTML or XML parser, providing Pythonic
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idioms for iterating, searching, and modifying the parse tree.
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# Quick start
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```
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>>> from bs4 import BeautifulSoup
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>>> soup = BeautifulSoup("<p>Some<b>bad<i>HTML")
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>>> print(soup.prettify())
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<html>
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<body>
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<p>
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Some
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<b>
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bad
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<i>
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HTML
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</i>
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</b>
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</p>
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</body>
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</html>
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>>> soup.find(text="bad")
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'bad'
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>>> soup.i
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<i>HTML</i>
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#
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>>> soup = BeautifulSoup("<tag1>Some<tag2/>bad<tag3>XML", "xml")
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#
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>>> print(soup.prettify())
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<?xml version="1.0" encoding="utf-8"?>
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<tag1>
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Some
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<tag2/>
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bad
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<tag3>
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XML
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</tag3>
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</tag1>
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```
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To go beyond the basics, [comprehensive documentation is available](http://www.crummy.com/software/BeautifulSoup/bs4/doc/).
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# Links
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* [Homepage](http://www.crummy.com/software/BeautifulSoup/bs4/)
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* [Documentation](http://www.crummy.com/software/BeautifulSoup/bs4/doc/)
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* [Discussion group](http://groups.google.com/group/beautifulsoup/)
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* [Development](https://code.launchpad.net/beautifulsoup/)
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* [Bug tracker](https://bugs.launchpad.net/beautifulsoup/)
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* [Complete changelog](https://bazaar.launchpad.net/~leonardr/beautifulsoup/bs4/view/head:/CHANGELOG)
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# Note on Python 2 sunsetting
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Since 2012, Beautiful Soup has been developed as a Python 2 library
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which is automatically converted to Python 3 code as necessary. This
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makes it impossible to take advantage of some features of Python
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3.
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For this reason, I plan to discontinue Beautiful Soup's Python 2
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support at some point after December 31, 2020: one year after the
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sunset date for Python 2 itself. Beyond that point, new Beautiful Soup
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development will exclusively target Python 3. Of course, older
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releases of Beautiful Soup, which support both versions, will continue
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to be available.
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# Supporting the project
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If you use Beautiful Soup as part of your professional work, please consider a
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[Tidelift subscription](https://tidelift.com/subscription/pkg/pypi-beautifulsoup4?utm_source=pypi-beautifulsoup4&utm_medium=referral&utm_campaign=readme).
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This will support many of the free software projects your organization
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depends on, not just Beautiful Soup.
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If you use Beautiful Soup for personal projects, the best way to say
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thank you is to read
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[Tool Safety](https://www.crummy.com/software/BeautifulSoup/zine/), a zine I
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wrote about what Beautiful Soup has taught me about software
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development.
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# Building the documentation
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The bs4/doc/ directory contains full documentation in Sphinx
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format. Run `make html` in that directory to create HTML
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documentation.
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# Running the unit tests
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Beautiful Soup supports unit test discovery from the project root directory:
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```
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$ nosetests
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```
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```
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$ python -m unittest discover -s bs4
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```
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If you checked out the source tree, you should see a script in the
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home directory called test-all-versions. This script will run the unit
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tests under Python 2, then create a temporary Python 3 conversion of
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the source and run the unit tests again under Python 3.
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