Metadata-Version: 1.1
Name: SerpScrap
Version: 0.11.1
Summary: 
    A python scraper to extract and analyze data from
    search engine result pages and urls.
    Extract data, like url, title, snippet
    of results or ratings for given keywords.
    
Home-page: https://github.com/ecoron/SerpScrap
Author: Ronald Schmidt
Author-email: ronald.schmidt@zu-web.de
License: MIT
Description: =========
        SerpScrap
        =========
        
        .. image:: https://img.shields.io/pypi/v/SerpScrap.svg
            :target: https://pypi.python.org/pypi/SerpScrap
        
        .. image:: https://readthedocs.org/projects/serpscrap/badge/?version=latest
            :target: http://serpscrap.readthedocs.io/en/latest/
            :alt: Documentation Status
        
        .. image:: https://travis-ci.org/ecoron/SerpScrap.svg?branch=master
            :target: https://travis-ci.org/ecoron/SerpScrap
        
        .. image:: https://img.shields.io/docker/pulls/ecoron/serpscrap.svg
            :target: https://hub.docker.com/r/ecoron/serpscrap
        
        SEO python scraper to extract and analyze data from major search engine serps or text content of any other url.
        Extract data like title, url, type, text- and richsnippet of searchresults for given keywords. detect ads, automated screenshots.
        It might be usefull for SEO and research tasks.
        
        
        Extract these result types
        --------------------------
        
        * ads_main - advertisements within regular search results
        * image - result from image search
        * news - news teaser within regular search results
        * results - standard search result
        * shopping - shopping teaser within regular search results
        
        For each result of a resultspage get
        ====================================
        
        * domain
        * rank
        * rich snippet
        * site links
        * snippet
        * title
        * type
        * url
        * visible url
        
        Also get a screenshot of each result page.
        You can also scrape the text content of each result url.
        It is also possible to save the results as CSV for future analytics.
        If required you can also use your own proxylist.
        
        
        Ressources
        ----------
        
        See http://serpscrap.readthedocs.io/en/latest/ for documentation.
        
        Source is available at https://github.com/ecoron/SerpScrap
        
        
        Install
        -------
        
        The easy way to do:
        
        .. code-block:: python
        
           pip uninstall SerpScrap -y
           pip install SerpScrap --upgrade
        
        More details in the `install`_ section of the documentation.
        
        
        Usage
        =====
        
        SerpScrap in your applications
        
        .. code-block:: python
          
          #!/usr/bin/python3
          # -*- coding: utf-8 -*-
          import pprint
          import serpscrap
          
          keywords = ['example']
          
          config = serpscrap.Config()
          config.set('scrape_urls', False)
          
          scrap = serpscrap.SerpScrap()
          scrap.init(config=config.get(), keywords=keywords)
         results = scrap.run()
          
          for result in results:
              pprint.pprint(result)
        
        More detailes in the `examples`_ section of the documentation.
        
        To avoid encode/decode issues use this command before you start using SerpScrap in your cli.
        
        .. code-block:: bash
        
           chcp 65001
           set PYTHONIOENCODING=utf-8
        
        
        .. image:: https://raw.githubusercontent.com/ecoron/SerpScrap/master/docs/logo.png
            :target: https://github.com/ecoron/SerpScrap
        
        Supported OS
        ------------
        
        * SerpScrap should work on Linux, Windows and Mac OS with installed Python >= 3.4
        * SerpScrap requieres lxml
        * Doesn't work on iOS
        
        Changes
        -------
        Notes about major changes between releases
        
        0.11.0
        ======
        
        * Chrome headless is now the default browser, usage of phantomJS is deprecated
        * chromedriver is installed on the first run (tested on Linux and Windows. Mac OS should also work)
        * behavior of scraping raw text contents from serp urls, and of course given urls, has changed
        * run scraping of serp results and contents at once
        * csv output format changed, now it's tab separated and quoted
        
        0.10.0
        ======
        
        * support for headless chrome, adjusted default time between scrapes
        
        0.9.0
        =====
        
        * result types added (news, shopping, image)
        * Image search is supported
        
        0.8.0
        =====
        
        * text processing tools removed.
        * less requirements
        
        
        References
        ----------
        
        SerpScrap is using `Chrome headless`_ and `lxml`_ to scrape serp results. For raw text contents of fetched URL's, it is using `beautifulsoup4`_ .
        SerpScrap also supports `PhantomJs`_ ,which is deprecated, a scriptable headless WebKit, which is installed automaticly on the first run (Linux, Windows).
        The scrapcore was based on `GoogleScraper`_ , an outdated project, and has many changes and improvemts.
        
        .. target-notes::
        
        .. _`install`: http://serpscrap.readthedocs.io/en/latest/install.html
        .. _`examples`: http://serpscrap.readthedocs.io/en/latest/examples.html
        .. _`Chrome headless`: http://chromedriver.chromium.org/
        .. _`lxml`: https://lxml.de/
        .. _`beautifulsoup4`: https://www.crummy.com/software/BeautifulSoup/
        .. _`PhantomJs`: https://github.com/ariya/phantomjs
        .. _`GoogleScraper`: https://github.com/NikolaiT/GoogleScraper
        
        
Keywords: seo scraper ad-detection scraping keywords
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Internet
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
