scrapple 0.3.0
A framework for creating web content extractors
License: MIT
Keywords: scrapple,web,content,scraper,crawler,scraping,crawling,website,data,scrapy

Scrapple
========

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`Scrapple <http://scrappleapp.github.io/scrapple>`__ is a framework for
creating web scrapers and web crawlers according to a key-value based
configuration file. It provides a command line interface to run the
script on a given JSON-based configuration input, as well as a web
interface to provide the necessary input.

The primary goal of Scrapple is to abstract the process of designing web
content extractors. The focus is laid on what to extract, rather than
how to do it. The user-specified configuration file contains selector
expressions (XPath expressions or CSS selectors) and the attribute to be
selected. Scrapple does the work of running this extractor, without the
user worrying about writing a program. Scrapple can also be used to
generate a Python script that implements the desired extractor.

Installation
------------

You can install Scrapple by using

::

    $ sudo apt-get install libxml2-dev libxslt-dev python-dev lib32z1-dev
    $ pip install scrapple

Otherwise, you could clone this repository and install the package.

::

    $ git clone http://github.com/scrappleapp/scrapple scrapple
    $ cd scrapple
    $ pip install -r requirements.txt
    $ python setup.py install

How to use Scrapple
-------------------

Scrapple provides 4 commands to create and implement extractors.

-  `genconfig <http://scrapple.readthedocs.org/en/latest/framework/commands.html#genconfig>`__
-  `generate <http://scrapple.readthedocs.org/en/latest/framework/commands.html#generate>`__
-  `run <http://scrapple.readthedocs.org/en/latest/framework/commands.html#run>`__
-  `web <http://scrapple.readthedocs.org/en/latest/framework/commands.html#web>`__

Scrapple implements the desired extractor on the basis of the
user-specified configuration file. There are guidelines regarding how to
write these configuration files.

The configuration file is the basic specification of the extractor
required. It contains the URL for the web page to be loaded, the
selector expressions for the data to be extracted and in the case of
crawlers, the selector expression for the links to be crawled through.

The keys used in the configuration file are :

-  **project\_name** : Specifies the name of the project with which the
   configuration file is associated.
-  **selector\_type** : Specifies the type of selector expressions used.
   This could be "xpath" or "css".
-  **scraping** : Specifies parameters for the extractor to be created.

   -  **url** : Specifies the URL of the base web page to be loaded.

   -  **data** : Specifies a list of selectors for the data to be
      extracted.

      -  **selector** : Specifies the selector expression.
      -  **attr** : Specifies the attribute to be extracted from the
         result of the selector expression.
      -  **field** : Specifies the field name under which this data is
         to stored.
      -  **default** : Specifies the default value to be used if the
         selector expression fails.

   -  **table** : Specifies a description for scraping tabular data.

      -  **table\_type** : Specifies the type of table ("rows" or
         "columns"). This determines the type of table to be extracted.
         A row extraction is when there is a single row to be extracted
         and mapped to a set of headers. A column extraction is when a
         set of rows have to be extracted, giving a list of header-value
         mappings.
      -  **header** : Specifies the headers to be used for the table.
         This can be a list of headers, or a selector that gives the
         list of headers.
      -  **prefix** : Specifies a prefix to be added to each header.
      -  **suffix** : Specifies a suffix to be added to each header.
      -  **selector** : Specifies the selector for the data. For row
         extraction, this is a selector that gives the row to be
         extracted. For column extraction, this is a list of selectors
         for each column.
      -  **attr** : Specifies the attribute to be extracted from the
         selected tag.
      -  **default** : Specifies the default value to be used if the
         selector does not return any data.

   -  **next** : Specifies the crawler implementation.

      -  **follow\_link** : Specifies the selector expression for the
         ``<a>`` tags to be crawled through.

The main objective of the configuration file is to specify extraction
rules in terms of selector expressions and the attribute to be
extracted. There are certain set forms of selector/attribute value pairs
that perform various types of content extraction.

Selector expressions :

-  CSS selector or XPath expressions that specify the tag to be
   selected.
-  "url" to take the URL of the current page on which extraction is
   being performed.

Attribute selectors :

-  "text" to extract the textual content from that tag.
-  "href", "src" etc., to extract any of the other attributes of the
   selected tag.

Tutorials
---------

[For a more detailed tutorial, check out the `tutorial in the
documentation <http://scrapple.readthedocs.org/en/latest/#experimentation-results>`__]

In this simple example for using Scrapple, we'll extract NBA player
information from `the ESPN website <http://espn.go.com/nba/teams>`__.

To first create the skeleton configuration file, we use the genconfig
command.

::

    $ scrapple genconfig nba http://espn.go.com/nba/teams --type=crawler --levels=2

This creates nba.json - a sample Scrapple configuration file for a
crawler, which uses XPath expressions as selectors. This can be edited
and the required follow link selector, data selectors and attributes can
be specified.

.. code:: javascript

    {
        "project_name": "nba",
        "selector_type": "xpath",
        "scraping": {
            "url": "http://espn.go.com/nba/teams",
            "data": [
                {
                    "field": "",
                    "selector": "",
                    "attr": "",
                    "default": ""
                }
            ],
            "next": [
                {
                    "follow_link": "//*[@class='mod-content']//a[3]",
                    "scraping": {
                        "data": [
                            {
                                "field": "team",
                                "selector": "//h2",
                                "attr": "text",
                                "default": "<no_team>"
                            }
                        ],
                        "next": [
                            {
                                "follow_link": "//*[@class='mod-content']/table[1]//tr[@class!='colhead']//a",
                                "scraping": {
                                    "data": [
                                        {
                                            "field": "name",
                                            "selector": "//h1",
                                            "attr": "text",
                                            "default": "<no_name>"
                                        },
                                        {
                                            "field": "headshot_link",
                                            "selector": "//*[@class='main-headshot']/img",
                                            "attr": "src",
                                            "default": "<no_image>"
                                        },
                                        {
                                            "field": "number & position",
                                            "selector": "//ul[@class='general-info']/li[1]",
                                            "attr": "text",
                                            "default": "<00> #<GFC>"
                                        }
                                    ],
                                    "table": [
                                        {
                                            "table_type": "rows",
                                            "header": "//div[@class='player-stats']//table//th",
                                            "prefix": "season_",
                                            "suffix": "",
                                            "selector": "//div[@class='player-stats']//table//tr[1]/td",
                                            "attr": "text",
                                            "default": ""
                                        },
                                        {
                                            "table_type": "rows",
                                            "header": "//div[@class='player-stats']//table//th",
                                            "prefix": "career_",
                                            "suffix": "",
                                            "selector": "//div[@class='player-stats']//table//tr[@class='career']/td",
                                            "attr": "text",
                                            "default": ""
                                        }
                                    ]
                                }
                            }
                        ]
                    }
                }
            ]
        }
    }

The extractor can be run using the run command -

::

    $ scrapple run nba nba_players -o json

This creates nba\_players.json which contains the extracted data. An
example snippet of this data :

.. code:: javascript

    {

        "project": "nba",
        "data": [

            # nba_players.json continues

            {
                "career_APG" : "9.9",
                "career_PER" : "",
                "career_PPG" : "18.6",
                "career_RPG" : "4.4",
                "headshot_link" : "http://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/2779.png&w=350&h=254",
                "name" : "Chris Paul",
                "number & position" : "#3 PG",
                "season_APG" : "9.2",
                "season_PER" : "23.49",
                "season_PPG" : "17.6",
                "season_RPG" : "3.5",
                "team" : "Los Angeles Clippers"
            },
            {
                "career_APG" : "3.6",
                "career_PER" : "",
                "career_PPG" : "20.3",
                "career_RPG" : "5.8",
                "headshot_link" : "http://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/662.png&w=350&h=254",
                "name" : "Paul Pierce",
                "number & position" : "#34 SF",
                "season_APG" : "0.9",
                "season_PER" : "7.55",
                "season_PPG" : "5.0",
                "season_RPG" : "2.6",
                "team" : "Los Angeles Clippers"
            },
            {
                "career_APG" : "2.9",
                "career_PER" : "",
                "career_PPG" : "3.7",
                "career_RPG" : "1.8",
                "headshot_link" : "http://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/4182.png&w=350&h=254",
                "name" : "Pablo Prigioni",
                "number & position" : "#9 PG",
                "season_APG" : "1.9",
                "season_PER" : "8.72",
                "season_PPG" : "2.3",
                "season_RPG" : "1.5",
                "team" : "Los Angeles Clippers"
            },
            {
                "career_APG" : "2.0",
                "career_PER" : "",
                "career_PPG" : "11.1",
                "career_RPG" : "1.9",
                "headshot_link" : "http://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/3024.png&w=350&h=254",
                "name" : "J.J. Redick",
                "number & position" : "#4 SG",
                "season_APG" : "1.6",
                "season_PER" : "18.10",
                "season_PPG" : "15.9",
                "season_RPG" : "1.5",
                "team" : "Los Angeles Clippers"
            },

            # nba_players.json continues
        ]

    }

The run command can also be used to create a CSV file with the extracted
data, using the --output\_type=csv argument.

The generate command can be used to generate a Python script that
implements this extractor. In essence, it replicates the execution of
the run command.

::

    $ scrapple generate nba nba_script -o json

This creates nba\_script.py, which extracts the required data and stores
the data in a JSON document.

Documentation
-------------

You can read the `complete documentation <http://scrapple.rtfd.org>`__
for an extensive coverage on the background behind Scrapple, a thorough
explanation on the Scrapple package implementation and a complete
coverage of tutorials on how to use Scrapple to run your scraper/crawler
jobs.

Authors
-------

Scrapple is maintained by `Alex Mathew <http://github.com/AlexMathew>`__
and `Harish Balakrishnan <http://github.com/harishb93>`__.

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History
=======

0.3.0 - 2016-09-23
------------------

* Set up table scraping parameters and execution
* Fix json configuration generation

0.2.6 - 2015-11-27
------------------

* Edit requirements

0.2.5 - 2015-05-28
------------------

* Add levels argument for genconfig command, to create crawler config files for a specific depth

0.2.4 - 2015-04-13
------------------

* Update documentation
* Minor fixes

0.2.3 - 2015-03-11
------------------

* Include implementation to use csv as the output format

0.2.2 - 2015-02-22
------------------

* Fix bug in generate script template

0.2.1 - 2015-02-21
------------------

* Update tests

0.2.0 - 2015-02-20
------------------

* Include implementation for ``scrapple run`` and ``scrapple generate`` for crawlers
* Modify web interface for editing scraper config files
* Revise skeleton configuration files

0.1.1 - 2015-02-10
------------------

* Release on PyPI with revisions
* Include web interface for editing scraper config files
* Modified implementations of certain functions

0.1.0 - 2015-02-04
------------------

* First release on PyPI