Scrapple

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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.

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 :

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 :

Attribute selectors :

Tutorials

[For a more detailed tutorial, check out the tutorial in the documentation]

In this simple example for using Scrapple, we'll extract NBA player information from the ESPN website.

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.

{
    "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 :

{

    "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 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 and Harish Balakrishnan.