rentswatch-scraper 0.9.0

A basic framework to scrap renting ads

This package provides an easy and maintenable way to build a Rentswatch’s scraper. Rentswatch is a cross-borders investigation aiming to collect data around flat renting in Europe. Its scrapers mainly focus on adverts.

How to install

Install using pip…

pip install rentswatch-scraper

How to use

Let’s take a look at a quick example of using Rentswatch Scraper to build a simple model-backed scraper to collect data from a website.

First, you may import the package components to build your scraper:

#!/usr/bin/env python
from rentswatch_scraper.scraper import Scraper
from rentswatch_scraper.browser import geocode, convert
from rentswatch_scraper.fields import RegexField, ComputedField
from rentswatch_scraper import reporting

To factorize as much code as possible we created an abstract class that every scraper will implement. For the sake of simplicity we’ll use a dummy website as follow:

class DummyScraper(Scraper):
    # Those are the basic meta-properties that define the scraper behavior
    class Meta:
        country         = 'FR'
        site            = "dummy"
        baseUrl         = 'http://dummy.io'
        listUrl         = baseUrl + '/rent/city/paris/list.php'
        adBlockSelector = '.ad-page-link'

Without any further configuration, this scraper will start to collect ads from the list page of dummy.io. To find links to the ads, it will use the CSS selector .ad-page-link to get <a> markups and follow their href attributes.

We have now to teach the scraper how to extract key figures from the ad page.

class DummyScraper(Scraper):
    # HEADS UP: Meta declarations are hidden here
    # ...
    # ...

    # Extract data using a CSS Selector.
    realtorName = RegexField('.realtor-title')
    # Extract data using a CSS Selector and a Regex.
    serviceCharge = RegexField('.description-list', 'charges : (.*)\s€')
    # Extract data using a CSS Selector and a Regex.
    # This will throw a custom exception if the field is missing.
    livingSpace = RegexField('.description-list', 'surface :(\d*)', required=True, exception=reporting.SpaceMissingError)
    # Extract the value directly, without using a Regex
    totalRent = RegexField('.description-price', required=True, exception=reporting.RentMissingError)
    # Store this value as a private property (begining with a underscore).
    # It won't be saved in the database but it can be helpful as you we'll see.
    _address = RegexField('.description-address')

Every attribute will be saved as a Ad’s property, according to the Ad model.

Some properties may not be extractable from the HTML. You may need to use a custom function that received existing properties. For this reason we created a second field type named ComputedField. Since the properties order of declaration is recorded, we can use previously declared (and extracted) values to compute new ones.

class DummyScraper(Scraper):
    # ...
    # ...

    # Use existing properties `totalRent` and `livingSpace` as they were
    # extracted before this one.
    pricePerSqm = ComputedField(fn=lambda s, values: values["totalRent"] / values["livingSpace"])
    # This full exemple use private properties to find latitude and longitude.
    # To do so we use a buid-in function named `convert` that transforms an
    # address into a dictionary of coordinates.
    _latLng = ComputedField(fn=lambda s, values: geocode(values['_address'], 'FRA') )
    # Gets a the dictionary field we want.
    latitude = ComputedField(fn=lambda s, values: values['_latLng']['lat'])
    longitude = ComputedField(fn=lambda s, values: values['_latLng']['lng'])

All you need to do now is to create an instance of your class and run the scraper.

# When you script is executed directly
if __name__ == "__main__":
  dummyScraper = DummyScraper()
  dummyScraper.run()

API Doc

class Scraper

Methods

The Scraper class defines a lot of method that we encourage you to redefine in order to have the full control of your scraper behavior.

Name	Description
extract_ad	Extract ads list from a page’s soup.
fail	Print out an error message.
fetch_ad	Fetch a single ad page from the target website then create Ad instances by calling èxtract_ad.
fetch_series	Fetch a single list page from the target website then fetch an ad by calling fetch_ad.
find_ad_blocks	Extract ad block from a page list. Called within fetch_series.
get_ad_href	Extract a href attribute from an ad block. . Called within fetch_series.
get_ad_id	Extract a siteId from an ad block. Called within fetch_series.
get_fields	Used internally to generate a list of property to extract from the ad.
get_series	Fetch a list page from the target website.
has_issue	True if we met issues with this ad before.
is_scraped	True if we already scraped this ad before.
ok	Print out an success message.
prepare	Just before saving the values.
run	Run the scrapper.
transform_page	Transform HTML content of the series page before parsing it.