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A basic framework to scrap renting ads

Project description

This package provides an easy and maintenable way to build a Rentswatch scraper. Rentswatch is a cross-borders investigation that collects data on flat rents in Europe. Its scrapers mainly focus on classified ads.

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, 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 an 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 uses 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 Ad

Attributes

As seen above, every Ad attribute might be used as a Scraper attribute to declare which attribute extract.

Name Type Description
status String “listed” if needs more scraping, “scraped” if it’s done
site String Name of the website
createdAt DateTime Date the ad was first scraped
siteId String The unique ID from the site where it’s scrapped from
serviceCharge Float Extra costs (heating mostly)
baseRent Float Base costs (without heating)
totalRent Float Total cost
livingSpace Float Surface in square meters
pricePerSqm Float Price per square meter
furnished Bool True if the flat or house is furnished
realtor Bool True if realtor, n if rented by a physical person
realtorName Unicode The name of the realtor or person offering the flat
latitude Float Latitude
longitude Float Longitude
balcony Bool True if there is a balcony/terrasse
yearConstructed String The year the building was built
cellar Bool True if the flat comes with a cellar
parking Bool True if the flat comes with a parking or a garage
houseNumber String House Number in the street
street String Street name (incl. “street”)
zipCode String ZIP code
city Unicode City
lift Bool True if a lift is present
typeOfFlat String Type of flat (no typology)
noRooms String Number of rooms
floor String Floor the flat is at
garden Bool True if there is a garden
barrierFree Bool True if the flat is wheelchair accessible
country String Country, 2 letter code
sourceUrl String URL of the page

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.

Start a migration

Use Yoyo:

yoyo new ./migrations -m "Your migration's description"

And apply it:

yoyo apply --database mysql://user:password@host/db ./migrations

Project details


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