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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.

Project details


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