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A library that enables programmatic interaction with daft.ie. Daft.ie has nationwide coverage and contains about 80% of the total available properties in Ireland.

Project description

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A library that enables programmatic interaction with Daft.ie. Daft.ie has nationwide coverage and contains about 80% of the total available properties in Ireland.

Installation

Daftlistings is available on the Python Package Index (PyPI). You can install daftlistings using pip.

virtualenv env
source env/bin/activate
pip install daftlistings

To install the development version, run:

pip install https://github.com/AnthonyBloomer/daftlistings/archive/dev.zip

Usage

from daftlistings import Daft

daft = Daft()
listings = daft.search()

for listing in listings:
    print(listing.formalised_address)
    print(listing.daft_link)
    print(listing.price)

Examples

Get apartments to let in Dublin City that are between €1000 and €1500 and contact the advertiser of each listing.

from daftlistings import Daft, RentType

daft = Daft()

daft.set_county("Dublin City")
daft.set_listing_type(RentType.APARTMENTS)
daft.set_min_price(1000)
daft.set_max_price(1500)

listings = daft.search()

if len(listings) > 0:
    first = listings[0]

    contact = first.contact_advertiser(
        name="Jane Doe",
        contact_number="019202222",
        email="jane@example.com",
        message="Hi, I seen your listing on daft.ie and I would like to schedule a viewing."
    )

    if contact:
        print("Advertiser contacted")

You can sort the listings by price, distance, upcoming viewing or date using the SortType object. The SortOrder object allows you to sort the listings descending or ascending.

from daftlistings import Daft, SortOrder, SortType, RentType

daft = Daft()

daft.set_county("Dublin City")
daft.set_listing_type(RentType.ANY)
daft.set_sort_order(SortOrder.ASCENDING)
daft.set_sort_by(SortType.PRICE)
daft.set_max_price(2500)

listings = daft.search()

for listing in listings:
    print(listing.formalised_address)
    print(listing.daft_link)
    print(listing.price)
    features = listing.features
    if features is not None:
        print('Features: ')
        for feature in features:
            print(feature)
    print("")

Parse listing data from a given search result url.

from daftlistings import Daft

offset = 0

while 1:
    daft = Daft()
    daft.set_result_url("https://www.daft.ie/dublin-city/new-homes-for-sale/?ad_type=new_development")
    daft.set_offset(offset)
    listings = daft.search()
    if not listings:
        break
    for listing in listings:
        print(listing.formalised_address)
        print(listing.price)
        print(' ')
    offset += 10

Find student accommodation near UCD that is between 850 and 1000 per month

from daftlistings import Daft, SortOrder, SortType, RentType, University, StudentAccommodationType

daft = Daft()
daft.set_listing_type(RentType.STUDENT_ACCOMMODATION)
daft.set_university(University.UCD)
daft.set_student_accommodation_type(StudentAccommodationType.ROOMS_TO_SHARE)
daft.set_min_price(850)
daft.set_max_price(1000)
daft.set_sort_by(SortType.PRICE)
daft.set_sort_order(SortOrder.ASCENDING)
daft.set_offset(offset)
listings = daft.search()

for listing in listings:
    print(listing.price)
    print(listing.formalised_address)
    print(listing.daft_link)

For more examples, check the Examples folder

Tests

The Python unittest module contains its own test discovery function, which you can run from the command line:

python -m unittest discover tests/

Contributing

  • Fork the project and clone locally.
  • Create a new branch for what you’re going to work on.
  • Push to your origin repository.
  • Create a new pull request in GitHub.

Project details


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