daftlistings

Daftlistings

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.title)
    print(listing.price)
    print(listing.daft_link)
    # ...

By default, the Daft search function iterates over each page of results and appends each Listing object to the array that is returned. If you wish to limit the number of results that are returned you can use the max_pages argument.

daft.search(max_pages=1)

Examples

Search for apartments for rent in Dublin.

from daftlistings import Daft, Location, SearchType, PropertyType

daft = Daft()
daft.set_location(Location.DUBLIN)
daft.set_search_type(SearchType.RESIDENTIAL_RENT)
daft.set_property_type(PropertyType.APARTMENT)

listings = daft.search()

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

Search for houses for sale in Dublin between 400 and 500k.

from daftlistings import Daft, Location, SearchType, PropertyType

daft = Daft()
daft.set_location(Location.DUBLIN)
daft.set_search_type(SearchType.RESIDENTIAL_SALE)
daft.set_property_type(PropertyType.HOUSE)
daft.set_min_price(400000)
daft.set_max_price(500000)

listings = daft.search()

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

Search for student accomodation near Dundalk IT.

from daftlistings import Daft, Location, SearchType

daft = Daft()
daft.set_location(Location.DUNDALK_INSTITUTE_OF_TECHNOLOGY_LOUTH)
daft.set_search_type(SearchType.STUDENT_ACCOMMODATION)

listings = daft.search()

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

Search for commercial listings.

from daftlistings import Daft, SearchType

daft = Daft()
daft.set_search_type(SearchType.COMMERCIAL_SALE)

listings = daft.search()

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

Search properties according to criteria then sort by nearness to Dublin Castle

from daftlistings import Daft, SearchType

daft = Daft()

daft.set_location("Dublin City")
daft.set_search_type(SearchType.RESIDENTIAL_RENT)
daft.set_min_price(1000)
daft.set_max_price(1500)

listings = daft.search(max_pages=1)

dublin_castle_coords = [53.3429, -6.2674]
listings.sort(key=lambda x: x.distance_to(dublin_castle_coords))

for listing in listings:
    print(f'{listing.title}')
    print(f'{listing.daft_link}')
    print(f'{listing.price}')
    print(f'{listing.distance_to(dublin_castle_coords):.3}km')
    print('')

Search properties within 10kms of Dublin city centre

from daftlistings import Daft, SearchType

daft = Daft()

daft.set_location("Dublin City Centre", Distance.KM10)
daft.set_search_type(SearchType.RESIDENTIAL_RENT)

listings = daft.search(max_pages=1)

for listing in listings:
    print(f'{listing.title}')
    print(f'{listing.daft_link}')
    print(f'{listing.price}')
    print('')

Search rental properties in Dublin with monthly rent lower than 1500 euros and visualize it on a map

import pandas as pd
from daftlistings import Daft, Location, SearchType, PropertyType, SortType, MapVisualization

 
daft = Daft()
daft.set_location(Location.DUBLIN)
daft.set_search_type(SearchType.RESIDENTIAL_RENT)
daft.set_sort_type(SortType.PRICE_ASC)
daft.set_max_price(1500)

listings = daft.search()

# cache the listings in the local file
with open("result.txt", "w") as fp:
    fp.writelines("%s\n" % listing.as_dict_for_mapping() for listing in listings)

# read from the local file
with open("result.txt") as fp:
  lines = fp.readlines()

properties = []
for line in lines:
  properties.append(eval(line))

df = pd.DataFrame(properties)
print(df)

dublin_map = MapVisualization(df)
dublin_map.add_markers()
dublin_map.add_colorbar()
dublin_map.save("ireland_rent.html")
print("Done, please checkout the html file")

Search for apartments for rent in Dublin with an alarm and parking.

from daftlistings import Daft, Location, SearchType, PropertyType, Facility

daft = Daft()
daft.set_location(Location.DUBLIN)
daft.set_search_type(SearchType.RESIDENTIAL_RENT)
daft.set_property_type(PropertyType.APARTMENT)
daft.set_facility(Facility.PARKING)
daft.set_facility(Facility.ALARM)

listings = daft.search()

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

Running 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.

Note: We use (Black)[https://github.com/psf/black] for code formatting. After making any changes to the code, it is important for you to ensure that it passes Black's lint check.