# Get the current properties for rent in Dublin that are between 1000 and 1500 per month.
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)
daft.set_furnished(True)
daft.set_keywords(['quiet'])
listings = daft.search()
for listing in listings:
print(listing.formalised_address)
facilities = listing.facilities
if facilities is not None:
print('Facilities: ')
for facility in facilities:
print(facility)
features = listing.features
if features is not None:
print('Features: ')
for feature in features:
print(feature)
print("")
from daftlistings import Daft, RentType, RoomType, Gender
daft = Daft()
daft.set_listing_type(RentType.ROOMS_TO_SHARE)
daft.set_room_type(RoomType.DOUBLE)
daft.set_furnished(True)
daft.set_county('Dublin City')
daft.set_area('Castleknock')
daft.set_gender(Gender.MALE)
daft.set_with_photos(True)
listings = daft.search()
for listing in listings:
print(listing.get_price)
print(listing.formalised_address)
print(listing.daft_link)
print(listing.contact_number)
print(' ')
from daftlistings import Daft, SaleType, HouseType, SortType
import statsmodels.api as sm
import pandas as pd
import numpy as np
from IPython.display import HTML
daft = Daft()
county = "Dublin"
areas = ["Dublin 15", "Dublin 7"]
#areas = ["Ashtown"]
daft.set_listing_type(SaleType.HOUSES)
daft.set_house_type(HouseType.DETACHED)
daft.set_area(areas)
daft.set_county(county)
daft.set_sort_by(SortType.PRICE)
df = pd.DataFrame()
listings = daft.search()
for listing in listings:
# print(listing)
try:
data = pd.DataFrame([
listing.price, listing.bedrooms, listing.bathrooms,
listing.dwelling_type, listing.ber_code, listing.floor_area,
listing.town, listing.county, listing.daft_link
]).T
def test_area_commercial_properties(self):
daft = Daft()
daft.set_county("Dublin City")
daft.set_listing_type(SaleType.COMMERCIAL)
listings = daft.search()
self.assertTrue(len(listings) > 0)
from daftlistings import Daft, SaleType
import csv
offset = 0
pages = True
daft_data = []
#Check the fastest way to write a list into csv format!
# while pages:
#for pages in range(1):
while pages:
daft = Daft()
daft.set_county('Dublin')
#daft.set_area([])
daft.set_listing_type(SaleType.PROPERTIES)
daft.set_offset(offset)
listings = daft.get_listings()
if not listings:
pages = False
for listing in listings:
link = listing.get_daft_link()
county = listing.get_county()
town = listing.get_town()
address = listing.get_formalised_address()
price = listing.get_price()
dwelling_type = listing.get_dwelling_type()
bedrooms = listing.get_num_bedrooms()