- last x days param
parent
40bbf76db1
commit
088088ae51
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@ -106,7 +106,7 @@ def _process_result(result: Property) -> pd.DataFrame:
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return properties_df
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def _scrape_single_site(location: str, site_name: str, listing_type: str, radius: float, proxy: str = None) -> pd.DataFrame:
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def _scrape_single_site(location: str, site_name: str, listing_type: str, radius: float, proxy: str = None, sold_last_x_days: int = None) -> pd.DataFrame:
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"""
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Helper function to scrape a single site.
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"""
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@ -118,6 +118,7 @@ def _scrape_single_site(location: str, site_name: str, listing_type: str, radius
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site_name=SiteName.get_by_value(site_name.lower()),
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proxy=proxy,
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radius=radius,
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sold_last_x_days=sold_last_x_days
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)
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site = _scrapers[site_name.lower()](scraper_input)
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@ -136,12 +137,14 @@ def scrape_property(
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site_name: Union[str, list[str]] = "realtor.com",
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listing_type: str = "for_sale",
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radius: float = None,
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sold_last_x_days: int = None,
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proxy: str = None,
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keep_duplicates: bool = False
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) -> pd.DataFrame:
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"""
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Scrape property from various sites from a given location and listing type.
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:param sold_last_x_days: Sold in last x days
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:param radius: Radius in miles to find comparable properties on individual addresses
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:param keep_duplicates:
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:param proxy:
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@ -160,12 +163,12 @@ def scrape_property(
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results = []
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if len(site_name) == 1:
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final_df = _scrape_single_site(location, site_name[0], listing_type, radius, proxy)
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final_df = _scrape_single_site(location, site_name[0], listing_type, radius, proxy, sold_last_x_days)
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results.append(final_df)
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else:
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with ThreadPoolExecutor() as executor:
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futures = {
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executor.submit(_scrape_single_site, location, s_name, listing_type, radius, proxy): s_name
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executor.submit(_scrape_single_site, location, s_name, listing_type, radius, proxy, sold_last_x_days): s_name
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for s_name in site_name
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}
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@ -11,6 +11,7 @@ class ScraperInput:
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site_name: SiteName
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radius: float | None = None
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proxy: str | None = None
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sold_last_x_days: int | None = None
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class Scraper:
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@ -31,6 +32,7 @@ class Scraper:
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self.listing_type = scraper_input.listing_type
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self.site_name = scraper_input.site_name
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self.radius = scraper_input.radius
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self.sold_last_x_days = scraper_input.sold_last_x_days
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def search(self) -> list[Property]:
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...
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@ -4,7 +4,7 @@ homeharvest.realtor.__init__
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This module implements the scraper for relator.com
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"""
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from ..models import Property, Address
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from ..models import Property, Address, ListingType
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from .. import Scraper
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from ....exceptions import NoResultsFound
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from ....utils import parse_address_one, parse_address_two
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@ -204,6 +204,10 @@ class RealtorScraper(Scraper):
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}
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}}"""
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sold_date_param = ('sold_date: { min: "$today-%sD" }' % self.sold_last_x_days
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if self.listing_type == ListingType.SOLD and self.sold_last_x_days is not None
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else "")
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if not is_for_comps:
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query = (
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"""query Home_search(
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@ -220,11 +224,17 @@ class RealtorScraper(Scraper):
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postal_code: $postal_code
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state_code: $state_code
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status: %s
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%s
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}
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limit: 200
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offset: $offset
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) %s"""
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% (self.listing_type.value.lower(), results_query))
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% (
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self.listing_type.value.lower(),
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sold_date_param,
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results_query
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)
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)
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else:
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query = (
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"""query Property_search(
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@ -233,10 +243,16 @@ class RealtorScraper(Scraper):
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$offset: Int!,
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) {
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property_search(
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query: { nearby: { coordinates: $coordinates, radius: $radius } }
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query: {
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nearby: {
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coordinates: $coordinates
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radius: $radius
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}
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%s
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}
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limit: 200
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offset: $offset
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) %s""" % results_query)
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) %s""" % (sold_date_param, results_query))
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payload = {
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"query": query,
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@ -14,7 +14,19 @@ def test_realtor_comps():
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radius=0.5,
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)
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print(result)
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assert result is not None and len(result) > 0
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def test_realtor_last_x_days_sold():
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days_result_30 = scrape_property(
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location="Dallas, TX", site_name="realtor.com", listing_type="sold", sold_last_x_days=30
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)
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days_result_10 = scrape_property(
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location="Dallas, TX", site_name="realtor.com", listing_type="sold", sold_last_x_days=10
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)
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assert all([result is not None for result in [days_result_30, days_result_10]]) and len(days_result_30) != len(days_result_10)
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def test_realtor():
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