mirror of
https://github.com/Bunsly/HomeHarvest.git
synced 2026-03-04 19:44:29 -08:00
[chore]: clean up
This commit is contained in:
Cullen Watson
@@ -1,5 +1,4 @@
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import pandas as pd
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from typing import Union
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import concurrent.futures
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from concurrent.futures import ThreadPoolExecutor
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@@ -7,7 +6,7 @@ from .core.scrapers import ScraperInput
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from .utils import process_result, ordered_properties
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from .core.scrapers.realtor import RealtorScraper
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from .core.scrapers.models import ListingType, Property, SiteName
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from .exceptions import InvalidSite, InvalidListingType
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from .exceptions import InvalidListingType
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_scrapers = {
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@@ -15,10 +14,7 @@ _scrapers = {
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}
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def _validate_input(site_name: str, listing_type: str) -> None:
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if site_name.lower() not in _scrapers:
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raise InvalidSite(f"Provided site, '{site_name}', does not exist.")
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def _validate_input(listing_type: str) -> None:
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if listing_type.upper() not in ListingType.__members__:
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raise InvalidListingType(f"Provided listing type, '{listing_type}', does not exist.")
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@@ -27,7 +23,7 @@ def _scrape_single_site(location: str, site_name: str, listing_type: str, radius
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"""
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Helper function to scrape a single site.
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"""
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_validate_input(site_name, listing_type)
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_validate_input(listing_type)
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scraper_input = ScraperInput(
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location=location,
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@@ -40,6 +36,7 @@ def _scrape_single_site(location: str, site_name: str, listing_type: str, radius
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site = _scrapers[site_name.lower()](scraper_input)
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results = site.search()
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print(f"found {len(results)}")
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properties_dfs = [process_result(result) for result in results]
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if not properties_dfs:
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@@ -50,22 +47,19 @@ def _scrape_single_site(location: str, site_name: str, listing_type: str, radius
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def scrape_property(
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location: str,
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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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) -> 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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Scrape properties from Realtor.com based on 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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:param location: US Location (e.g. 'San Francisco, CA', 'Cook County, IL', '85281', '2530 Al Lipscomb Way')
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:param site_name: Site name or list of site names (e.g. ['realtor.com', 'zillow'], 'redfin')
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:param listing_type: Listing type (e.g. 'for_sale', 'for_rent', 'sold')
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:param listing_type: Listing type (e.g. 'for_sale', 'for_rent', 'sold'). Default is 'for_sale'.
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:param radius: Radius in miles to find comparable properties on individual addresses. Optional.
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:param sold_last_x_days: Number of past days to filter sold properties. Optional.
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:param proxy: Proxy IP address to be used for scraping. Optional.
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:returns: pd.DataFrame containing properties
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"""
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site_name = "realtor.com"
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@@ -38,7 +38,8 @@ def main():
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parser.add_argument(
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"-r",
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"--radius",
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"--sold-properties-radius",
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dest="sold_properties_radius", # This makes sure the parsed argument is stored as radius_for_comps in args
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type=float,
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default=None,
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help="Get comparable properties within _ (eg. 0.0) miles. Only applicable for individual addresses."
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@@ -46,7 +47,7 @@ def main():
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args = parser.parse_args()
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result = scrape_property(args.location, args.listing_type, proxy=args.proxy)
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result = scrape_property(args.location, args.listing_type, radius_for_comps=args.radius_for_comps, proxy=args.proxy)
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if not args.filename:
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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@@ -32,39 +32,34 @@ class Address:
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@dataclass
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class Agent:
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name: str
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phone: str | None = None
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email: str | None = None
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class Description:
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style: str | None = None
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beds: int | None = None
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baths_full: int | None = None
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baths_half: int | None = None
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sqft: int | None = None
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lot_sqft: int | None = None
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sold_price: int | None = None
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year_built: int | None = None
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garage: float | None = None
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stories: int | None = None
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@dataclass
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class Property:
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property_url: str | None = None
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property_url: str
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mls: str | None = None
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mls_id: str | None = None
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status: str | None = None
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style: str | None = None
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beds: int | None = None
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baths_full: int | None = None
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baths_half: int | None = None
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list_price: int | None = None
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list_date: str | None = None
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sold_price: int | None = None
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last_sold_date: str | None = None
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prc_sqft: float | None = None
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est_sf: int | None = None
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lot_sf: int | None = None
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hoa_fee: int | None = None
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address: Address | None = None
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yr_blt: int | None = None
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list_price: int | None = None
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list_date: str | None = None
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last_sold_date: str | None = None
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prc_sqft: int | None = None
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hoa_fee: int | None = None
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description: Description | None = None
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latitude: float | None = None
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longitude: float | None = None
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stories: int | None = None
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prkg_gar: float | None = None
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neighborhoods: Optional[str] = None
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@@ -2,38 +2,26 @@
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homeharvest.realtor.__init__
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~~~~~~~~~~~~
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This module implements the scraper for relator.com
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This module implements the scraper for realtor.com
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"""
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from ..models import Property, Address, ListingType
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from typing import Dict, Union, Optional
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from .. import Scraper
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from ....exceptions import NoResultsFound
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from ..models import Property, Address, ListingType, Description
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class RealtorScraper(Scraper):
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SEARCH_URL = "https://www.realtor.com/api/v1/rdc_search_srp?client_id=rdc-search-new-communities&schema=vesta"
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PROPERTY_URL = "https://www.realtor.com/realestateandhomes-detail/"
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ADDRESS_AUTOCOMPLETE_URL = "https://parser-external.geo.moveaws.com/suggest"
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def __init__(self, scraper_input):
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self.counter = 1
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super().__init__(scraper_input)
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self.search_url = (
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"https://www.realtor.com/api/v1/rdc_search_srp?client_id=rdc-search-new-communities&schema=vesta"
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)
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def handle_location(self):
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headers = {
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"authority": "parser-external.geo.moveaws.com",
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"accept": "*/*",
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"accept-language": "en-US,en;q=0.9",
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"origin": "https://www.realtor.com",
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"referer": "https://www.realtor.com/",
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"sec-ch-ua": '"Chromium";v="116", "Not)A;Brand";v="24", "Google Chrome";v="116"',
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"sec-ch-ua-mobile": "?0",
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"sec-ch-ua-platform": '"Windows"',
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"sec-fetch-dest": "empty",
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"sec-fetch-mode": "cors",
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"sec-fetch-site": "cross-site",
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"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
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}
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params = {
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"input": self.location,
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"client_id": self.listing_type.value.lower().replace("_", "-"),
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@@ -42,9 +30,8 @@ class RealtorScraper(Scraper):
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}
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response = self.session.get(
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"https://parser-external.geo.moveaws.com/suggest",
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self.ADDRESS_AUTOCOMPLETE_URL,
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params=params,
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headers=headers,
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)
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response_json = response.json()
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@@ -70,22 +57,19 @@ class RealtorScraper(Scraper):
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stories
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}
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address {
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address_validation_code
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city
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country
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county
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line
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postal_code
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state_code
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street_direction
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street_name
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street_number
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street_name
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street_suffix
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street_post_direction
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unit_value
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unit
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unit_descriptor
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zip
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city
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state_code
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postal_code
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location {
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coordinate {
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lat
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lon
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}
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}
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}
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basic {
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baths
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@@ -113,25 +97,24 @@ class RealtorScraper(Scraper):
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"variables": variables,
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}
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response = self.session.post(self.search_url, json=payload)
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response = self.session.post(self.SEARCH_URL, json=payload)
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response_json = response.json()
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property_info = response_json["data"]["property"]
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return [
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Property(
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property_url="https://www.realtor.com/realestateandhomes-detail/"
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+ property_info["details"]["permalink"],
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stories=property_info["details"]["stories"],
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mls_id=property_id,
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property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
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address=self._parse_address(property_info, search_type="handle_address"),
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description=self._parse_description(property_info)
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)
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]
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def general_search(self, variables: dict, search_type: str, return_total: bool = False) -> list[Property] | int:
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def general_search(self, variables: dict, search_type: str) -> Dict[str, Union[int, list[Property]]]:
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"""
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Handles a location area & returns a list of properties
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"""
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results_query = """{
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count
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total
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@@ -141,86 +124,87 @@ class RealtorScraper(Scraper):
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status
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last_sold_price
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last_sold_date
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hoa {
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fee
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}
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list_price
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price_per_sqft
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description {
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sqft
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beds
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baths_full
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baths_half
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beds
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lot_sqft
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sqft
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sold_price
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year_built
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garage
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sold_price
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type
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sub_type
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name
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stories
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}
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source {
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raw {
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area
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status
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style
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}
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last_update_date
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contract_date
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id
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listing_id
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name
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type
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listing_href
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community_id
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management_id
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corporation_id
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subdivision_status
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spec_id
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plan_id
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tier_rank
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feed_type
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}
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hoa {
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fee
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}
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location {
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address {
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street_number
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street_name
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street_suffix
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unit
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city
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country
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line
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postal_code
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state_code
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state
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postal_code
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coordinate {
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lon
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lat
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}
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street_direction
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street_name
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street_number
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street_post_direction
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street_suffix
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unit
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}
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neighborhoods {
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name
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name
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}
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}
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list_price
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price_per_sqft
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style_category_tags {
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exterior
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}
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source {
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id
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}
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}
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}
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}"""
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sold_date_param = ('sold_date: { min: "$today-%sD" }' % self.sold_last_x_days
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if self.listing_type == ListingType.SOLD and self.sold_last_x_days is not None
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if self.listing_type == ListingType.SOLD and self.sold_last_x_days
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else "")
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sort_param = ('sort: [{ field: sold_date, direction: desc }]'
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if self.listing_type == ListingType.SOLD
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else 'sort: [{ field: list_date, direction: desc }]')
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if search_type == "area":
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if search_type == "comps":
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print('general - comps')
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query = (
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"""query Property_search(
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$coordinates: [Float]!
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$radius: String!
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$offset: Int!,
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) {
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property_search(
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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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status: %s
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%s
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}
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%s
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limit: 200
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offset: $offset
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) %s""" % (
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self.listing_type.value.lower(),
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sold_date_param,
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sort_param,
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results_query
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)
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)
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else:
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print('general - not comps')
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query = (
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"""query Home_search(
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$city: String,
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@@ -238,60 +222,27 @@ class RealtorScraper(Scraper):
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status: %s
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%s
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}
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%s
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limit: 200
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offset: $offset
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) %s"""
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% (
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self.listing_type.value.lower(),
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sold_date_param,
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sort_param,
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results_query
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)
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)
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elif search_type == "comp_address":
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query = (
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"""query Property_search(
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$coordinates: [Float]!
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$radius: String!
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$offset: Int!,
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) {
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property_search(
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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: $offset
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) %s""" % (sold_date_param, results_query))
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else:
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query = (
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"""query Property_search(
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$property_id: [ID]!
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$offset: Int!,
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) {
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property_search(
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query: {
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property_id: $property_id
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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""" % (sold_date_param, results_query))
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payload = {
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"query": query,
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"variables": variables,
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}
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response = self.session.post(self.search_url, json=payload)
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response = self.session.post(self.SEARCH_URL, json=payload)
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response.raise_for_status()
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response_json = response.json()
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search_key = "home_search" if search_type == "area" else "property_search"
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if return_total:
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return response_json["data"][search_key]["total"]
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search_key = "property_search" if search_type == "comps" else "home_search"
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properties: list[Property] = []
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@@ -303,7 +254,7 @@ class RealtorScraper(Scraper):
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or response_json["data"][search_key] is None
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or "results" not in response_json["data"][search_key]
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):
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return []
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return {"total": 0, "properties": []}
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for result in response_json["data"][search_key]["results"]:
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self.counter += 1
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@@ -312,122 +263,131 @@ class RealtorScraper(Scraper):
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if "source" in result and isinstance(result["source"], dict)
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else None
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||||
)
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mls_id = (
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result["source"].get("listing_id")
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if "source" in result and isinstance(result["source"], dict)
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else None
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||||
)
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||||
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if not mls_id:
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if not mls:
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continue
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# not type
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||||
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||||
neighborhoods_list = []
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neighborhoods = result["location"].get("neighborhoods", [])
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||||
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||||
if neighborhoods:
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||||
for neighborhood in neighborhoods:
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name = neighborhood.get("name")
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||||
if name:
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||||
neighborhoods_list.append(name)
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||||
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||||
neighborhoods_str = (
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||||
", ".join(neighborhoods_list) if neighborhoods_list else None
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||||
)
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||||
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||||
able_to_get_lat_long = result and result.get("location") and result["location"].get("address") and result["location"]["address"].get("coordinate")
|
||||
|
||||
realty_property = Property(
|
||||
property_url="https://www.realtor.com/realestateandhomes-detail/"
|
||||
+ result["property_id"],
|
||||
mls=mls,
|
||||
mls_id=mls_id,
|
||||
mls_id=result["source"].get("listing_id") if "source" in result and isinstance(result["source"], dict) else None,
|
||||
property_url=f"{self.PROPERTY_URL}{result['property_id']}",
|
||||
status=result["status"].upper(),
|
||||
style=result["description"]["type"].upper(),
|
||||
beds=result["description"]["beds"],
|
||||
baths_full=result["description"]["baths_full"],
|
||||
baths_half=result["description"]["baths_half"],
|
||||
est_sf=result["description"]["sqft"],
|
||||
lot_sf=result["description"]["lot_sqft"],
|
||||
list_price=result["list_price"],
|
||||
list_date=result["list_date"].split("T")[0]
|
||||
if result["list_date"]
|
||||
else None,
|
||||
sold_price=result["description"]["sold_price"],
|
||||
prc_sqft=result["price_per_sqft"],
|
||||
last_sold_date=result["last_sold_date"],
|
||||
list_date=result["list_date"].split("T")[0] if result.get("list_date") else None,
|
||||
prc_sqft=result.get("price_per_sqft"),
|
||||
last_sold_date=result.get("last_sold_date"),
|
||||
hoa_fee=result["hoa"]["fee"] if result.get("hoa") and isinstance(result["hoa"], dict) else None,
|
||||
address=Address(
|
||||
street=f"{result['location']['address']['street_number']} {result['location']['address']['street_name']} {result['location']['address']['street_suffix']}",
|
||||
unit=result["location"]["address"]["unit"],
|
||||
city=result["location"]["address"]["city"],
|
||||
state=result["location"]["address"]["state_code"],
|
||||
zip=result["location"]["address"]["postal_code"],
|
||||
),
|
||||
yr_blt=result["description"]["year_built"],
|
||||
latitude=result["location"]["address"]["coordinate"].get("lat") if able_to_get_lat_long else None,
|
||||
longitude=result["location"]["address"]["coordinate"].get("lon") if able_to_get_lat_long else None,
|
||||
prkg_gar=result["description"]["garage"],
|
||||
stories=result["description"]["stories"],
|
||||
neighborhoods=neighborhoods_str,
|
||||
address=self._parse_address(result, search_type="general_search"),
|
||||
neighborhoods=self._parse_neighborhoods(result),
|
||||
description=self._parse_description(result)
|
||||
)
|
||||
properties.append(realty_property)
|
||||
|
||||
return properties
|
||||
|
||||
# print(response_json["data"]["property_search"], variables["offset"])
|
||||
# print(response_json["data"]["home_search"]["total"], variables["offset"])
|
||||
return {
|
||||
"total": response_json["data"][search_key]["total"],
|
||||
"properties": properties,
|
||||
}
|
||||
|
||||
def search(self):
|
||||
location_info = self.handle_location()
|
||||
location_type = location_info["area_type"]
|
||||
is_for_comps = self.radius is not None and location_type == "address"
|
||||
|
||||
offset = 0
|
||||
search_variables = {
|
||||
"offset": offset,
|
||||
"offset": 0,
|
||||
}
|
||||
|
||||
search_type = "comp_address" if is_for_comps \
|
||||
else "address" if location_type == "address" and not is_for_comps \
|
||||
else "area"
|
||||
search_type = "comps" if self.radius and location_type == "address" else "area"
|
||||
print(search_type)
|
||||
if location_type == "address":
|
||||
if not self.radius: #: single address search, non comps
|
||||
property_id = location_info["mpr_id"]
|
||||
search_variables |= {"property_id": property_id}
|
||||
return self.handle_address(property_id)
|
||||
|
||||
if location_type == "address" and not is_for_comps: #: single address search, non comps
|
||||
property_id = location_info["mpr_id"]
|
||||
search_variables = search_variables | {"property_id": property_id}
|
||||
else: #: general search, comps (radius)
|
||||
coordinates = list(location_info["centroid"].values())
|
||||
search_variables |= {
|
||||
"coordinates": coordinates,
|
||||
"radius": "{}mi".format(self.radius),
|
||||
}
|
||||
|
||||
general_search = self.general_search(search_variables, search_type)
|
||||
if general_search:
|
||||
return general_search
|
||||
else:
|
||||
return self.handle_address(property_id) #: TODO: support single address search for query by property address (can go from property -> listing to get better data)
|
||||
|
||||
elif not is_for_comps: #: area search
|
||||
search_variables = search_variables | {
|
||||
else: #: general search, location
|
||||
search_variables |= {
|
||||
"city": location_info.get("city"),
|
||||
"county": location_info.get("county"),
|
||||
"state_code": location_info.get("state_code"),
|
||||
"postal_code": location_info.get("postal_code"),
|
||||
}
|
||||
else: #: comps search
|
||||
coordinates = list(location_info["centroid"].values())
|
||||
search_variables = search_variables | {
|
||||
"coordinates": coordinates,
|
||||
"radius": "{}mi".format(self.radius),
|
||||
}
|
||||
|
||||
total = self.general_search(search_variables, return_total=True, search_type=search_type)
|
||||
result = self.general_search(search_variables, search_type=search_type)
|
||||
total = result["total"]
|
||||
homes = result["properties"]
|
||||
|
||||
homes = []
|
||||
with ThreadPoolExecutor(max_workers=10) as executor:
|
||||
futures = [
|
||||
executor.submit(
|
||||
self.general_search,
|
||||
variables=search_variables | {"offset": i},
|
||||
return_total=False,
|
||||
search_type=search_type,
|
||||
)
|
||||
for i in range(0, total, 200)
|
||||
for i in range(200, min(total, 10000), 200)
|
||||
]
|
||||
|
||||
for future in as_completed(futures):
|
||||
homes.extend(future.result())
|
||||
homes.extend(future.result()["properties"])
|
||||
|
||||
return homes
|
||||
|
||||
@staticmethod
|
||||
def _parse_neighborhoods(result: dict) -> Optional[str]:
|
||||
neighborhoods_list = []
|
||||
neighborhoods = result["location"].get("neighborhoods", [])
|
||||
|
||||
if neighborhoods:
|
||||
for neighborhood in neighborhoods:
|
||||
name = neighborhood.get("name")
|
||||
if name:
|
||||
neighborhoods_list.append(name)
|
||||
|
||||
return ", ".join(neighborhoods_list) if neighborhoods_list else None
|
||||
|
||||
@staticmethod
|
||||
def _parse_address(result: dict, search_type):
|
||||
if search_type == "general_search":
|
||||
return Address(
|
||||
street=f"{result['location']['address']['street_number']} {result['location']['address']['street_name']} {result['location']['address']['street_suffix']}",
|
||||
unit=result["location"]["address"]["unit"],
|
||||
city=result["location"]["address"]["city"],
|
||||
state=result["location"]["address"]["state_code"],
|
||||
zip=result["location"]["address"]["postal_code"],
|
||||
)
|
||||
return Address(
|
||||
street=f"{result['address']['street_number']} {result['address']['street_name']} {result['address']['street_suffix']}",
|
||||
unit=result['address']['unit'],
|
||||
city=result['address']['city'],
|
||||
state=result['address']['state_code'],
|
||||
zip=result['address']['postal_code'],
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _parse_description(result: dict) -> Description:
|
||||
description_data = result.get("description", {})
|
||||
return Description(
|
||||
style=description_data.get("type", "").upper(),
|
||||
beds=description_data.get("beds"),
|
||||
baths_full=description_data.get("baths_full"),
|
||||
baths_half=description_data.get("baths_half"),
|
||||
sqft=description_data.get("sqft"),
|
||||
lot_sqft=description_data.get("lot_sqft"),
|
||||
sold_price=description_data.get("sold_price"),
|
||||
year_built=description_data.get("year_built"),
|
||||
garage=description_data.get("garage"),
|
||||
stories=description_data.get("stories"),
|
||||
)
|
||||
@@ -1,18 +1,6 @@
|
||||
class InvalidSite(Exception):
|
||||
"""Raised when a provided site is does not exist."""
|
||||
|
||||
|
||||
class InvalidListingType(Exception):
|
||||
"""Raised when a provided listing type is does not exist."""
|
||||
|
||||
|
||||
class NoResultsFound(Exception):
|
||||
"""Raised when no results are found for the given location"""
|
||||
|
||||
|
||||
class GeoCoordsNotFound(Exception):
|
||||
"""Raised when no property is found for the given address"""
|
||||
|
||||
|
||||
class SearchTooBroad(Exception):
|
||||
"""Raised when the search is too broad"""
|
||||
|
||||
@@ -39,7 +39,6 @@ def process_result(result: Property) -> pd.DataFrame:
|
||||
prop_data["MLS"] = prop_data["mls"]
|
||||
prop_data["MLS #"] = prop_data["mls_id"]
|
||||
prop_data["Status"] = prop_data["status"]
|
||||
prop_data["Style"] = prop_data["style"]
|
||||
|
||||
if "address" in prop_data:
|
||||
address_data = prop_data["address"]
|
||||
@@ -49,26 +48,27 @@ def process_result(result: Property) -> pd.DataFrame:
|
||||
prop_data["State"] = address_data.state
|
||||
prop_data["Zip"] = address_data.zip
|
||||
|
||||
prop_data["Community"] = prop_data["neighborhoods"]
|
||||
prop_data["Beds"] = prop_data["beds"]
|
||||
prop_data["FB"] = prop_data["baths_full"]
|
||||
prop_data["NumHB"] = prop_data["baths_half"]
|
||||
prop_data["EstSF"] = prop_data["est_sf"]
|
||||
prop_data["ListPrice"] = prop_data["list_price"]
|
||||
prop_data["Lst Date"] = prop_data["list_date"]
|
||||
prop_data["Sold Price"] = prop_data["sold_price"]
|
||||
prop_data["COEDate"] = prop_data["last_sold_date"]
|
||||
prop_data["LotSFApx"] = prop_data["lot_sf"]
|
||||
prop_data["PrcSqft"] = prop_data["prc_sqft"]
|
||||
prop_data["HOAFee"] = prop_data["hoa_fee"]
|
||||
|
||||
if prop_data.get("prc_sqft") is not None:
|
||||
prop_data["PrcSqft"] = round(prop_data["prc_sqft"], 2)
|
||||
description = result.description
|
||||
prop_data["Style"] = description.style
|
||||
prop_data["Beds"] = description.beds
|
||||
prop_data["FB"] = description.baths_full
|
||||
prop_data["NumHB"] = description.baths_half
|
||||
prop_data["EstSF"] = description.sqft
|
||||
prop_data["LotSFApx"] = description.lot_sqft
|
||||
prop_data["Sold Price"] = description.sold_price
|
||||
prop_data["YrBlt"] = description.year_built
|
||||
prop_data["PrkgGar"] = description.garage
|
||||
prop_data["Stories"] = description.stories
|
||||
|
||||
prop_data["YrBlt"] = prop_data["yr_blt"]
|
||||
prop_data["LATITUDE"] = prop_data["latitude"]
|
||||
prop_data["LONGITUDE"] = prop_data["longitude"]
|
||||
prop_data["Stories"] = prop_data["stories"]
|
||||
prop_data["PrkgGar"] = prop_data["prkg_gar"]
|
||||
prop_data["Community"] = prop_data["neighborhoods"]
|
||||
|
||||
properties_df = pd.DataFrame([prop_data])
|
||||
properties_df = properties_df.reindex(columns=ordered_properties)
|
||||
|
||||
Reference in New Issue
Block a user