51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
import warnings
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import pandas as pd
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from .core.scrapers import ScraperInput
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from .utils import process_result, ordered_properties, validate_input
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from .core.scrapers.realtor import RealtorScraper
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from .core.scrapers.models import ListingType
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from .exceptions import InvalidListingType, NoResultsFound
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def scrape_property(
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location: str,
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listing_type: str = "for_sale",
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radius: float = None,
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mls_only: bool = False,
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past_days: int = None,
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pending_or_contingent: bool = False,
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proxy: str = None,
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) -> pd.DataFrame:
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"""
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Scrape properties from Realtor.com based on a given location and listing type.
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:param location: Location to search (e.g. "Dallas, TX", "85281", "2530 Al Lipscomb Way")
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:param listing_type: Listing Type (for_sale, for_rent, sold)
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:param radius: Get properties within _ (e.g. 1.0) miles. Only applicable for individual addresses.
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:param mls_only: If set, fetches only listings with MLS IDs.
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:param past_days: Get properties sold or listed (dependent on your listing_type) in the last _ days.
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:param pending_or_contingent: If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.
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:param proxy: Proxy to use for scraping
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"""
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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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listing_type=ListingType[listing_type.upper()],
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proxy=proxy,
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radius=radius,
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mls_only=mls_only,
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last_x_days=past_days,
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pending_or_contingent=pending_or_contingent,
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)
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site = RealtorScraper(scraper_input)
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results = site.search()
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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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raise NoResultsFound("no results found for the query")
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", category=FutureWarning)
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return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties]
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