110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
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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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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_scrapers = {
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"realtor.com": RealtorScraper,
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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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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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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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_validate_input(site_name, 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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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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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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return pd.DataFrame()
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return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties]
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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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: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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:returns: pd.DataFrame containing properties
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"""
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site_name = "realtor.com"
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if site_name is None:
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site_name = list(_scrapers.keys())
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if not isinstance(site_name, list):
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site_name = [site_name]
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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, 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, sold_last_x_days): s_name
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for s_name in site_name
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}
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for future in concurrent.futures.as_completed(futures):
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result = future.result()
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results.append(result)
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results = [df for df in results if not df.empty and not df.isna().all().all()]
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if not results:
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return pd.DataFrame()
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final_df = pd.concat(results, ignore_index=True)
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columns_to_track = ["Street", "Unit", "Zip"]
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#: validate they exist, otherwise create them
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for col in columns_to_track:
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if col not in final_df.columns:
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final_df[col] = None
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return final_df
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