import pandas as pd from typing import Union import concurrent.futures from concurrent.futures import ThreadPoolExecutor from .core.scrapers import ScraperInput from .core.scrapers.redfin import RedfinScraper from .core.scrapers.realtor import RealtorScraper from .core.scrapers.zillow import ZillowScraper from .core.scrapers.models import ListingType, Property, SiteName from .exceptions import InvalidSite, InvalidListingType _scrapers = { "redfin": RedfinScraper, "realtor.com": RealtorScraper, "zillow": ZillowScraper, } def validate_input(site_name: str, listing_type: str) -> None: if site_name.lower() not in _scrapers: raise InvalidSite(f"Provided site, '{site_name}', does not exist.") if listing_type.upper() not in ListingType.__members__: raise InvalidListingType( f"Provided listing type, '{listing_type}', does not exist." ) def get_ordered_properties(result: Property) -> list[str]: return [ "property_url", "site_name", "listing_type", "property_type", "status_text", "currency", "price", "apt_min_price", "tax_assessed_value", "square_feet", "price_per_sqft", "beds", "baths", "lot_area_value", "lot_area_unit", "street_address", "unit", "city", "state", "zip_code", "country", "posted_time", "bldg_min_beds", "bldg_min_baths", "bldg_min_area", "bldg_unit_count", "bldg_name", "stories", "year_built", "agent_name", "mls_id", "description", "img_src", "latitude", "longitude", ] def process_result(result: Property) -> pd.DataFrame: prop_data = result.__dict__ prop_data["site_name"] = prop_data["site_name"].value prop_data["listing_type"] = prop_data["listing_type"].value.lower() if "property_type" in prop_data and prop_data["property_type"] is not None: prop_data["property_type"] = prop_data["property_type"].value.lower() else: prop_data["property_type"] = None if "address" in prop_data: address_data = prop_data["address"] prop_data["street_address"] = address_data.street_address prop_data["unit"] = address_data.unit prop_data["city"] = address_data.city prop_data["state"] = address_data.state prop_data["zip_code"] = address_data.zip_code prop_data["country"] = address_data.country del prop_data["address"] properties_df = pd.DataFrame([prop_data]) properties_df = properties_df[get_ordered_properties(result)] return properties_df def _scrape_single_site( location: str, site_name: str, listing_type: str ) -> pd.DataFrame: """ Helper function to scrape a single site. """ validate_input(site_name, listing_type) scraper_input = ScraperInput( location=location, listing_type=ListingType[listing_type.upper()], site_name=SiteName.get_by_value(site_name.lower()), ) site = _scrapers[site_name.lower()](scraper_input) results = site.search() properties_dfs = [process_result(result) for result in results] properties_dfs = [df.dropna(axis=1, how='all') for df in properties_dfs if not df.empty] if not properties_dfs: return pd.DataFrame() return pd.concat(properties_dfs, ignore_index=True) def scrape_property( location: str, site_name: Union[str, list[str]] = None, listing_type: str = "for_sale", ) -> pd.DataFrame: """ Scrape property from various sites from a given location and listing type. :returns: pd.DataFrame :param location: US Location (e.g. 'San Francisco, CA', 'Cook County, IL', '85281', '2530 Al Lipscomb Way') :param site_name: Site name or list of site names (e.g. ['realtor.com', 'zillow'], 'redfin') :param listing_type: Listing type (e.g. 'for_sale', 'for_rent', 'sold') :return: pd.DataFrame containing properties """ if site_name is None: site_name = list(_scrapers.keys()) if not isinstance(site_name, list): site_name = [site_name] results = [] if len(site_name) == 1: final_df = _scrape_single_site(location, site_name[0], listing_type) results.append(final_df) else: with ThreadPoolExecutor() as executor: futures = { executor.submit(_scrape_single_site, location, s_name, listing_type): s_name for s_name in site_name } for future in concurrent.futures.as_completed(futures): result = future.result() results.append(result) results = [df for df in results if not df.empty and not df.isna().all().all()] if not results: return pd.DataFrame() final_df = pd.concat(results, ignore_index=True) columns_to_track = ["street_address", "city", "unit"] #: validate they exist, otherwise create them for col in columns_to_track: if col not in final_df.columns: final_df[col] = None final_df = final_df.drop_duplicates(subset=["street_address", "city", "unit"], keep="first") return final_df