163 lines
4.8 KiB
Python
163 lines
4.8 KiB
Python
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]
|
|
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)
|
|
|
|
if not results:
|
|
return pd.DataFrame()
|
|
|
|
final_df = pd.concat(results, ignore_index=True)
|
|
final_df = final_df.drop_duplicates(subset="street_address", keep="first")
|
|
return final_df
|