241 lines
8.8 KiB
Python
241 lines
8.8 KiB
Python
"""Main funda scraper module"""
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import multiprocessing as mp
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import os
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import pandas as pd
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import requests
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from bs4 import BeautifulSoup
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from typing import List, Dict
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import datetime
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from funda_scraper.config.core import config
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from funda_scraper.preprocess import preprocess_data
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from funda_scraper.utils import logger
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from tqdm import tqdm
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from tqdm.contrib.concurrent import process_map
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class FundaScraper:
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"""
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Handles the main scraping function.
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"""
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def __init__(
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self,
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area: str = None,
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want_to: str = "buy",
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n_pages: int = 1,
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find_past: bool = False,
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):
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self.area = area.lower().replace(" ", "-") if isinstance(area, str) else area
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self.want_to = want_to
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self.find_past = find_past
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self.n_pages = min(max(n_pages, 1), 999)
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self.links: List[str] = []
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self.raw_df = pd.DataFrame()
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self.clean_df = pd.DataFrame()
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self.base_url = config.base_url
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self.selectors = config.css_selector
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def __repr__(self):
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return (
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f"FundaScraper(area={self.area}, "
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f"want_to={self.want_to}, "
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f"n_pages={self.n_pages}, "
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f"use_past_data={self.find_past})"
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)
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@property
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def site_url(self) -> Dict[str, str]:
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"""Return the corresponding urls."""
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if self.to_buy:
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return {
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"close": f"{self.base_url}/koop/verkocht/{self.area}/",
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"open": f"{self.base_url}/koop/{self.area}/",
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}
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else:
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return {
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"close": f"{self.base_url}/huur/{self.area}/verhuurd/",
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"open": f"{self.base_url}/huur/{self.area}/",
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}
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@property
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def to_buy(self) -> bool:
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"""Whether to buy or not"""
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if self.want_to.lower() in ["buy", "koop", "b"]:
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return True
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elif self.want_to.lower() in ["rent", "huur", "r"]:
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return False
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else:
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raise ValueError("'want_to' must be 'either buy' or 'rent'.")
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@staticmethod
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def _check_dir() -> None:
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"""Check whether a temporary directory for data"""
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if not os.path.exists("data"):
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os.makedirs("data")
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@staticmethod
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def _get_links_from_one_page(url: str) -> List[str]:
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"""Scrape all the available housing items from one Funda search page."""
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response = requests.get(url, headers=config.header)
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soup = BeautifulSoup(response.text, "lxml")
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house = soup.find_all(attrs={"data-object-url-tracking": "resultlist"})
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item_list = [h.get("href") for h in house]
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return list(set(item_list))
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def init(
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self,
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area: str = None,
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want_to: str = None,
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n_pages: int = None,
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find_past: bool = None,
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) -> None:
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"""Overwrite or initialise the searching scope."""
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if area is not None:
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self.area = area
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if want_to is not None:
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self.want_to = want_to
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if n_pages is not None:
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self.n_pages = n_pages
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if find_past is not None:
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self.find_past = find_past
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def fetch_links(self) -> None:
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"""Find all the available links across multiple pages. """
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if self.area is None or self.want_to is None:
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raise ValueError("You haven't set the area and what you're looking for.")
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logger.info("*** Phase 1: Fetch all the available links from all pages *** ")
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urls = []
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main_url = self.site_url["close"] if self.find_past else self.site_url["open"]
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for i in tqdm(range(0, self.n_pages + 1)):
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item_list = self._get_links_from_one_page(main_url + f"p{i}")
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if len(item_list) == 0:
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self.n_pages = i
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break
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urls += item_list
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urls = list(set(urls))
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logger.info(
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f"*** Got all the urls. {len(urls)} houses found in {self.n_pages} pages. ***"
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)
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self.links = ["https://www.funda.nl" + url for url in urls]
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@staticmethod
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def get_value(soup: BeautifulSoup, selector: str) -> str:
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"""Use CSS selector to find certain features."""
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try:
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return soup.select(selector)[0].text
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except IndexError:
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return "na"
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def scrape_from_url(self, url: str) -> List[str]:
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"""Scrape all the features from one house item given a link. """
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# Initialize for each page
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response = requests.get(url, headers=config.header)
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soup = BeautifulSoup(response.text, "lxml")
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# Get the value according to respective CSS selectors
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list_since_selector = (
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self.selectors.listed_since
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if self.to_buy
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else ".fd-align-items-center:nth-child(7) span"
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)
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result = [
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url,
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self.get_value(soup, self.selectors.price),
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self.get_value(soup, self.selectors.address),
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self.get_value(soup, self.selectors.descrip),
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self.get_value(soup, list_since_selector).replace("\n", ""),
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self.get_value(soup, self.selectors.zip_code)
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.replace("\n", "")
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.replace("\r ", ""),
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self.get_value(soup, self.selectors.size),
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self.get_value(soup, self.selectors.year),
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self.get_value(soup, self.selectors.living_area),
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self.get_value(soup, self.selectors.kind_of_house),
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self.get_value(soup, self.selectors.building_type),
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self.get_value(soup, self.selectors.num_of_rooms).replace("\n", ""),
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self.get_value(soup, self.selectors.num_of_bathrooms).replace("\n", ""),
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self.get_value(soup, self.selectors.layout),
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self.get_value(soup, self.selectors.energy_label).replace(
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"\r\n ", ""
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),
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self.get_value(soup, self.selectors.insulation).replace("\n", ""),
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self.get_value(soup, self.selectors.heating).replace("\n", ""),
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self.get_value(soup, self.selectors.ownership).replace("\n", ""),
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self.get_value(soup, self.selectors.exteriors),
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self.get_value(soup, self.selectors.parking),
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self.get_value(soup, self.selectors.neighborhood_name),
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self.get_value(soup, self.selectors.date_list),
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self.get_value(soup, self.selectors.date_sold),
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self.get_value(soup, self.selectors.term),
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self.get_value(soup, self.selectors.price_sold),
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self.get_value(soup, self.selectors.last_ask_price).replace("\n", ""),
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self.get_value(soup, self.selectors.last_ask_price_m2).split("\r")[0],
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]
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return result
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def scrape_pages(self) -> None:
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"""Scrape all the content acoss multiple pages."""
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logger.info("*** Phase 2: Start scraping results from individual links ***")
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df = pd.DataFrame({key: [] for key in self.selectors.keys()})
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# Scrape pages with multiprocessing to improve efficiency
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pools = mp.cpu_count()
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content = process_map(self.scrape_from_url, self.links, max_workers=pools)
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for i, c in enumerate(content):
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df.loc[len(df)] = c
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df["city"] = self.area
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df["log_id"] = datetime.datetime.now().strftime("%Y%m-%d%H-%M%S")
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logger.info(f"*** All scraping done: {df.shape[0]} results ***")
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self.raw_df = df
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def save_csv(self, df: pd.DataFrame, filepath: str = None) -> None:
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"""Save the result to a .csv file."""
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if filepath is None:
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self._check_dir()
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date = str(datetime.datetime.now().date()).replace("-", "")
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if self.find_past:
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if self.to_buy:
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status = "sold"
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else:
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status = "rented"
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else:
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if self.to_buy:
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status = "selling"
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else:
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status = "renting"
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filepath = (
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f"./data/houseprice_{date}_{self.area}_{status}_{len(self.links)}.csv"
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)
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df.to_csv(filepath, index=False)
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logger.info(f"*** File saved: {filepath}. ***")
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def run(self, raw_data: bool = False, save: bool = False, filepath: str = None) -> pd.DataFrame:
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"""Scrape all links and all content."""
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self.fetch_links()
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self.scrape_pages()
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if raw_data:
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if save:
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self.save_csv(self.raw_df, filepath)
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return self.raw_df
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else:
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logger.info("*** Cleaning data ***")
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clean_df = preprocess_data(df=self.raw_df, is_past=self.find_past)
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self.clean_df = clean_df
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if save:
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self.save_csv(self.clean_df, filepath)
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return clean_df
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logger.info("*** Done! ***")
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if __name__ == "__main__":
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scraper = FundaScraper(area="amsterdam", want_to="rent", find_past=False, n_pages=1)
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df = scraper.run()
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print(df.head()) |