School/february/task_180222/ss_scraper.py
2022-08-02 20:34:11 +03:00

127 lines
4.5 KiB
Python

# Author - Kristiāns Francis Cagulis
# Date - 21.02.2022
# Title - Patstāvīgais darbs "SS.com scraping"
from bs4 import BeautifulSoup
import requests
import pandas as pd
from loadbar import LoadBar
from os import mkdir, listdir
from datetime import datetime
HEADERS = {
"User-Agent":
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.97 Safari/537.36 Vivaldi/4.1.2369.21'
}
class SS:
def __init__(self, url, name):
self.url = url
self.name = name
def _get_page_amount(self):
page = requests.get(self.url, headers=HEADERS)
soup = BeautifulSoup(page.content, 'html.parser')
try:
last_url = soup.find(class_='td2').findChild('a')['href']
page_amount = last_url[last_url.find(
"page") + 4:last_url.find(".html")]
except:
page_amount = 1
# print(f"Page amount = {page_amount}")
return int(page_amount)
def get_data(self):
items = []
item_no = 1
page_amount = self._get_page_amount()
# widgets = ["Getting data...", pbar.Bar("*")]
# bar = pbar.ProgressBar(max_value=page_amount, widgets=widgets).start()
bar = LoadBar(max=page_amount * 30, head="#", body="#")
bar.start()
for page_number in range(1, page_amount + 1):
url = self.url + f"/page{page_number}.html"
page = requests.get(url, headers=HEADERS)
soup = BeautifulSoup(page.content, 'html.parser')
# item ids
ids = [tag['id']
for tag in soup.select('tr[id]')] # creates list with ids
# removes "tr_bnr" elements from list
ids = [x for x in ids if "tr_bnr" not in x]
ids.remove("head_line") # removes first "head_line" id
# print(f"Page {page_number}")
# getting item data
for id in soup.find_all(id=ids):
# print(f"Item {item_no}")
bar.update(step=item_no)
item_no += 1
for elem in id.find_all(class_='msga2-o pp6'):
items.append(elem.get_text())
if len(id.find_all(class_='msga2-o pp6')) == 7:
del items[-2]
# adverts url
item_url = id.findChild(class_='msg2').findChild(
'div').findChild('a')['href'] # gets url
item_url = "https://www.ss.com" + item_url
item_page = requests.get(item_url, headers=HEADERS)
item_soup = BeautifulSoup(item_page.content, 'html.parser')
# adverts full text
item_text = item_soup.find(
id='msg_div_msg').get_text() # gets full text
# removes text last part (table)
item_text = item_text[:item_text.find("Pilsēta:")]
items.append(item_text)
# adverts publication date
# gets all 'msg_footer' class'
item_date = item_soup.find_all('td', class_='msg_footer')
item_date = item_date[2].get_text() # extracts 3rd element
items.append(item_date[8:18]) # crops date
bar.end()
chunk_size = 8
# combines each 'chunk_size' elements into array
chunked_items_list = [items[i:i + chunk_size]
for i in range(0, len(items), chunk_size)]
columns = ["Atrašanās vieta", "Istabu skaits", "Kvadratūra", "Stāvs",
"Sērija", "Cena", "Pilns sludinājuma teksts", "Izvietošanas datums"]
df = pd.DataFrame(chunked_items_list, columns=columns)
time = datetime.now().strftime("%d%m%y%H%M%S") # current time
if "excel" not in listdir("output"):
mkdir("output/excel")
df.to_excel(
excel_writer=f"output/excel/ss_{self.name}_{time}.xlsx", index=False)
flats_riga = SS(
"https://www.ss.com/lv/real-estate/flats/riga/all/sell/", "riga")
flats_rigareg = SS(
"https://www.ss.com/lv/real-estate/flats/riga-region/all/sell/", "rigareg")
flats_aizkraukle = SS(
"https://www.ss.com/lv/real-estate/flats/aizkraukle-and-reg/sell/", "aizkraukle")
flats_tukums = SS(
"https://www.ss.com/lv/real-estate/flats/tukums-and-reg/sell/", "tukums")
flats_ogre = SS(
"https://www.ss.com/lv/real-estate/flats/ogre-and-reg/sell/", "ogre")
def main():
flats_riga.get_data()
# flats_rigareg.get_data()
if __name__ == '__main__':
main()