I've recently started looking into purchasing some land, and I'm writing a little app to help me organize details in Jira/Confluence to help me keep track of who I've talked to and what I talked to them about in regards to each parcel of land individually.
So, I wrote this little scraper for landwatch(dot)com:
[url is just a listing on the website]
from bs4 import BeautifulSoup
import requests
def get_property_data(url):
headers = ({'User-Agent':
'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'})
response = requests.get(url, headers=headers) # Maybe request Url with read more already gone
soup = BeautifulSoup(response.text, 'html5lib')
title = soup.find_all(class_='b442a')[0].text
details = soup.find_all('p', class_='d19de')
price = soup.find_all('div', class_='_260f0')[0].text
deets = []
for i in range(len(details)):
if details[i].text != '':
deets.append(details[i].text)
detail = ''
for i in deets:
detail += '<p>' + i + '</p>'
return [title, detail, price]
Everything works great except that the class d19de has a ton of values hidden behind the Read More button.
While Googling away at this, I discovered How to Scrape reviews with read more from Webpages using BeautifulSoup, however I either don't understand what they're doing well enough to implement it, or this just doesn't work anymore:
import requests ; from bs4 import BeautifulSoup
soup = BeautifulSoup(requests.get("http://www.mouthshut.com/product-reviews/Lakeside-Chalet-Mumbai-reviews-925017044").text, "html.parser")
for title in soup.select("a[id^=ctl00_ctl00_ContentPlaceHolderFooter_ContentPlaceHolderBody_rptreviews_]"):
items = title.get('href')
if items:
broth = BeautifulSoup(requests.get(items).text, "html.parser")
for item in broth.select("div.user-review p.lnhgt"):
print(item.text)
Any thoughts on how to bypass that Read More button? I'm really hoping to do this in BeautifulSoup, and not selenium.
Here's an example URL for testing: https://www.landwatch.com/huerfano-county-colorado-recreational-property-for-sale/pid/410454403
That data is present within a script tag. Here is an example of extracting that content, parsing with json, and outputting land description info as a list:
from bs4 import BeautifulSoup
import requests, json
url = 'https://www.landwatch.com/huerfano-county-colorado-recreational-property-for-sale/pid/410454403'
headers = ({'User-Agent':
'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'})
response = requests.get(url, headers=headers) # Maybe request Url with read more already gone
soup = BeautifulSoup(response.text, 'html5lib')
all_data = json.loads(soup.select_one('[type="application/ld+json"]').string)
details = all_data['description'].split('\r\r')
You may wish to examine what else is in that script tag:
from pprint import pprint
pprint(all_data)
Related
BeautifulSoup doesn’t find any tag on this page. Does anyone know what the problem can be?
I can find elements on the page with selenium, but since I have a list of pages, I don’t want to use selenium.
import requests
from bs4 import BeautifulSoup
url = 'https://dzen.ru/news/story/VMoskovskoj_oblasti_zapushhen_chat-bot_ochastichnoj_mobilizacii--b093f9a22a32ed6731e4a4ca50545831?lang=ru&from=reg_portal&fan=1&stid=fOB6O7PV5zeCUlGyzvOO&t=1664886434&persistent_id=233765704&story=90139eae-79df-5de1-9124-0d830e4d59a5&issue_tld=ru'
page = requests.get(url)
soup = BeautifulSoup(page.text, 'lxml')
soup.find_all('h1')
You can get the info on that page by adding headers to your requests, mimicking what you can see in Dev tools - Network tab main request to that url. Here is one way to get all links from that page:
import requests
from bs4 import BeautifulSoup as bs
headers = {
'Cookie': 'sso_checked=1',
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.5112.79 Safari/537.36'
}
url = 'https://dzen.ru/news/story/VMoskovskoj_oblasti_zapushhen_chat-bot_ochastichnoj_mobilizacii--b093f9a22a32ed6731e4a4ca50545831?lang=ru&from=reg_portal&fan=1&stid=fOB6O7PV5zeCUlGyzvOO&t=1664886434&persistent_id=233765704&story=90139eae-79df-5de1-9124-0d830e4d59a5&issue_tld=ru'
r = requests.get(url, headers=headers)
soup = bs(r.text, 'html.parser')
links = [a.get('href') for a in soup.select('a')]
print(links)
Result printed in terminal:
['/news', 'https://dzen.ru/news', 'https://dzen.ru/news/region/moscow', 'https://dzen.ru/news/rubric/mobilizatsiya', 'https://dzen.ru/news/rubric/personal_feed', 'https://dzen.ru/news/rubric/politics', 'https://dzen.ru/news/rubric/society', 'https://dzen.ru/news/rubric/business', 'https://dzen.ru/news/rubric/world', 'https://dzen.ru/news/rubric/sport', 'https://dzen.ru/news/rubric/incident', 'https://dzen.ru/news/rubric/culture', 'https://dzen.ru/news/rubric/computers', 'https://dzen.ru/news/rubric/science', 'https://dzen.ru/news/rubric/auto', 'https://www.mosobl.kp.ru/online/news/4948743/?utm_source=yxnews&utm_medium=desktop', 'https://www.mosobl.kp.ru/online/news/4948743/?utm_source=yxnews&utm_medium=desktop', 'https://www.mosobl.kp.ru/online/news/4948743/?utm_source=yxnews&utm_medium=desktop', 'https://mosregtoday.ru/soc/v-podmoskove-zapustili-chat-bot-po-voprosam-chastichnoj-mobilizacii/?utm_source=yxnews&utm_medium=desktop', ...]
Hi Everyone receive error msg when executing this code :
from bs4 import BeautifulSoup
import requests
import html.parser
from requests_html import HTMLSession
session = HTMLSession()
response = session.get("https://www.imdb.com/chart/boxoffice/?ref_=nv_ch_cht")
soup = BeautifulSoup(response.content, 'html.parser')
tables = soup.find_all("tr")
for table in tables:
movie_name = table.find("span", class_ = "secondaryInfo")
print(movie_name)
output:
movie_name = table.find("span", class_ = "secondaryInfo").text
AttributeError: 'NoneType' object has no attribute 'text'
You selected for the first row which is the header and doesn't have that class as it doesn't list the prices. An alternative way is to simply exclude the header with a css selector of nth-child(n+2). You also only need requests.
from bs4 import BeautifulSoup
import requests
response = requests.get("https://www.imdb.com/chart/boxoffice/?ref_=nv_ch_cht")
soup = BeautifulSoup(response.content, 'html.parser')
for row in soup.select('tr:nth-child(n+2)'):
movie_name = row.find("span", class_ = "secondaryInfo")
print(movie_name.text)
Just use the SelectorGadget Chrome extension to grab CSS selector by clicking on the desired element in your browser without inventing anything superfluous. However, it's not working perfectly if the HTML structure is terrible.
You're looking for this:
for result in soup.select(".titleColumn a"):
movie_name = result.text
Also, there's no need in using HTMLSession IF you don't want to persist certain parameters across requests to the same host (website).
Code and example in the online IDE:
from bs4 import BeautifulSoup
import requests
# user-agent is used to act as a real user visit
# this could reduce the chance (a little bit) of being blocked by a website
# and prevent from IP limit block or permanent ban
headers = {
"User-Agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
response = requests.get("https://www.imdb.com/chart/boxoffice/?ref_=nv_ch_cht", headers=headers)
soup = BeautifulSoup(response.text, 'html.parser')
for result in soup.select(".titleColumn a"):
movie_name = result.text
print(movie_name)
# output
'''
Eternals
Dune: Part One
No Time to Die
Venom: Let There Be Carnage
Ron's Gone Wrong
The French Dispatch
Halloween Kills
Spencer
Antlers
Last Night in Soho
'''
P.S. There's a dedicated web scraping blog of mine. If you need to parse search engines, have a try using SerpApi.
Disclaimer, I work for SerpApi.
This is the URL where I'm trying to extract the shipping price:
url = "https://www.amazon.com/AmazonBasics-Ultra-Soft-Micromink-Sherpa-Blanket/dp/B0843ZJGNP/ref=sr_1_1_sspa?dchild=1&keywords=amazonbasics&pd_rd_r=5cb1aaf8-d692-4abf-9131-ebd533ad5763&pd_rd_w=8Uw69&pd_rd_wg=kTKEB&pf_rd_p=9349ffb9-3aaa-476f-8532-6a4a5c3da3e7&pf_rd_r=PYFBYA98FS6B8BR7TGJD&qid=1623412994&sr=8-1-spons&psc=1&spLa=ZW5jcnlwdGVkUXVhbGlmaWVyPUEzM0xaSFIzVzFTUUpMJmVuY3J5cHRlZElkPUEwNzk3MjgzM1NQRlFQQkc4VFJGWSZlbmNyeXB0ZWRBZElkPUEwNzU1NzM0M0VMQ1hTNDJFTzYxQyZ3aWRnZXROYW1lPXNwX2F0ZiZhY3Rpb249Y2xpY2tSZWRpcmVjdCZkb05vdExvZ0NsaWNrPXRydWU="
My code is:
r = requests.get(url,headers=HEADERS,proxies=proxyDict)
soup = BeautifulSoup(r.content,'html.parser')
needle="$93.63"
#I also tried complete sentences
#"$93.63 Shipping & Import Fees Deposit to India"
#"$93.63 Shipping & Import Fees Deposit to India"
print(soup.find_all(text=needle))
#I also tried print(soup.find_all(text=re.compile(needle)))
But this always returns an empty list.
I can see the required text in inspect element as well as downloaded soup that I printed on the console.
However when I do the same thing with the actual product price($27.99), soup.find_all() works as expected.
So far I haven't been able to figure out the problem here. Sorry for any silly mistakes.
Search the field, not the values.
import requests
from bs4 import BeautifulSoup
url = "https://www.amazon.com/AmazonBasics-Ultra-Soft-Micromink-Sherpa-Blanket/dp/B0843ZJGNP/ref=sr_1_1_sspa?dchild=1&keywords=amazonbasics&pd_rd_r=5cb1aaf8-d692-4abf-9131-ebd533ad5763&pd_rd_w=8Uw69&pd_rd_wg=kTKEB&pf_rd_p=9349ffb9-3aaa-476f-8532-6a4a5c3da3e7&pf_rd_r=PYFBYA98FS6B8BR7TGJD&qid=1623412994&sr=8-1-spons&psc=1&spLa=ZW5jcnlwdGVkUXVhbGlmaWVyPUEzM0xaSFIzVzFTUUpMJmVuY3J5cHRlZElkPUEwNzk3MjgzM1NQRlFQQkc4VFJGWSZlbmNyeXB0ZWRBZElkPUEwNzU1NzM0M0VMQ1hTNDJFTzYxQyZ3aWRnZXROYW1lPXNwX2F0ZiZhY3Rpb249Y2xpY2tSZWRpcmVjdCZkb05vdExvZ0NsaWNrPXRydWU="
HEADERS = ({'User-Agent':
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36',
'Accept-Language': 'en-US, en;q=0.5'})
r = requests.get(url, headers=HEADERS)
soup = BeautifulSoup(r.content,'html.parser')
value = soup.find("span", {"id" : "priceblock_ourprice"}).contents
print(value)
from bs4 import BeautifulSoup as bs
import requests
url = "https://www.amazon.com/AmazonBasics-Ultra-Soft-Micromink-Sherpa-Blanket/dp/B0843ZJGNP/ref=sr_1_1_sspa?dchild=1&keywords=amazonbasics&pd_rd_r=5cb1aaf8-d692-4abf-9131-ebd533ad5763&pd_rd_w=8Uw69&pd_rd_wg=kTKEB&pf_rd_p=9349ffb9-3aaa-476f-8532-6a4a5c3da3e7&pf_rd_r=PYFBYA98FS6B8BR7TGJD&qid=1623412994&sr=8-1-spons&psc=1&spLa=ZW5jcnlwdGVkUXVhbGlmaWVyPUEzM0xaSFIzVzFTUUpMJmVuY3J5cHRlZElkPUEwNzk3MjgzM1NQRlFQQkc4VFJGWSZlbmNyeXB0ZWRBZElkPUEwNzU1NzM0M0VMQ1hTNDJFTzYxQyZ3aWRnZXROYW1lPXNwX2F0ZiZhY3Rpb249Y2xpY2tSZWRpcmVjdCZkb05vdExvZ0NsaWNrPXRydWU="
soup = bs(requests.get(url).content, 'lxml').prettify()
print(soup)
I am trying to extract text from websites using BeautifulSoup but willing to explore other options. Currently I am trying to use something like this:
from bs4 import BeautifulSoup
from urllib.request import Request, urlopen
boston_url = 'https://www.mass.gov/service-details/request-for-proposal-rfp-notices'
hdr = {'User-Agent': 'Mozilla/5.0'}
req = Request(boston_url,headers=hdr)
webpage = urlopen(req)
htmlText = webpage.read().decode('utf-8')
pageText = BeautifulSoup(htmlText, "html.parser")
body = pageText.find_all(text=True)
The goal being to figure out how to extract the text in the red box.You can see the output I get from the CMD photo below. It is very messy and i'm not sure how to find body paragraphs of text from that. I could loop over the output and look for certain words but I need to do this to multiple sites and I won't know what's in the body paragraph.
It's probably simpler than you make it. Let's try to simplify it:
import requests
from bs4 import BeautifulSoup as bs
boston_url = 'https://www.mass.gov/service-details/request-for-proposal-rfp-notices'
hdr = {'User-Agent': 'Mozilla/5.0'}
req = requests.get(boston_url,headers=hdr)
soup = bs(req.text,'lxml')
soup.select('main main div.ma__rich-text>p')[0].text
Output:
'PERAC has not reviewed the RFP notices or other related materials posted on this page for compliance with M.G.L. Chapter 32, section 23B. The publication of these notices should not be interpreted as an indication that PERAC has made a determination as to that compliance.'
You can use the bs.find('p', text=re.compile('PERAC')) to extract that paragraph:
from bs4 import BeautifulSoup
import requests
import re
headers = {
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
'AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/83.0.4103.61 Safari/537.36'
}
boston_url = (
'https://www.mass.gov/service-details/request-for-proposal-rfp-notices'
)
resp = requests.get(boston_url, headers=headers)
bs = BeautifulSoup(resp.text)
bs.find('p', text=re.compile('PERAC'))
I'm trying to scrape the likes and retweets from the results of a Twitter search.
After running the Python below, I get an empty list, []. I'm not using the Twitter API because it doesn't look at the tweets by hashtag this far back.
The code I'm using is:
from bs4 import BeautifulSoup
import requests
url = 'https://twitter.com/search?q=%23bangkokbombing%20since%3A2015-08-10%20until%3A2015-09-30&src=typd&lang=en'
r = requests.get(url)
data = r.text
soup = BeautifulSoup(data, "lxml")
all_likes = soup.find_all('span', class_='ProfileTweet-actionCountForPresentation')
print(all_likes)
I can successfully save the html to file using this code. It is missing large amounts of information when I search the text, such as the class names I am looking for...
So (part of) the problem is apparently in accurately accessing the source code.
filename = 'newfile2.txt'
with open(filename, 'w') as handle:
handle.writelines(str(data))
This screenshot shows the span that I'm trying to scrape.
I've looked at this question, and others like it, but I'm not quite getting there.
How can I use BeautifulSoup to get deeply nested div values?
It seems that your GET request returns valid HTML but with no tweet elements in the #timeline element. However, adding a user agent to the request headers seems to remedy this.
from bs4 import BeautifulSoup
import requests
url = 'https://twitter.com/search?q=%23bangkokbombing%20since%3A2015-08-10%20until%3A2015-09-30&src=typd&lang=en'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'}
r = requests.get(url, headers=headers)
data = r.text
soup = BeautifulSoup(data, "lxml")
all_likes = soup.find_all('span', class_='ProfileTweet-actionCountForPresentation')
print(all_likes)