I have been trying to scrape a website such as the one below. In the footer there are a bunch of links of their social media out of which the LinkedIn URL is the point of focus for me. Is there a way to fish out only that link maybe using regex or any other libraries available in Python.
This is what I have tried so far -
import requests
from bs4 import BeautifulSoup
url = "https://www.southcoast.org/"
req = requests.get(url)
soup = BeautifulSoup(reqs.text,"html.parser")
for link in soup.find_all('a'):
print(link.get('href'))
But I'm fetching all the URLs instead of the one I'm looking for.
Note: I'd appreciate a dynamic code which I can use for other sites as well.
Thanks in advance for you suggestion/help.
One approach could be to use css selectors and look for string linkedin.com/company/ in values of href attributes:
soup.select_one('a[href*="linkedin.com/company/"]')['href']
Example
import requests
from bs4 import BeautifulSoup
url = "https://www.southcoast.org/"
req = requests.get(url)
soup = BeautifulSoup(req.text,"html.parser")
# single (first) link
link = e['href'] if(e := soup.select_one('a[href*="linkedin.com/company/"]')) else None
# multiple links
links = [link['href'] for link in soup.select('a[href*="linkedin.com/company/"]')]
I am trying to get a value from a webpage. In the source code of the webpage, the data is in CDATA format and also comes from a jQuery. I have managed to write the below code which gets a large amount of text, where the index 21 contains the information I need. However, this output is large and not in a format I understand. Within the output I need to isolate and output "redshift":"0.06" but dont know how. what is the best way to solve this.
import requests
from bs4 import BeautifulSoup
link = "https://wis-tns.weizmann.ac.il/object/2020aclx"
html = requests.get(link).text
soup = BeautifulSoup(html, "html.parser")
res = soup.findAll('b')
print soup.find_all('script')[21]
It can be done using the current approach you have. However, I'd advise against it. There's a neater way to do it by observing that the redshift value is present in a few convenient places on the page itself.
The following approach should work for you. It looks for tables on the page with the class "atreps-results-table" -- of which there are two. We take the second such table and look for the table cell with the class "cell-redshift". Then, we just print out its text content.
from bs4 import BeautifulSoup
import requests
link = 'https://wis-tns.weizmann.ac.il/object/2020aclx'
html = requests.get(link).text
soup = BeautifulSoup(html, 'html.parser')
tab = soup.find_all('table', {'class': 'atreps-results-table'})[1]
redshift = tab.find('td', {'class': 'cell-redshift'})
print(redshift.text)
Try simply:
soup.select_one('div.field-redshift > div.value>b').text
If you view the Page Source of the URL, you will find that there are two script elements that are having CDATA. But the script element in which you are interested has jQuery in it. So you have to select the script element based on this knowledge. After that, you need to do some cleaning to get rid of CDATA tags and jQuery. Then with the help of json library, convert JSON data to Python Dictionary.
import requests
from bs4 import BeautifulSoup
import json
page = requests.get('https://wis-tns.weizmann.ac.il/object/2020aclx')
htmlpage = BeautifulSoup(page.text, 'html.parser')
scriptelements = htmlpage.find_all('script')
for script in scriptelements:
if 'CDATA' in script.text and 'jQuery' in script.text:
scriptcontent = script.text.replace('<!--//--><![CDATA[//>', '').replace('<!--', '').replace('//--><!]]>', '').replace('jQuery.extend(Drupal.settings,', '').replace(');', '')
break
jsondata = json.loads(scriptcontent)
print(jsondata['objectFlot']['plotMain1']['params']['redshift'])
I am trying to scrape data to get the text I need. I want to find the line that says aberdeen and all lines after it which contain the airport info. Here is a pic of the html hierarchy:
I am trying to locate the text elements inside the class "i1" with this code:
import requests
from bs4 import BeautifulSoup
page = requests.get('http://www.airportcodes.org/')
soup = BeautifulSoup(page.text, 'html.parser')
table = soup.find('div',attrs={"class":"i1"})
print(table.text)
But I am not getting the values I expect at all. Here is a link to the data if curious. I am new to scraping obviously.
The problem is your BeautifulSoup parser:
import requests
from bs4 import BeautifulSoup
page = requests.get('http://www.airportcodes.org/')
soup = BeautifulSoup(page.text, 'lxml')
table = soup.find('div',attrs={"class":"i1"})
print(table.text)
If what you want is the text elements, you can use:
soup.get_text()
Note: this will give you all the text elements.
why are people suggesting selenium? this doesnt dynamically load the data ... requests + re is all you need, you dont even need beautiful soup
data = requests.get('http://www.airportcodes.org/').content
cities_and_codes =re.findall("([A-Za-z, ]+)\(([A-Z]{3})\)",data)
just look for any alphanumeric characters (including also comma and space)
followed by exactly 3 uppercase letters in parenthesis
I'm trying to Scrape a blog "https://blog.feedspot.com/ai_rss_feeds/" and crawl through all the links in it to look for Artificial Intelligence related information in each of the crawled links.
The blog follows a pattern - It has multiple RSS Feeds and each Feed has an attribute called "Site" in the UI. I need to get all the links in the "Site" attribute. Example : aitrends.com, sciecedaily.com/... etc. In the code, the main div has a class called "rss-block", which has another nested class called "data" and each data has several tags and the tags have in them. The value in href gives the links to be crawled upon. We need to look for AI related articles in each of those links found by scraping the above-mentioned structure.
I've tried various variations of the following code but nothing seemed to help much.
import requests
from bs4 import BeautifulSoup
page = requests.get('https://blog.feedspot.com/ai_rss_feeds/')
soup = BeautifulSoup(page.text, 'html.parser')
class_name='data'
dataSoup = soup.find(class_=class_name)
print(dataSoup)
artist_name_list_items = dataSoup.find('a', href=True)
print(artist_name_list_items)
I'm struggling to even get the links in that page, let alone craling through each of these links to scrape articles related to AI in them.
If you could help me finish both the parts of the problem, that'd be a great learning for me. Please refer to the source of https://blog.feedspot.com/ai_rss_feeds/ for the HTML Structure. Thanks in advance!
The first twenty results are stored in the html as you see on page. The others are pulled from a script tag and you can regex them out to create the full list of 67. Then loop that list and issue requests to those for further info. I offer a choice of two different selectors for the initial list population (the second - commented out - uses :contains - available with bs4 4.7.1+)
from bs4 import BeautifulSoup as bs
import requests, re
p = re.compile(r'feed_domain":"(.*?)",')
with requests.Session() as s:
r = s.get('https://blog.feedspot.com/ai_rss_feeds/')
soup = bs(r.content, 'lxml')
results = [i['href'] for i in soup.select('.data [rel="noopener nofollow"]:last-child')]
## or use with bs4 4.7.1 +
#results = [i['href'] for i in soup.select('strong:contains(Site) + a')]
results+= [re.sub(r'\n\s+','',i.replace('\\','')) for i in p.findall(r.text)]
for link in results:
#do something e.g.
r = s.get(link)
soup = bs(r.content, 'lxml')
# extract info from indiv page
To get all the sublinks for each block, you can use soup.find_all:
from bs4 import BeautifulSoup as soup
import requests
d = soup(requests.get('https://blog.feedspot.com/ai_rss_feeds/').text, 'html.parser')
results = [[i['href'] for i in c.find('div', {'class':'data'}).find_all('a')] for c in d.find_all('div', {'class':'rss-block'})]
Output:
[['http://aitrends.com/feed', 'https://www.feedspot.com/?followfeedid=4611684', 'http://aitrends.com/'], ['https://www.sciencedaily.com/rss/computers_math/artificial_intelligence.xml', 'https://www.feedspot.com/?followfeedid=4611682', 'https://www.sciencedaily.com/news/computers_math/artificial_intelligence/'], ['http://machinelearningmastery.com/blog/feed', 'https://www.feedspot.com/?followfeedid=4575009', 'http://machinelearningmastery.com/blog/'], ['http://news.mit.edu/rss/topic/artificial-intelligence2', 'https://www.feedspot.com/?followfeedid=4611685', 'http://news.mit.edu/topic/artificial-intelligence2'], ['https://www.reddit.com/r/artificial/.rss', 'https://www.feedspot.com/?followfeedid=4434110', 'https://www.reddit.com/r/artificial/'], ['https://chatbotsmagazine.com/feed', 'https://www.feedspot.com/?followfeedid=4470814', 'https://chatbotsmagazine.com/'], ['https://chatbotslife.com/feed', 'https://www.feedspot.com/?followfeedid=4504512', 'https://chatbotslife.com/'], ['https://aws.amazon.com/blogs/ai/feed', 'https://www.feedspot.com/?followfeedid=4611538', 'https://aws.amazon.com/blogs/ai/'], ['https://developer.ibm.com/patterns/category/artificial-intelligence/feed', 'https://www.feedspot.com/?followfeedid=4954414', 'https://developer.ibm.com/patterns/category/artificial-intelligence/'], ['https://lexfridman.com/category/ai/feed', 'https://www.feedspot.com/?followfeedid=4968322', 'https://lexfridman.com/ai/'], ['https://medium.com/feed/#Francesco_AI', 'https://www.feedspot.com/?followfeedid=4756982', 'https://medium.com/#Francesco_AI'], ['https://blog.netcoresmartech.com/rss.xml', 'https://www.feedspot.com/?followfeedid=4998378', 'https://blog.netcoresmartech.com/'], ['https://www.aitimejournal.com/feed', 'https://www.feedspot.com/?followfeedid=4979214', 'https://www.aitimejournal.com/'], ['https://blogs.nvidia.com/feed', 'https://www.feedspot.com/?followfeedid=4611576', 'https://blogs.nvidia.com/'], ['http://feeds.feedburner.com/AIInTheNews', 'https://www.feedspot.com/?followfeedid=623918', 'http://aitopics.org/whats-new'], ['https://blogs.technet.microsoft.com/machinelearning/feed', 'https://www.feedspot.com/?followfeedid=4431827', 'https://blogs.technet.microsoft.com/machinelearning/'], ['https://machinelearnings.co/feed', 'https://www.feedspot.com/?followfeedid=4611235', 'https://machinelearnings.co/'], ['https://www.artificial-intelligence.blog/news?format=RSS', 'https://www.feedspot.com/?followfeedid=4611100', 'https://www.artificial-intelligence.blog/news/'], ['https://news.google.com/news?cf=all&hl=en&pz=1&ned=us&q=artificial+intelligence&output=rss', 'https://www.feedspot.com/?followfeedid=4611157', 'https://news.google.com/news/section?q=artificial%20intelligence&tbm=nws&*'], ['https://www.youtube.com/feeds/videos.xml?channel_id=UCEqgmyWChwvt6MFGGlmUQCQ', 'https://www.feedspot.com/?followfeedid=4611505', 'https://www.youtube.com/channel/UCEqgmyWChwvt6MFGGlmUQCQ/videos']]
I've made a scraper in python. It is running smoothly. Now I would like to discard or accept specific links from that page as in, links only containing "mobiles" but even after making some conditional statement I can't do so. Hope I'm gonna get any help to rectify my mistakes.
import requests
from bs4 import BeautifulSoup
def SpecificItem():
url = 'https://www.flipkart.com/'
Process = requests.get(url)
soup = BeautifulSoup(Process.text, "lxml")
for link in soup.findAll('div',class_='')[0].findAll('a'):
if "mobiles" not in link:
print(link.get('href'))
SpecificItem()
On the other hand if I do the same thing using lxml library with xpath, It works.
import requests
from lxml import html
def SpecificItem():
url = 'https://www.flipkart.com/'
Process = requests.get(url)
tree = html.fromstring(Process.text)
links = tree.xpath('//div[#class=""]//a/#href')
for link in links:
if "mobiles" not in link:
print(link)
SpecificItem()
So, at this point i think with BeautifulSoup library the code should be somewhat different to get the purpose served.
The root of your problem is your if condition works a bit differently between BeautifulSoup and lxml. Basically, if "mobiles" not in link: with BeautifulSoup is not checking if "mobiles" is in the href field. I didn't look too hard but I'd guess it's comparing it to the link.text field instead. Explicitly using the href field does the trick:
import requests
from bs4 import BeautifulSoup
def SpecificItem():
url = 'https://www.flipkart.com/'
Process = requests.get(url)
soup = BeautifulSoup(Process.text, "lxml")
for link in soup.findAll('div',class_='')[0].findAll('a'):
href = link.get('href')
if "mobiles" not in href:
print(href)
SpecificItem()
That prints out a bunch of links and none of them include "mobiles".