I have a next link which represent an exact graph I want to scrape: https://index.minfin.com.ua/ua/economy/index/svg.php?indType=1&fromYear=2010&acc=1
I'm simply can't understand is it a xml or svg graph and how to scrape data. I think I need to use bs4, requests but don't know the way to do that.
Anyone could help?
You will load HTML like this:
import requests
url = "https://index.minfin.com.ua/ua/economy/index/svg.php?indType=1&fromYear=2010&acc=1"
resp = requests.get(url)
data = resp.text
Then you will create a BeatifulSoup object with this HTML.
from bs4 import BeautifulSoup
soup = BeautifulSoup(html, features="html.parser")
After this, it is usually very subjective how to parse out what you want. The candidate codes may vary a lot. This is how I did it:
Using BeautifulSoup, I parsed all "rect"s and check if "onmouseover" exists in that rect.
rects = soup.svg.find_all("rect")
yx_points = []
for rect in rects:
if rect.has_attr("onmouseover"):
text = rect["onmouseover"]
x_start_index = text.index("'") + 1
y_finish_index = text[x_start_index:].index("'") + x_start_index
yx = text[x_start_index:y_finish_index].split()
print(text[x_start_index:y_finish_index])
yx_points.append(yx)
As you can see from the image below, I scraped onmouseover= part and get those 02.2015 155,1 parts.
Here, this is how yx_points looks like now:
[['12.2009', '100,0'], ['01.2010', '101,8'], ['02.2010', '103,7'], ...]
from bs4 import BeautifulSoup
import requests
import re
#First get all the text from the url.
url="https://index.minfin.com.ua/ua/economy/index/svg.php?indType=1&fromYear=2010&acc=1"
response = requests.get(url)
html = response.text
#Find all the tags in which the data is stored.
soup = BeautifulSoup(html, 'lxml')
texts = soup.findAll("rect")
final = []
for each in texts:
names = each.get('onmouseover')
try:
q = re.findall(r"'(.*?)'", names)
final.append(q[0])
except Exception as e:
print(e)
#The details are appended to the final variable
Related
I've struggled on this for days and not sure what the issue could be - basically, I'm trying to extract the profile box data (picture below) of each link -- going through inspector, I thought I could pull the p tags and do so.
I'm new to this and trying to understand, but here's what I have thus far:
-- a code that (somewhat) succesfully pulls the info for ONE link:
import requests
from bs4 import BeautifulSoup
# getting html
url = 'https://basketball.realgm.com/player/Darius-Adams/Summary/28720'
req = requests.get(url)
soup = BeautifulSoup(req.text, 'html.parser')
container = soup.find('div', attrs={'class', 'main-container'})
playerinfo = container.find_all('p')
print(playerinfo)
I then also have a code that pulls all of the HREF tags from multiple links:
from bs4 import BeautifulSoup
import requests
def get_links(url):
links = []
website = requests.get(url)
website_text = website.text
soup = BeautifulSoup(website_text)
for link in soup.find_all('a'):
links.append(link.get('href'))
for link in links:
print(link)
print(len(links))
get_links('https://basketball.realgm.com/dleague/players/2022')
get_links('https://basketball.realgm.com/dleague/players/2021')
get_links('https://basketball.realgm.com/dleague/players/2020')
So basically, my goal is to combine these two, and get one code that will pull all of the P tags from multiple URLs. I've been trying to do it, and I'm really not sure at all why this isn't working here:
from bs4 import BeautifulSoup
import requests
def get_profile(url):
profiles = []
req = requests.get(url)
soup = BeautifulSoup(req.text, 'html.parser')
container = soup.find('div', attrs={'class', 'main-container'})
for profile in container.find_all('a'):
profiles.append(profile.get('p'))
for profile in profiles:
print(profile)
get_profile('https://basketball.realgm.com/player/Darius-Adams/Summary/28720')
get_profile('https://basketball.realgm.com/player/Marial-Shayok/Summary/26697')
Again, I'm really new to web scraping with Python but any advice would be greatly appreciated. Ultimately, my end goal is to have a tool that can scrape this data in a clean way all at once.
(Player name, Current Team, Born, Birthplace, etc).. maybe I'm doing it entirely wrong but any guidance is welcome!
You need to combine your two scripts together and make requests for each player. Try the following approach. This searches for <td> tags that have the data-td=Player attribute:
import requests
from bs4 import BeautifulSoup
def get_links(url):
data = []
req_url = requests.get(url)
soup = BeautifulSoup(req_url.content, "html.parser")
for td in soup.find_all('td', {'data-th' : 'Player'}):
a_tag = td.a
name = a_tag.text
player_url = a_tag['href']
print(f"Getting {name}")
req_player_url = requests.get(f"https://basketball.realgm.com{player_url}")
soup_player = BeautifulSoup(req_player_url.content, "html.parser")
div_profile_box = soup_player.find("div", class_="profile-box")
row = {"Name" : name, "URL" : player_url}
for p in div_profile_box.find_all("p"):
try:
key, value = p.get_text(strip=True).split(':', 1)
row[key.strip()] = value.strip()
except: # not all entries have values
pass
data.append(row)
return data
urls = [
'https://basketball.realgm.com/dleague/players/2022',
'https://basketball.realgm.com/dleague/players/2021',
'https://basketball.realgm.com/dleague/players/2020',
]
for url in urls:
print(f"Getting: {url}")
data = get_links(url)
for entry in data:
print(entry)
import pandas as pd
from bs4 import BeautifulSoup
import requests
import os
url = 'https://fr.indeed.com/jobs?q=data%20anlayst&l=france'
#grabbing page content and parsing it into html
def data_grabber(url):
page = requests.get(url)
html = page.text
soup = BeautifulSoup(html, 'html.parser')
job_soup = soup.find_all('div', {"class":"job_seen_beacon"})
return job_soup
def job_title(url):
titles = data_grabber(url)
for title in titles:
t = title.find_all('tbody')
return t
this is my source code, and im testing it out in jupyter notebook to make sure my functions work correctly but I've hit a small road block. My html soup from my first function works perfectly. It grabs all the info from indeed, especially the job_seen_beacon class.
Mr job_title function is wrong because it only outputs the first 'tbody' class it finds. refer to image here, I don't have enough points on stack
while for my data_grabber it returns every single job_seen_beacon. If you were able to scroll, you would easily see the multiple job_seen_beacon's.
I'm clearly missing something but I can't see it, any ideas?
What happens?
In moment you are return something from a function you leave it and that happens in first iteration.
Not sure where you will end up with your code, but you can do something like that:
def job_title(item):
title = item.select_one('h2')
return title.get_text('|',strip=True).split('|')[-1] if title else 'No Title'
Example
from bs4 import BeautifulSoup
import requests
url = 'https://fr.indeed.com/jobs?q=data%20anlayst&l=france'
#grabbing page content and parsing it into html
def data_grabber(url):
page = requests.get(url)
html = page.text
soup = BeautifulSoup(html, 'html.parser')
job_soup = soup.find_all('div', {"class":"job_seen_beacon"})
return job_soup
def job_title(item):
title = item.select_one('h2')
return title.get_text('|',strip=True).split('|')[-1] if title else 'No Title'
def job_location(item):
location = item.select_one('div.companyLocation')
return location.get_text(strip=True) if location else 'No Location'
data = []
for item in data_grabber(url):
data.append({
'title':job_title(item),
'companyLocation':job_location(item)
})
data
Output
[{'title': 'Chef de Projet Big Data H/F', 'companyLocation': 'Lyon (69)'},{'title': 'Chef de Projet Big Data F/H', 'companyLocation': 'Lyon 9e (69)'}]
i am trying to scrape news from reuters but there is a click to view more at the bottom on the website. I could not know how to load the hidden results by using beautiful soup.
from bs4 import BeautifulSoup
import urllib.request
def scrape_reuters_news(ticker):
url = "https://www.reuters.com/search/news?sortBy=relevance&dateRange=pastWeek&blob="+ticker
scraped_data = urllib.request.urlopen(url)
scraped_data = scraped_data.read()
parsed_articles = BeautifulSoup(scraped_data, 'lxml')
links = parsed_articles.find_all("h3")
articles = []
titles = []
title_class = "Text__text___3eVx1j Text__dark-grey___AS2I_p Text__medium___1ocDap Text__heading_2___sUlNJP Heading__base___1dDlXY Heading__heading_2___3f_bIW ArticleHeader__heading___3ibi0Q"
for link in links:
paragraphs = ""
url = "https://www.reuters.com/"+str(link)[41:63]
scraped_data = urllib.request.urlopen(url)
scraped_data = scraped_data.read()
parsed_article = BeautifulSoup(scraped_data, 'lxml')
article = parsed_article.find_all("p")
title = parsed_article.select("h1", {"class": title_class})
titles.append(title[0].text.strip())
for paragraph in article:
paragraphs += paragraph.text + " "
articles.append(paragraphs)
return titles, articles
# edit
ticker = "apple"
news = scrape_reuters_news(ticker)
When you click the load more a callback is issued that you can find in the network tab. If you grab the number of results from the search page, you can add this into the callback to get all results in one go. I then use regex to extract the id to reconstruct each detail page url and the title (headline)
You would then visit each link to get the paragraph info.
Please note:
There is some de-duplication work to do. There exist different ids which lead to same content. So perhaps exclude based on title?
You may need to consider whether any pre-processing of ticker needs to happen e.g. convert to lowercase, replace spaces with "-". I don't know all your use cases.
from bs4 import BeautifulSoup as bs
import requests, re
ticker = 'apple'
with requests.Session() as s:
r = s.get(f'https://www.reuters.com/search/news?sortBy=relevance&dateRange=pastWeek&blob={ticker}')
soup = bs(r.content, 'lxml')
num_results = soup.select_one('.search-result-count-num').text
r = s.get(f'https://www.reuters.com/assets/searchArticleLoadMoreJson?blob={ticker}&bigOrSmall=big&articleWithBlog=true&sortBy=relevance&dateRange=pastWeek&numResultsToShow={num_results}&pn=&callback=addMoreNewsResults')
p = re.compile(r'id: "(.*?)"')
p2 = re.compile(r'headline: "(.*?)"')
links = [f'https://www.reuters.com/article/id{i}' for i in p.findall(r.text)]
headlines = [bs(i, 'lxml').get_text() for i in p2.findall(r.text)]
print(len(links), len(headlines))
From the detail pages you can get the paragraphs with
paras = ' '.join([i.get_text() for i in soup.select('[data-testid*=paragraph-]')])
I am trying to parse a txt, example as below link.
The txt, however, is in the form of html. I am trying to get "COMPANY CONFORMED NAME" which located at the top of the file, and my function should return "Monocle Acquisition Corp".
https://www.sec.gov/Archives/edgar/data/1754170/0001571049-19-000004.txt
I have tried below:
import requests
from bs4 import BeautifulSoup
url = 'https://www.sec.gov/Archives/edgar/data/1754170/0001571049-19-000004.txt'
r = requests.get(url)
soup = BeautifulSoup(r.content, "html")
However, "soup" does not contain "COMPANY CONFORMED NAME" at all.
Can someone point me in the right direction?
The data you are looking for is not in an HTML structure so Beautiful Soup is not the best tool. The correct and fast way of searching for this data is just using a simple Regular Expression like this:
import re
import requests
url = 'https://www.sec.gov/Archives/edgar/data/1754170/0001571049-19-000004.txt'
r = requests.get(url)
text_string = r.content.decode()
name_re = re.compile("COMPANY CONFORMED NAME:[\\t]*(.+)\n")
match = name_re.search(text_string).group(1)
print(match)
the part you look like is inside a huge tag <SEC-HEADER>
you can get the whole section by using soup.find('sec-header')
but you will need to parse the section manually, something like this works, but it's some dirty job :
(view it in replit : https://repl.it/#gui3/stackoverflow-parsing-html)
import requests
from bs4 import BeautifulSoup
url = 'https://www.sec.gov/Archives/edgar/data/1754170/0001571049-19-000004.txt'
r = requests.get(url)
soup = BeautifulSoup(r.content, "html")
header = soup.find('sec-header').text
company_name = None
for line in header.split('\n'):
split = line.split(':')
if len(split) > 1 :
key = split[0]
value = split[1]
if key.strip() == 'COMPANY CONFORMED NAME':
company_name = value.strip()
break
print(company_name)
There may be some library able to parse this data better than this code
I was building a web-scraper using python.
The purpose of my scraper is to fetch all the links to websites from this webpage http://www.ebizmba.com/articles/torrent-websites
I want output like -
www.thepiratebay.se
www.kat.ph
I am new to python and scraping, and I was doing this just for practice. Please help me to get the right output.
My code --------------------------------------
import requests
from bs4 import BeautifulSoup
r = requests.get("http://www.ebizmba.com/articles/torrent-websites")
soup = BeautifulSoup(r.content, "html.parser")
data = soup.find_all("div", {"class:", "main-container-2"})
for item in data:
print(item.contents[1].find_all("a"))
My Output --- http://i.stack.imgur.com/Xi37B.png
If you are webscraping for practice, have a look at regular expressions.
This here would get just the headline links... The Needle string is the match string, the brackets (http://.*?) contain the match group.
import urllib2
import re
myURL = "http://www.ebizmba.com/articles/torrent-websites"
req = urllib2.Request(myURL)
Needle1 = '<p><a href="(http:.*?)" rel="nofollow" target="_blank">'
for match in re.finditer(Needle1, urllib2.urlopen(req).read()):
print(match.group(1))
Use .get('href') like this:
import requests
from bs4 import BeautifulSoup
r = requests.get("http://www.ebizmba.com/articles/torrent-websites")
soup = BeautifulSoup(r.text, "html.parser")
data = soup.find_all("div", {"class:", "main-container-2"})
for i in data:
for j in i.contents[1].find_all("a"):
print(j.get('href'))
Full output:
http://www.thepiratebay.se
http://siteanalytics.compete.com/thepiratebay.se
http://quantcast.com/thepiratebay.se
http://www.alexa.com/siteinfo/thepiratebay.se/
http://www.kickass.to
http://siteanalytics.compete.com/kickass.to
http://quantcast.com/kickass.to
http://www.alexa.com/siteinfo/kickass.to/
http://www.torrentz.eu
http://siteanalytics.compete.com/torrentz.eu
http://quantcast.com/torrentz.eu
http://www.alexa.com/siteinfo/torrentz.eu/
http://www.extratorrent.cc
http://siteanalytics.compete.com/extratorrent.cc
http://quantcast.com/extratorrent.cc
http://www.alexa.com/siteinfo/extratorrent.cc/
http://www.yify-torrents.com
http://siteanalytics.compete.com/yify-torrents.com
http://quantcast.com/yify-torrents.com
http://www.alexa.com/siteinfo/yify-torrents.com
http://www.bitsnoop.com
http://siteanalytics.compete.com/bitsnoop.com
http://quantcast.com/bitsnoop.com
http://www.alexa.com/siteinfo/bitsnoop.com/
http://www.isohunt.to
http://siteanalytics.compete.com/isohunt.to
http://quantcast.com/isohunt.to
http://www.alexa.com/siteinfo/isohunt.to/
http://www.sumotorrent.sx
http://siteanalytics.compete.com/sumotorrent.sx
http://quantcast.com/sumotorrent.sx
http://www.alexa.com/siteinfo/sumotorrent.sx/
http://www.torrentdownloads.me
http://siteanalytics.compete.com/torrentdownloads.me
http://quantcast.com/torrentdownloads.me
http://www.alexa.com/siteinfo/torrentdownloads.me/
http://www.eztv.it
http://siteanalytics.compete.com/eztv.it
http://quantcast.com/eztv.it
http://www.alexa.com/siteinfo/eztv.it/
http://www.rarbg.com
http://siteanalytics.compete.com/rarbg.com
http://quantcast.com/rarbg.com
http://www.alexa.com/siteinfo/rarbg.com/
http://www.1337x.org
http://siteanalytics.compete.com/1337x.org
http://quantcast.com/1337x.org
http://www.alexa.com/siteinfo/1337x.org/
http://www.torrenthound.com
http://siteanalytics.compete.com/torrenthound.com
http://quantcast.com/torrenthound.com
http://www.alexa.com/siteinfo/torrenthound.com/
https://demonoid.org/
http://siteanalytics.compete.com/demonoid.pw
http://quantcast.com/demonoid.pw
http://www.alexa.com/siteinfo/demonoid.pw/
http://www.fenopy.se
http://siteanalytics.compete.com/fenopy.se
http://quantcast.com/fenopy.se
http://www.alexa.com/siteinfo/fenopy.se/