I am trying to get scrape some data from stockrow.com using BeautifulSoup.
However there seems to be some diffrences between inspect and view sourcecode (im using chrome, but i do not see that being a problem for Pyton).
This is resulting in some trouble as the sourcecode itself does not show any html-tags such as h1. They are however showing up when i use the inspect tool.
The part i am trying to scrape (among other things) - this is show using the inspect tool:
<h1>Teva Pharmaceutical Industries Ltd<small>(TEVA)</small></h1>
My current code, printing an empty list:
import bs4 as bs
import urllib.request
class Stock:
stockrow_url = "https://stockrow.com"
url_suffix = "/financials/{}/annual"
def __init__(self, ticker : str, stock_url=stockrow_url, url_suffix = url_suffix):
# Stock ticker
self.ticker = ticker.upper()
# URLs for financial statements related to the ticker
self.stock_url = stock_url + "/{}".format(self.ticker)
sauce = urllib.request.urlopen(self.stock_url).read()
soup = bs.BeautifulSoup(sauce, 'html.parser').h1
print(soup)
self.income_url = self.stock_url + url_suffix.format("income")
self.balance_sheet_url = self.stock_url + url_suffix.format("balance")
self.cash_flow_url = self.stock_url + url_suffix.format("cashflow")
teva = Stock("teva")
print(teva.get_income_statement())
The page is dynamically generated using jscript and cannot be handled by beautifulsoup. You can capture the information using either selenium and the like, or by looking for API calls.
In this case, you can get for TEVA, background information using
import json
import requests
hdr = {'User-Agent':'Mozilla/5.0'}
url = "https://stockrow.com/api/companies/TEVA.json?ticker=TEVA"
response = requests.get(url, headers=hdr)
info = json.loads(response.text)
info
Similarly, the income statement is hiding here:
url = 'https://stockrow.com/api/companies/TEVA/financials.json?ticker=TEVA&dimension=MRY§ion=Income+Statement'
Using the same code as above but with this other url, will get you your income statement, in json format.
And you can take it from there. Search around - there is a lot of information available on this topic. Good luck.
Related
how to get access to this API:
import requests
url = 'https://b2c-api-premiumlabel-production.azurewebsites.net/api/b2c/page/menu?id_loja=2691'
print(requests.get(url))
I'm trying to retrieve data from this site via API, I found the url above and I can see its data , however I can't seem to get it right because I'm running into code 403.
This is the website url:
https://www.nagumo.com.br/osasco-lj46-osasco-ayrosa-rua-avestruz/departamentos
I'm trying to retrieve items category, they are visible for me, but I'm unable to take them.
Later I'll use these categories to iterate over products API.
API Category
Obs: please be gentle it's my first post here =]
To get the data as you shown in your image the following headers and endpoint are needed:
import requests
headers = {
'sm-token': '{"IdLoja":2691,"IdRede":884}',
'User-Agent': 'Mozilla/5.0',
'Referer': 'https://www.nagumo.com.br/osasco-lj46-osasco-ayrosa-rua-avestruz/departamentos',
}
params = {
'id_loja': '2691',
}
r = requests.get('https://www.nagumo.com.br/api/b2c/page/menu', params=params, headers=headers)
r.json()
Not sure exactly what your issue is here.
Bu if you want to see the content of the response and not just the 200/400 reponses. You need to add '.content' to your print.
Eg.
#Create Session
s = requests.Session()
#Example Connection Variables, probably not required for your use case.
setCookieUrl = 'https://www...'
HeadersJson = {'Accept-Language':'en-us'}
bodyJson = {"__type":"xxx","applicationName":"xxx","userID":"User01","password":"password2021"}
#Get Request
p = s.get(otherUrl, json=otherBodyJson, headers=otherHeadersJson)
print(p) #Print response (200 etc)
#print(p.headers)
#print(p.content) #Print the content of the response.
#print(s.cookies)
I'm also new here haha, but besides this requests library, you'll also need another one like beautiful soup for what you're trying to do.
bs4 installation: https:https://www.crummy.com/software/BeautifulSoup/bs4/doc/#installing-beautiful-soup
Once you install it and import it, it's just continuing what you were doing to actively get your data.
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
this gets the entire HTML content of the page, and so, you can get your data from this page based on their css selectors like this:
site_data = soup.select('selector')
site_data is an array of things with that 'selector', so a simple for loop and an array to add your items in would suffice (as an example, getting links for each book on a bookstore site)
For example, if i was trying to get links from a site:
import requests
from bs4 import BeautifulSoup
sites = []
URL = 'https://b2c-api-premiumlabel-production.azurewebsites.net/api/b2c/page/menu?id_loja=2691'
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
links = soup.select("a") # list of all items with this selector
for link in links:
sites.append(link)
Also, a helpful tip is when you inspect the page (right click and at the bottom press 'inspect'), you can see the code for the page. Go to the HTML and find the data you want and right click it and select copy -> copy selector. This will make it really easy for you to get the data you want on that site.
helpful sites:
https://oxylabs.io/blog/python-web-scraping
https://realpython.com/beautiful-soup-web-scraper-python/
The original code is here : https://github.com/amitabhadey/Web-Scraping-Images-using-Python-via-BeautifulSoup-/blob/master/code.py
So i am trying to adapt a Python script to collect pictures from a website to get better at web scraping.
I tried to get images from "https://500px.com/editors"
The first error was
The code that caused this warning is on line 12 of the file/Bureau/scrapper.py. To get rid of this warning, pass the additional argument
'features="lxml"' to the BeautifulSoup constructor.
So I did :
soup = BeautifulSoup(plain_text, features="lxml")
I also adapted the class to reflect the tag in 500px.
But now the script stopped running and nothing happened.
In the end it looks like this :
import requests
from bs4 import BeautifulSoup
import urllib.request
import random
url = "https://500px.com/editors"
source_code = requests.get(url)
plain_text = source_code.text
soup = BeautifulSoup(plain_text, features="lxml")
for link in soup.find_all("a",{"class":"photo_link "}):
href = link.get('href')
print(href)
img_name = random.randrange(1,500)
full_name = str(img_name) + ".jpg"
urllib.request.urlretrieve(href, full_name)
print("loop break")
What did I do wrong?
Actually the website is loaded via JavaScript using XHR request to the following API
So you can reach it directly via API.
Note that you can increase parameter rpp=50 to any number as you want for getting more than 50 result.
import requests
r = requests.get("https://api.500px.com/v1/photos?rpp=50&feature=editors&image_size%5B%5D=1&image_size%5B%5D=2&image_size%5B%5D=32&image_size%5B%5D=31&image_size%5B%5D=33&image_size%5B%5D=34&image_size%5B%5D=35&image_size%5B%5D=36&image_size%5B%5D=2048&image_size%5B%5D=4&image_size%5B%5D=14&sort=&include_states=true&include_licensing=true&formats=jpeg%2Clytro&only=&exclude=&personalized_categories=&page=1&rpp=50").json()
for item in r['photos']:
print(item['url'])
also you can access the image url itself in order to write it directly!
import requests
r = requests.get("https://api.500px.com/v1/photos?rpp=50&feature=editors&image_size%5B%5D=1&image_size%5B%5D=2&image_size%5B%5D=32&image_size%5B%5D=31&image_size%5B%5D=33&image_size%5B%5D=34&image_size%5B%5D=35&image_size%5B%5D=36&image_size%5B%5D=2048&image_size%5B%5D=4&image_size%5B%5D=14&sort=&include_states=true&include_licensing=true&formats=jpeg%2Clytro&only=&exclude=&personalized_categories=&page=1&rpp=50").json()
for item in r['photos']:
print(item['image_url'][-1])
Note that image_url key hold different img size. so you can choose your preferred one and save it. here I've taken the big one.
Saving directly:
import requests
with requests.Session() as req:
r = req.get("https://api.500px.com/v1/photos?rpp=50&feature=editors&image_size%5B%5D=1&image_size%5B%5D=2&image_size%5B%5D=32&image_size%5B%5D=31&image_size%5B%5D=33&image_size%5B%5D=34&image_size%5B%5D=35&image_size%5B%5D=36&image_size%5B%5D=2048&image_size%5B%5D=4&image_size%5B%5D=14&sort=&include_states=true&include_licensing=true&formats=jpeg%2Clytro&only=&exclude=&personalized_categories=&page=1&rpp=50").json()
result = []
for item in r['photos']:
print(f"Downloading {item['name']}")
save = req.get(item['image_url'][-1])
name = save.headers.get("Content-Disposition")[9:]
with open(name, 'wb') as f:
f.write(save.content)
Looking at the page you're trying to scrape I noticed something. The data doesn't appear to load until a few moments after the page finishes loading. This tells me that they're using a JS framework to load the images after page load.
Your scraper will not work with this page due to the fact that it does not run JS on the pages it's pulling. Running your script and printing out what plain_text contains proves this:
<a class='photo_link {{#if hasDetailsTooltip}}px_tooltip{{/if}}' href='{{photoUrl}}'>
If you look at the href attribute on that tag you'll see it's actually a templating tag used by JS UI frameworks.
Your options now are to either see what APIs they're calling to get this data (check the inspector in your web browser for network calls, if you're lucky they may not require authentication) or to use a tool that runs JS on pages. One tool I've seen recommended for this is selenium, though I've never used it so I'm not fully aware of its capabilities; I imagine the tooling around this would drastically increase the complexity of what you're trying to do.
I am setting up code to check the reputation of any URL E.g. http://go.mobisla.com/ on Website "https://www.virustotal.com/gui/home/url"
First, the very basic thing I am doing is to extract all the Website contents using BeautifulSoup but seems the information I am looking for is in shadow-root(open) -- div.detections and span.individual-detection.
Example Copied Element from Webpage results:
No engines detected this URL
I am new to Python, wondering if you can share the best way to extract the information
Tried requests.get() function but it doesn't give the required information
import requests
import os,sys
from bs4 import BeautifulSoup
import pandas as pd
url_check = "deloplen.com:443"
url = "https://www.virustotal.com/gui/home/url"
req = requests.get(url + url_str)
html = req.text
soup = BeautifulSoup(html, 'html.parser')
print(soup.prettify())
Expect to see "2 engines detected this URL" along with Detection Example: Dr. Web Malicious
If you use their website, it'll only return a loading screen for VirusTotal, as this isn't the proper way.
What Shows Up:
Instead, what you're supposed to do is use their public API to make requests. However, you'll have to make an account to obtain a Public API Key.
You can use this code which is able to retrieve JSON info about the link. However, you'll have to fill in the API KEY with yours.
import requests, json
user_api_key = "<api key>"
resource = "deloplen.com:443"
# feel free to remove this, just makes it look nicer
def pp_json(json_thing, sort=True, indents=4):
if type(json_thing) is str:
print(json.dumps(json.loads(json_thing), sort_keys=sort, indent=indents))
else:
print(json.dumps(json_thing, sort_keys=sort, indent=indents))
return None
response = requests.get("https://www.virustotal.com/vtapi/v2/url/report?apikey=" + user_api_key + "&resource=" + resource)
json_response = response.json()
pretty_json = pp_json(json_response)
print(pretty_json)
If you want to learn more about the API, you can use their documentation.
I am quite new to Python and am building a web scraper, which will scrape the following page and links in them: https://www.nalpcanada.com/Page.cfm?PageID=33
The problem is the page's default is to display the first 10 search results, however, I want to scrape all 150 search results (when 'All' is selected, there are 150 links).
I have tried messing around with the URL, but the URL remains static no matter what display results option is selected. I have also tried to look at the Network section of the Developer Tools on Chrome, but can't seem to figure out what to use to display all results.
Here is my code so far:
import bs4
import requests
import csv
import re
response = requests.get('https://www.nalpcanada.com/Page.cfm?PageID=33')
soup = bs4.BeautifulSoup(response.content, "html.parser")
urls = []
for a in soup.findAll('a', href=True, class_="employerProfileLink", text="Vancouver, British Columbia"):
urls.append(a['href'])
pagesToCrawl = ['https://www.nalpcanada.com/' + url + '&QuestionTabID=47' for url in urls]
for pages in pagesToCrawl:
html = requests.get(pages)
soupObjs = bs4.BeautifulSoup(html.content, "html.parser")
nameOfFirm = soupObjs.find('div', class_="ip-left").find('h2').next_element
tbody = soupObjs.find('div', {"id":"collapse8"}).find('tbody')
offers = tbody.find('td').next_sibling.next_sibling.next_element
seeking = tbody.find('tr').next_sibling.next_sibling.find('td').next_sibling.next_sibling.next_element
print('Firm name:', nameOfFirm)
print('Offers:', offers)
print('Seeking:', seeking)
print('Hireback Rate:', int(offers) / int(seeking))
Replacing your response call with this code seems to work. The reason is that you weren't passing in the cookie properly.
response = requests.get(
'https://www.nalpcanada.com/Page.cfm',
params={'PageID': 33},
cookies={'DISPLAYNUM': '100000000'}
)
The only other issue I came across was that a ValueError was being raised by this line when certain links (like YLaw Group) don't seem to have "offers" and/or "seeking".
print('Hireback Rate:', int(offers) / int(seeking))
I just commented out the line since you will have to decide what to do in those cases.
For last few days I am trying to scrap the following website (link pasted below) which has a few excels and pdfs available in a table. I am able to do it for the home page successfully. There are total 59 pages from which these excels/ pdfs have to be scrapped. In most of the websites I have seen till now there is a query parameter which is available in the site url which changes as you move from one page to another. In this case, we have a _doPostBack function probably because of which the URL remains the same on every page you go to. I looked at multiple solutions and posts which are suggesting to see the parameters of post call and use them but I am not able to make sense of the parameters which are provided in post call (this is the first time I am scrapping a website).
Can someone please suggest some resource which can help me write a code which helps me in moving from one page to another using python. The details are as follows:
Website link - http://accord.fairfactories.org/ffcweb/Web/ManageSuppliers/InspectionReportsEnglish.aspx
My current code which extracts the CAP excel sheet from the home page (this is working perfect and is provided just for reference)
from urllib.request import urlopen
from urllib.request import urlretrieve
from bs4 import BeautifulSoup
import re
import urllib
Base = "http://accord.fairfactories.org/ffcweb/Web"
html = urlopen("http://accord.fairfactories.org/ffcweb/Web/ManageSuppliers/InspectionReportsEnglish.aspx")
bs = BeautifulSoup(html)
name = bs.findAll("td", {"class":"column_style_right column_style_left"})
i = 1
for link in bs.findAll("a", {"id":re.compile("CAP(?!\w)")}):
if 'href' in link.attrs:
name = str(i)+".xlsx"
a = link.attrs['href']
b = a.strip("..")
c = Base+b
urlretrieve(c, name)
i = i+1
Please let me know if I have missed anything while providing the information and please don't rate me -ve else I won't be able to ask any questions further
For aspx sites, you need to look for things like __EVENTTARGET, __EVENTVALIDATION etc.. and post those parameters with each request, this will get all the pages and using requests with bs4:
import requests
from bs4 import BeautifulSoup
from urlparse import urljoin # python 3 use from urllib.parse import urljoin
# All the keys need values set bar __EVENTTARGET, that stays the same.
data = {
"__EVENTTARGET": "gvFlex",
"__VIEWSTATE": "",
"__VIEWSTATEGENERATOR": "",
"__VIEWSTATEENCRYPTED": "",
"__EVENTVALIDATION": ""}
def validate(soup, data):
for k in data:
# update post values in data.
if k != "__EVENTTARGET":
data[k] = soup.select_one("#{}".format(k))["value"]
def get_all_excel():
base = "http://accord.fairfactories.org/ffcweb/Web"
url = "http://accord.fairfactories.org/ffcweb/Web/ManageSuppliers/InspectionReportsEnglish.aspx"
with requests.Session() as s:
# Add a user agent for each subsequent request.
s.headers.update({"User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:47.0) Gecko/20100101 Firefox/47.0"})
r = s.get(url)
bs = BeautifulSoup(r.content, "lxml")
# get links from initial page.
for xcl in bs.select("a[id*=CAP]"):
yield urljoin(base, xcl["href"])
# need to re-validate the post data in our dict for each request.
validate(bs, data)
last = bs.select_one("a[href*=Page$Last]")
i = 2
# keep going until the last page button is not visible
while last:
# Increase the counter to set the target to the next page
data["__EVENTARGUMENT"] = "Page${}".format(i)
r = s.post(url, data=data)
bs = BeautifulSoup(r.content, "lxml")
for xcl in bs.select("a[id*=CAP]"):
yield urljoin(base, xcl["href"])
last = bs.select_one("a[href*=Page$Last]")
# again re-validate for next request
validate(bs, data)
i += 1
for x in (get_all_excel()):
print(x)
If we run it on the first three pages, you can see we get the data you want:
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9965
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9552
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10650
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11969
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10086
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10905
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10840
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9229
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11310
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9178
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9614
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9734
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10063
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10871
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9468
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9799
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9278
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=12252
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9342
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9966
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11595
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9652
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10271
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10365
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10087
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9967
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11740
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=12375
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11643
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10952
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=12013
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9810
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10953
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10038
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9664
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=12256
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9262
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9210
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9968
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9811
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11610
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9455
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11899
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10273
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9766
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9969
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10088
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10366
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9393
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9813
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11795
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9814
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11273
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=12187
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10954
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9556
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11709
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9676
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10251
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10602
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10089
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9908
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10358
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9469
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11333
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9238
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9816
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9817
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10736
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10622
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9394
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9818
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=10592
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=9395
http://accord.fairfactories.org/Utilities/DownloadFile.aspx?id=11271