What is web scraping?
Web Scraping (also termed Screen Scraping, Web Data Extraction, Web Harvesting etc.) is a technique employed to extract large amounts of data from websites whereby the data is extracted and saved to a local file in your computer or to a database in table (spreadsheet) format.
If you want to make money with the knowledge of web scraping, you create a bot that successfully gets the valuable data you wished for, then sell the data or bot, or use it to buy or sell or make money on betting via sure bet.
There are some ways to make money using web scraping without selling data: Sport Arbitrage, Stock market, eCommerce, Niche News Aggregation (pick a niche, like celebrity news sites, scrape the top 10 sites, etc), Daily News (pay for a subscription to get past major site paywalls, then make the data free or discounted),Offline, intranet, or hard-to-access data, Lead Generation, Machine learning (Google images), Price monitoring (Ebay), Lead generation (Yelp) [scraping contact info for local biz], Market research (Brewdog) [scraping types of beer and their ratings, for example), App Development (Find Real Estate, Homes for Sale, Apartments & Houses for Rent | realtor.com®) [I can only assume scraping realty data and copying it], Academic Research (Techcrunch), Find Relevant Top Hashtags, etc…
Scraping data from betting sites is a good way to make money because you don’t have to sell data you obtained, but only use that data in your favor. If you never scraped a betting site, I recommend you first check my step by step tutorial Scraping a Betting Site in 10 Minutes where I show the basics of scraping a bookmaker.
It doesn’t matter what sports you like; chances are you or someone you know at least once earned some money betting on their favorite team. You might’ve won because of good luck or knowledge of the sport, but probably you’ve also lost because you can’t always guess what’s going to happen in the future. But what if you could make a profit regardless of the match outcome? This is called ‘surebet’ and isn’t new in the gambling world.
Surebet is a situation when a bettor can make a profit regardless of the outcome by placing one bet per each outcome with different bookmakers. This happens when different bookmakers have different odds for the same game due to either bookmakers’ differing opinions (statistics) on event outcomes or errors. We can find those errors by scraping different bookmakers.
If you decided to make money with surebets, keep this in mind:
Avoid ‘account limitation’: Bookmakers, in general, dislike people who are good at gambling (no matter how they win); that’s why some people who earn money in betting sites get limitations. This means that you’d only bet a maximum amount of money per event set by the bookmaker — $5, $10, etc. If you start getting money with surebets, you may be seen as a ‘good bettor.’ To appear like an average person under bookmakers’ radars, experience bettors do this:Use many bookmakers: Create accounts in different bookmakers and spread your bets around them. It’ll be harder to identify you as a smart player in this way.
Round your stake: Although in the example I gave, I used decimal numbers; you shouldn’t do this just because most people don’t bet like that. Avoid decimal numbers at any cost and do your best to round your stake to the nearest number of five. If the formula gives you $47, then bet either $45 or $50 instead.
Do not make unnecessary withdrawals from a bookmaker: After you get some money don’t try to cash out right away or withdraw big amounts at once, this may arouse suspicion.
Avoid betting on smaller markets: Not many people bet on less popular sports like table tennis or water polo, so making money here would be suspicious. Mix up small and large markets.
Remember that limited accounts can still withdraw money. Hopefully, with the tips above, you’ll avoid limitations for a good time.
Finally, these are some markets where surebets happen often:Hand to Hand (Win or lose sports like tennis, baseball, etc.)
Both teams to score
Over / Under
Lets say you want to find the price of an item on an eCommerce website. Normally, you will visit the website, search for the item and then scroll until you find the item.
But now let’s say you want to do this for thousands of items, perhaps across multiple websites. Maybe you are starting your own business and you want to keep track of the going prices for a variety of items. Manually checking prices on all of them is going to be very time consuming. To help you do this work faster, you can write a web scraper.
So how does this work?
When you visit a website with your browser, a server sends you some files, and the browser then renders them into pages that look nice and are easy for a human to use (hopefully). But you don’t need a browser to ask for those files. You can also write a computer program that requests those files. A web scraper (usually) will not render those files into pretty, usable pages, but instead load them into a format that makes them easy for a machine to read extremely quickly.
At that point, you can scan all of the files for all of the prices, and do whatever you like with them. You could average them and output a number. Or output the minimum and maximum prices. Or output the prices of the highest rated listings for whatever product you are curious about. Or feed the numbers to a graphing library that visualizes the data. Or put them into an Excel sheet. The possibilities are endless!
Some websites are hostile to this practice, however, and make you jump through hoops to prove that you are a real user and not a computer program. This makes sense, because too many webscrapers crawling all over your website can slow your site down or crash it. It’s also a way for competitors to get real time data about you, and you may want to make it more difficult for them to do so.
Stock markets tend to react very quickly to a variety of factors such as news, earnings reports, etc. While it may be prudent to develop trading strategies based on fundamental data, the rapid changes in the stock market are incredibly hard to predict and may not conform to the goals of more short term traders. This study aims to use data science as a means to both identify high potential stocks, as well as attempt to forecast future prices/price movement in an attempt to maximize an investor’s chances of success. Read more…
Lead Generation is crucial for any business, without new leads to fill your sales funnel it’s impossible to acquire your customers and grow your company. Some businesses garner a lot of inbound interest so PPC or social media ads may be enough to generate leads. But what if your product or service is something that most people don’t specifically search for? This might be a new technology, a niche product or B2B services where very few people might use a search engine to find you. Read more ….
The good thing about this code is that you do not need to log into any Instagram account. Anyone can access publicly available posts on Instagram using the hashtag. For example if you want to see the posts for the hashtag #newyork, you can do so by using the following URL:
- Don’t Hard Code Session Cookies:
So what should you do instead? Code your program to login and use the sessions to ensure your cookies get sent with every request!
s = requests.Session()
s.post("https://fakewebsite.com/login", login_data)for url in url_list:
response = s.get(url)
It takes just a little extra work but it will save you time from having to constantly update the code.
- Don’t DOS Websites: Not that type of DOS. I mean Denial Of Service. If you don’t think you are doing this you should read this section because I’m about to blow your mind. Writing a for loop to access a website is a DOS.
- Don’t Copy and Paste Reusable Code
- Don’t Write Single Threaded Scrapers: Note that more threads doesn’t always mean better performance. This is because all these threads live on the same core. Confusing I know but this is something you will likely come across in testing.
- Don’t Use the Same Pattern for Scraping: Many websites will ban you if you do the same thing over and over again. There are some strategies you can use to circumvent this.
Web scraping doesn’t have to be hard. The best thing you can do for yourself is build good tools that you can reuse and your web scraping life will be much easier. If you need assistance with a web scraping project feel free to reach out to me on twitter as I do consulting.
Wordometers is a website that provides data on live world statistics, and is the website we are going to scrape. Specifically we are going to scrape world population data that is in a table (seen below). Scraping data from a table is one of the most used forms of web scraping because most often then not the data we need in tables are not downloadable. So instead of getting the data manually we let a computer do it in mere seconds.
Beautiful Soup is one of the most powerful web scraping libraries and in my opinion the easiest to learn which is why were going to use it.
You can first extract images URLs (where the image is stored on the website) using Octoparse (a coding-free visual web scraping tool), and then download the images using image downloaders.
Online OCR Software
There are a few convenient and useful OCR tools in the Text Scanner such as below:
1. Images OCR
2. Screenshot OCR
3. Table OCR
4. Scanner/Digital Camera
All the OCR tools above can provide a different type of OCR conversions to help users from different file formats on different devices.
1. Extract Text from PDF.
2. Extract Text from Image.
3. Extract Text from Screenshot.
4. Extract Excel from Image.
5. Scan Text from Camera or Scanner.
What etiquette should web scrapers follow? – Web scraping code of conduct:
Scraping for your own personal use: no-one cares. Just make sure to throttle the process so you don’t hammer a website to the point it becomes a DDoS attack.
Scraping is legal. https://techcrunch.com/2022/04/18/web-scraping-legal-court/
I’m not sure if there is any real law against scraping, but there are licensing issues regarding data published. If someone is paying for a data provider, and you scrape that data, that may not be legal for you to collect and redistribute.
Web Scraping with Python: from Fundamentals to Practice
How do deal with https-domains with SSL certificates in BeautifulSoup? And please don’t say use verify = False:
BeautifulSoup is a library for pulling data out of HTML and XML. You have to make a request using another library(e.g. requests) to get HTML content of the page and pass it to BeautifulSoup for extracting useful information.
I haven’t faced with any problems during scraping HTTPs sites using requests lib.
For anyone who goes with
requests as your HTTP client, I would highly recommend adding requests-cache for a nice performance boost.
Why does Python not separate data into columns when exporting web scraping results to .csv?
Make sure to set the separator to
, (I think the default is
Also, you should use
BeautifulSoup(page.text) instead of
BeautifulSoup(page.content). If you give it bytes rather than text, BeautifulSoup has to guess the text encoding, which is slow and can produce incorrect results.
And at the end, remember to call
soup.decompose() to let python free up the memory.
How do I turn web scraping into a business?
Start by identifying the problem your service can solve. Eg, e-commerce companies wanting real time data on retail trends in their space, or financial firms wanting data on hiring trends gleaned from jobs postings, etc. If you can show how your tool addresses that problem better or cheaper than the current solution, and thus creates value and $ for your audience, you’ve got a business.
Is it possible to do web scraping without using any third-party modules?
Uh, of course you can. Here I wrote this just for you. I tried to make it slightly realistic so I gave it some error handling, a stopping point, absolute URL handling, and multithreading.
I think the first barrier you’ll run into with this is Python’s native HTML parser is very strict about what valid HTML is so it won’t interpret things the same way your web browser will. For that, I suggest using lxml as a parser (but that is a third-party module).
from collections import deque from html.parser import HTMLParser from threading import Lock from urllib.error import HTTPError from urllib.parse import urljoin from urllib.request import urlopen from concurrent.futures import ThreadPoolExecutor NUMBER_OF_THREADS = 10 MAX_DEPTH = 3 TARGET_URL = r"https://www.reddit.com/r/Python/comments/v89fm9/is_it_possible_to_do_web_scraping_without_using/" class MyHTMLParser(HTMLParser): def __init__(self, url=None): super().__init__() self.links =  self.url = url def handle_starttag(self, tag, attrs): if tag == "a": if "href" not in dict(attrs): return href = dict(attrs)["href"] # Convert relative links to absolute links if self.url: href = urljoin(self.url, href) self.links.append(href) def get_html(url): """ Get the content of a URL. """ try: return urlopen(url).read().decode("utf-8") except HTTPError as e: return e.read().decode("utf-8") def parse_html(html, url=None): """ Parse the HTML of a web page. """ parser = MyHTMLParser(url) parser.feed(html) return parser def handle(url, depth, callback, lock): """ Handle a web page. """ html = get_html(url) links = parse_html(html, url).links # Lock when printing to the terminal to avoid two threads printing at the same time with lock: print(depth, url) for link in links: # Lock when adding to the queue to avoid two threads adding to the queue at the same time with lock: callback((depth + 1, link)) def crawl(url, max_depth): """ Crawl a web page. """ seen = set() crawling = deque([(0, url)]) lock = Lock() with ThreadPoolExecutor(max_workers=NUMBER_OF_THREADS) as executor: tasks =  while crawling: depth, url = crawling.popleft() # If the depth is equal to the maximum depth, skip the URL (remember depth starts at 0) if depth == max_depth: continue # If the URL has already been seen, skip it if url in seen: continue seen.add(url) # Submit the task and add the task to the list of tasks tasks.append(executor.submit(handle, url, depth, crawling.append, lock)) # If the queue is empty and we still have tasks, wait for them one by one until we have something to do while tasks and not crawling: tasks.pop().result() if __name__ == "__main__": crawl(TARGET_URL, max_depth=MAX_DEPTH)
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