databigbang.comData Big Bang Blog | Creativity and Problem Solving for Data Science (whatever it may mean…) | An ex

perimental spin-off from Nektra Advanced Computing Data Big Bang Blog Creativity and Problem Solving for Data Science (whatever it may mean…) | An experimental spin-off from Nektra Advanced Computing Menu Home Big Data and Data Science Blogs Ordered by Google PageRank The Call of the Web Scraper Astrid, our Data Big Bang and Nektra content editor, is heading to Nepal on a birding and trekking quest. She needs birds sounds from xeno-canto and The Internet Bird Collection to identify the hundreds of species found in Nepal, but the site does not offer batch downloads. We could not pass up the opportunity to offer a useful scraper for birders. We found a blog post with code to download batches of recordings for specific species (not specific countries): Web Scraping with BeautifulSoup and Python . Like most script developers. we want to do things our own way. Our code allows simultaneous download of calls to speed up the process for specially diverse countries. Web scraping is often associated with indecorous Internet behavior, but in fact, it is also a way to automate tedious manual work. Imagine that you want to have the complete schedule from EasyJet to choose a flight. It can take less than one hour to scrape all the desired routes. Right now there are no entry-level tools for scraping sites like there are for photo editing. Fortunately, script developers share their scraping code on sites like ScraperWiki. If you liked this article, you might also like: Scraping Web Sites which Dynamically Load Data Precise Scraping with Google Chrome Web Scraping 101: Pulling Stories from Hacker News November 18, 2013 Sebastian Wain birders , birding , calls , ibc , nepal , xeno-canto 3 Comments Web Scraping 101: Pulling Stories from Hacker News This is a guest post by Hartley Brody, whose bookThe Ultimate Guide to Web Scraping ” goes into much more detail on web scraping best practices. You can follow him on Twitter, it’ll make his day! Thanks for contributing Hartley! Hacker News is a treasure trove of information on the hacker zeitgeist. There are all sorts of cool things you could do with the information once you pull it, but first you need to scrape a copy for yourself. Hacker News is actually a bit tricky to scrape since the site’s markup isn’t all that semantic — meaning the HTML elements and attributes don’t do a great job of explaining the content they contain. Everything on the HN homepage is in two tables, and there aren’t that many class es or id s to help us hone in on the particular HTML elements that hold stories. Instead, we’ll have to rely more on patterns and counting on elements as we go. Pull up the web inspector in Chrome and try zooming up and down the DOM tree. You’ll see that the markup is pretty basic. There’s an outer table that’s basically just used to keep things centered (85% of the screen width) and then an inner table that holds the stories. If you look inside the inner table, you’ll see that the rows come in groups of three: the first row in each group contains the headlines and story links, the second row contains the metadata about each story — like who posted it and how many points it has — and the third row is empty and adds a bit of padding between stories. This should be enough information for us to get started, so let’s dive into the code. I’m going to try and avoid the religious tech wars and just say that I’m using Python and my trusty standby libraries — requests and BeautifulSoup — although there are many other great options out there. Feel free to use your HTTP requests library and HTML parsing library of choice. In its purest form, web scraping is two simple steps: 1. Make a request to a website that generates HTML, and 2. Pull the content you want out of the HTML that’s returned. As the programmer, all you need to do is a bit of pattern recognition to find the URLs to request and the DOM elements to parse, and then you can let your libraries do the heavy lifting. Our code will just glue the two functions together to pull out just what we need. import requests from BeautifulSoup import BeautifulSoup # make a single request to the homepage r = requests.get("https://news.ycombinator.com/") # convert the plaintext HTML markup into a DOM-like structure that we can search soup = BeautifulSoup(r.text) # parse through the outer and inner tables, then find the rows outer_table = soup.find("table") inner_table = outer_table.findAll("table")[1] rows = inner_table.findAll("tr") stories = [] # create an empty list for holding stories rows_per_story = 3 # helps us iterate over the table for row_num in range(0, len(rows)-rows_per_story, rows_per_story): # grab the 1st & 2nd rows and create an array of their cells story_pieces = rows[row_num].findAll("td") meta_pieces = rows[row_num + 1].findAll("td") # create our story dictionary story = { "current_position": story_pieces[0].string, "link": story_pieces[2].find("a")["href"], "title": story_pieces[2].find("a").string, } try: story["posted_by"] = meta_pieces[1].findAll("a")[0].string except IndexError: continue # this is a job posting, not a story stories.append(story) import json print json.dumps(stories, indent=1) You’ll notice that inside the for loop, when we’re iterating over the rows in the table two at a time, we’re parsing out the individual pieces of content (link, title, etc) by← Older posts Read our Windows Driver Development and Custom Windows Software Development services. 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