AI News, Speeding up the Housing Search in San Francisco with Data Science
- On Monday, September 17, 2018
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Speeding up the Housing Search in San Francisco with Data Science
It was one year ago before I found my current sofa-in-a-converted-living-room that I started my search for housing, and it quickly became an overwhelming experience trying to find any kind of rent for less than an arm and a leg.
thought to myself, “there must have been some way to expedite the housing search!” As an aspiring data scientist, my solution was to create a web scraper that could collect the information of several regions of San Francisco and present them in a concise way without all the fluff.
So breaking down the big goal, my plan of attack is to: Step 1: Bring in the webscraping tools Step 2: Open up a webpage with Selenium “Selenium is a tool that lets us automate actions within a browser as if we were clicking or typing as a human.
The main reason is because of the developer tools which can be opened with these three keys: Option + Command + U By clicking those three keys simultaneously, you can view the HTML code behind any webpage.
Step 4: Scrape the link to the individual post Here comes the tricky part: how can I get the link to the individual seller’s page if I can’t click on the link and get it’s hyperlink without redirecting the Selenium browser?
Step 7: Bring it all together We have the pieces to (1) scrape a page’s titles and links, (2) move to another page and repeat step 1, and (3) append information all to a list.
So in conclusion, we learned that while scraping appears difficult and time consuming, it can be broken down into easier parts, visualized between the actual user interface and the HTML source behind it, and finally generalized to mimic the entire human behavior.
Mastering Python Web Scraping: Get Your Data Back
Do you ever find yourself in a situation where you need to get information out of a website that conveniently doesn’t have an export option?
This happened to a client of mine who desperately needed lists of email addresses from a platform that did not allow you to export your own data and hid the data behind a series of UI hurdles.
all of its dependencies. pip install pandas If you don’t have Splinter (and are not using Anaconda’s Python), simply download it with pip from the terminal/command line. pip install splinter If you don’t have Splinter (and are using Anaconda’s Python), download it with Anaconda’s package manager from the terminal/command line. conda install splinter If you want to set this up in a virtual environment (which has many advantages) but don’t know where to start, try reading our other blog post about virtual environments.
How To Crawl A Web Page with Scrapy and Python 3
Web scraping, often called web crawling or web spidering, or “programatically going over a collection of web pages and extracting data,” is a powerful tool for working with data on the web.
With a web scraper, you can mine data about a set of products, get a large corpus of text or quantitative data to play around with, get data from a site without an official API, or just satisfy your own personal curiosity.
By the end of this tutorial, you’ll have a fully functional Python web scraper that walks through a series of pages on Brickset and extracts data about LEGO sets from each page, displaying the data to your screen.
You can build a scraper from scratch using modules or libraries provided by your programming language, but then you have to deal with some potential headaches as your scraper grows more complex.
If you have a Python installation like the one outlined in the prerequisite for this tutorial, you already have pip installed on your machine, so you can install Scrapy with the following command: If you run into any issues with the installation, or you want to install Scrapy without using pip, check out the official installation docs.
This class will have two required attributes: Open the scrapy.py file in your text editor and add this code to create the basic spider: Let's break this down line by line: First, we import scrapy so that we can use the classes that the package provides.
If you look at the page we want to scrape, you'll see it has the following structure: When writing a scraper, it's a good idea to look at the source of the HTML file and familiarize yourself with the structure.
Another look at the [source](view-source:brickset.com/sets/year-2016) of the page we're parsing tells us that the name of each set is stored within an a tag inside an h1 tag for each set: The brickset object we’re looping over has its own css method, so we can pass in a selector to locate child elements.
You’ll notice two things going on in this code: Save the file and run the scraper again: This time you'll see the names of the sets appear in the output: Let's keep expanding on this by adding new selectors for images, pieces, and miniature figures, or minifigs that come with a set.
Take another look at the HTML for a specific set: We can see a few things by examining this code: So, let's modify the scraper to get this new information: Save your changes and run the scraper again: Now you’ll see that new data in the program's output: Now let's turn this scraper into a spider that follows links.
The scrapy.Request is a value that we return saying “Hey, crawl this page”, and callback=self.parse says “once you’ve gotten the HTML from this page, pass it back to this method so we can parse it, extract the data, and find the next page.'
Here’s our completed code for this tutorial, using Python-specific highlighting: In this tutorial you built a fully-functional spider that extracts data from web pages in less than thirty lines of code.
Link Building Case Study: How I Increased My Search Traffic by 110% in 14 Days
Here’s the brutal truth about link building: There are WAY too many people in internet marketing today that think “great content”
After executing “The Skyscraper Technique“, the number of backlinks to that page shot up like a rocket: More importantly, organic search traffic to my entire site —
doubled in just 14 days: As a nice bonus, that single post has driven more than 300,000 referral visitors to my site so far.
And I go over all of them in this short-and-sweet video: Like I mentioned in the video above, here are the 3-steps that make up The Skyscraper Technique: Step 1: Find link-worthy content Step 2: Make something even better Step 3: Reach out to the right people Here’s why this technique works so well (and what it has to do with a skyscraper): Have you ever walked by a really tall building and said to yourself: “Wow, that’s amazing!
Here’s how you can take existing content to the next level: Make It Longer In some cases, publishing an article that’s simply longer or includes more items will do the trick.
It took 10 gallons of coffee and 20 hours of sitting in front of my laptop (don’t worry, I took bathroom breaks)…
For example, most of the other ranking factor lists were sorely outdated and lacked important ranking factors, like social signals: If you find something with old information, create something that covers many of the same points…but update it with cutting-edge content.
For my guide, I added a nice banner at the top: More Thorough Most lists posts are just a bland list of bullet points without any meaty content that people can actually use.
In my case I noticed that the other ranking factor lists lacked references and detail: So I made sure each and every point on my list had a brief description (with a reference): Important Note: I recommend that you beat the existing content on every level: length, design, current information etc.
Weed out referring pages that don’t make sense to contact (forums, article directories etc.). In my case, after cleaning up the list, I had 160 very solid prospects to reach out to.
But with this strategy you already know ahead of time that your hard work is going to pay off (unlike pumping out reams of content hoping that something goes viral).
Make a simple GET request (just fetching a page) Make a POST requests (usually used when sending information to the server like submitting a form) Pass query arguments aka URL parameters (usually used when making a search query or paging through results) See what response code the server sent back (useful for detecting 4XX or 5XX errors) Access the full response as text (get the HTML of the page in a big string) Look for a specific substring of text within the response Check the response’s Content Type (see if you got back HTML, JSON, XML, etc) Now that you’ve made your HTTP request and gotten some HTML content, it’s time to parse it so that you can extract the values you’re looking for.
To get started, you’ll have to turn the HTML text that you got in the response into a nested, DOM-like structure that you can traverse and search Look for all anchor tags on the page (useful if you’re building a crawler and need to find the next pages to visit) Look for all tags with a specific class attribute (eg <li class='search-result'>...</li>) Look for the tag with a specific ID attribute (eg: <div id='bar'>...</div>) Look for nested patterns of tags (useful for finding generic elements, but only within a specific section of the page) Look for all tags matching CSS selectors (similar query to the last one, but might be easier to write for someone who knows CSS) Get a list of strings representing the inner contents of a tag (this includes both the text nodes as well as the text representation of any other nested HTML tags within) Return only the text contents within this tag, but ignore the text representation of other HTML tags (useful for stripping our pesky <span>, <strong>, <i>, or other inline tags that might show up sometimes) Convert the text that are extracting from unicode to ascii if you’re having issues printing it to the console or writing it to files Get the attribute of a tag (useful for grabbing the src attribute of an <img>
tag) Putting several of these concepts together, here’s a common idiom: iterating over a bunch of container tags and pull out content from each of them BeautifulSoup doesn’t currently support XPath selectors, and I’ve found them to be really terse and more of a pain than they’re worth.
It usually means that you won’t be making an HTTP request to the page’s URL that you see at the top of your browser window, but instead you’ll need to find the URL of the AJAX request that’s going on in the background to fetch the data from the server and load it into the page.
There’s not really an easy code snippet I can show here, but if you open the Chrome or Firefox Developer Tools, you can load the page, go to the “Network” tab and then look through the all of the requests that are being sent in the background to find the one that’s returning the data you’re looking for.
If you want to be polite and not overwhelm the target site you’re scraping, you can introduce an intentional delay or lag in your scraper to slow it down Some also recommend adding a backoff that’s proportional to how long the site took to respond to your request.
- On Saturday, October 19, 2019
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