Craigslist Scraping

Craigslist Scraping

February 21, 2022
0

How to Scrape Data from Craigslist | Octoparse

Table of Contents
1. Why do people scrape Craigslist
2. Is scraping Craigslist illegal
3. How to scrape data from Craigslist
4. Craigslist data scraping with Octoparse
5. Closing thoughts
Why do people scrape Craigslist?
Craigslist gathers expansive information. Some may not be satisfied just browsing it, they scrape data from Craigslist for a variety of reasons. Below are the typical 4 of them.
1> Individuals can extract first-hand information regarding houses, cars, computers and many more. When exported into excel sheets, it is much easier for them to look through and compare the data.
2> Craigslist, similar to Yellowpages and Yelp, is full of potential business leads for revenue generation. No doubt that leads are important, especially qualified ones. This is probably the reason why Craigslist appeals to so many people.
3> Gain profits by reselling goods. With scraped data in a well structure, people can better analyze prices and set a new one for reselling. However, reselling is rather in the gray area, thus this might not be a good try. It’s profitable sometimes, but the consequences may not be delightful.
4> Monitor competitors. Craigslist is full of precious information covering an array of industries where people can keep track of their competitors. Being informed of their strategies in real-time will help businesses gain an edge in competition.
Is scraping Craigslist illegal?
As one of the most popular websites out there to scrape, Craigslist has proved to be one of the toughest ones. The reason is simple: unlike websites that provide users with APIs to get data, Craigslist API is not aimed at pulling data off. Quite on the contrary, it is used for posting data on Craigslist.
Just like Facebook and LinkedIn, Craigslist’s terms clearly state that all sorts of robots, spiders, scripts, scrapers, crawlers are prohibited. And they won’t allow people to steal their users’ personal information on the site.
Craigslist has used various technological and legal methods to prevent being scraped for commercial purposes. In fact, in April 2017, Craigslist obtained a $60. 5 million judgment against 3 Taps Inc, a company that is accused of scraping real estate listings. A few months later, Craigslist reached another $31 million judgment with Instamotor, claiming that Instamotor’s car listing service was scraped from Craigslist, and they sent unsolicited emails to craigslist users for promotional purposes.
Nevertheless, as said in an article entitled 10 Myths about Web Scraping, it is illegal if you scrape confidential information for profit, but if you scrape public data discreetly for personal use, you should be fine.
How to scrape data from Craigslist?
If you are a coder, you can follow this Python tutorial on scraping East Bay Area Craigslist for apartments. The code in this tutorial can be modified to pull from any region, category, property type, etc. Or you can check out this Scrapy tutorial to learn to crawl Craigslist’s “Architecture & Engineering” jobs in New York and store the data to a CSV file.
But the problem with the above tutorials are obvious: they are way too complicated for non-coders. If you have zero coding experience and want a simple and quick method, here’s a catch – use an automated data scraping tool like Octoparse.
With the power of data scraping, we can extract all the info we want from Craigslist listings within clicks and export them into Excel, CSV, HTML, and/or databases easily. I will walk you through how to extract Craigslist real estate listings within 3 steps.
Real estate listing extracted from Craigslist
Craigslist data scraping with Octoparse
In this case, let’s scrape the housing/real estate for sale in Chicago. First thing first, install Octoparse and launch it on your computer.
Step 1: Enter the target Craigslist URL to build a crawler
Enter the listing URL into the box, and Octoparse will start detecting the page data automatically. As you can see, the data to be extracted is highlighted in red, and the preview section below allows you to pre-edit the data fields.
Step 2: Save the extraction setting
After making sure that the data fields are what we want, click “Save settings” and Octoparse will auto-generate a scraping workflow on the left-hand side.
Step 3: Run the extraction to get data
Finally, you only need to save the crawler and hit “Run” to start extraction. The scraping process can be done within 5 minutes.
Closing thoughts
Please note that even though this article guides you through extracting Craigslist data, you should always respect its Terms of Service and scrape at a moderate frequency.
Data scraping tools can not only scrape all Craigslist listings, but also they are used in many scenarios, including Marketing, E-commerce and Retail, Data Science, Equity and Financial Research, Data Journalism, Academic, Risk management, Insurance and many more. You can read about web scraping uses in business in this article: 25 Hacks to Grow Your Business With Web Data Extraction.
Author: Milly
How to Extract Data from Twitter Without Coding
Top 5 Social Media Scraping Tools for 2020
Scrape video information from YouTube
Scrape public posts from Facebook
Web Scraping Craigslist: A Complete Tutorial | by Riley Predum

Web Scraping Craigslist: A Complete Tutorial | by Riley Predum

I’ve been looking to make a move recently. And what better way to know I’m getting a good price than to sample from the “population” of housing on Craigslist? Sounds like a job for…Python and web scraping! In this article, I’m going to walk you through my code that scrapes East Bay Area Craigslist for apartments. The code here, and/or the URI parameters rather, can be modified to pull from any region, category, property type, etc. Pretty cool, huh? I’m going to share GitHub gists of each cell in the original Jupyter Notebook. If you’d like to just see the whole code at once, clone the repo. Otherwise, enjoy the read and follow along! Getting the DataFirst things first I needed to use the get module from the requests package. Then I defined a variable, response, and assigned it to the get method called on the base URL. What I mean by base URL is the URL at the first page you want to pull data from, minus any extra arguments. I went to the apartments section for the East Bay and checked the “Has Picture” filter to narrow down the search just a little though, so it’s not a true base URL. I then imported BeautifulSoup from bs4, which is the module that can actually parse the HTML of the web page retrieved from the server. I then checked the type and length of that item to make sure it matches the number of posts on the page (there are 120). You can find my import statements and setup code below:It prints out the length of posts which is 120, as the find_all method on the newly created html_soup variable in the code above, I found the posts. I needed to examine the website’s structure to find the parent tag of the posts. Looking at the screenshot below, you can see that it’s

. That is the tag for one single post, which is literally the box that contains all the elements I grabbed! In order to scale this, make sure to work in the following way: grab the first post and all the variables you want from it, make sure you know how to access each of them for one post before you loop the whole page, and lastly, make sure you successfully scraped one page before adding the loop that goes through all sultSet is indexed, so I looked at the first apartment by indexing posts[0]. Surprise, it’s all the code that belongs to that

tag! You should have this output for the first post in posts (posts[0]), assigned to price of the post is easy to () removes whitespace before and after a stringI grabbed the date and time by specifying the attribute ‘datetime’ on class ‘result-date’. By specifying the ‘datetime’ attribute, I saved a step in data cleaning by making it unnecessary to convert this attribute from a string to a datetime object. This could also be made into a one-liner by placing [‘datetime’] at the end of the () call, but I split it into two lines for URL and post title are easy because the ‘href’ attribute is the link and is pulled by specifying that argument. The title is just the text of that number of bedrooms and square footage are in the same tag, so I split these two values and grabbed each one element-wise. The neighborhood is the tag of class “result-hood”, so I grabbed the text of next block is the loop for all the pages for the East Bay. Since there isn’t always information on square footage and number of bedrooms, I built in a series of if statements embedded within the for loop to handle all loop starts on the first page, and for each post in that page, it works through the following logic:I included some data cleaning steps in the loop, like pulling the ‘datetime’ attribute and removing the ‘ft2’ from the square footage variable, and making that value an integer. I removed ‘br’ from the number of bedrooms as that was scraped as well. That way, I started data cleaning with some work already done. Elegant code is the best! I wanted to do more, but the code would become too specific to this region and might not work across code below creates the dataframe from the lists of values! Awesome! There it is. Admittedly, there is still a little bit of data cleaning to be done. I’ll go through that real quick, and then it’s time to explore the data! Exploratory Data AnalysisSadly, after removing the duplicate URLs I saw that there are only 120 instances. These numbers will be different if you run the code, since there will be different posts at different times of scraping. There were about 20 posts that didn’t have bedrooms or square footage listed too. For statistical reasons, this isn’t an incredible data set, but I took note of that and pushed scriptive statistics for the quantitative variablesI wanted to see the distribution of the pricing for the East Bay so I made the above plot. Calling the. describe() method, I got a more detailed look. The cheapest place is $850, and the most expensive is $4, next code block generates a scatter plot, where the points are colored by the number of bedrooms. This shows a clear and understandable stratification: we see layers of points clustered around particular prices and square footages, and as price and square footage increase so do the number of ’s not forget the workhorse of Data Science: linear regression. We can call a regplot() on these two variables to get a regression line with a bootstrap confidence interval calculated about the line and shown as a shaded region with the code below. If you haven’t heard of bootstrap confidence intervals, they are a really cool statistical technique that are worth a looks like we have an okay fit of the line on these two variables. Let’s check the correlations. I called () to get these:Correlation matrix for our variablesAs suspected, correlation is strong between number of bedrooms and square footage. That makes sense since square footage increases as the number of bedrooms icing By Neighborhood ContinuedI wanted to get a sense of how location affects price, so I grouped by neighborhood, and aggregated by calculating the mean for each following is produced with this single line of code: oupby(‘neighborhood’)() where ‘neighborhood’ is the ‘by=’ argument, and the aggregator function is the mean. I noticed that there are two North Oaklands: North Oakland and Oakland North, so I recoded one of them into the other like so:eb_apts[‘neighborhood’]. replace(‘North Oakland’, ‘Oakland North’, inplace=True). Grabbing the price and sorting it in ascending order can show the cheapest and most expensive places to live. The full line of code is now: oupby(‘neighborhood’)()[‘price’]. sort_values() and results in the following output:Average price by neighborhood sorted in ascending orderLastly, I looked at the spread of each neighborhood in terms of price. By doing this, I saw how prices in neighborhoods can vary, and to what ’s the code that produces the plot that rkeley had a huge spread. This is probably because it includes South Berkeley, West Berkeley, and Downtown Berkeley. In a future version of this project it may be important to consider changing the scope of each of the variables so they are more reflective of the variability of price between neighborhoods in each, there you have it! Take a look at this the next time you’re in the market for housing to see what a good price should be. Feel free to check out the repo and try it for yourself, or fork the project and do it for your city! Let me know what you come up with! Scrape you learned something new and would like to pay it forward to the next learner, consider donating any amount you’re comfortable with, thanks! Happy coding! Riley

How to Scrape Data from Craigslist | Octoparse

Frequently Asked Questions about craigslist scraping

Does Craigslist allow scraping?

Just like Facebook and LinkedIn, Craigslist’s terms clearly state that all sorts of robots, spiders, scripts, scrapers, crawlers are prohibited. And they won’t allow people to steal their users’ personal information on the site.Jan 16, 2021

What is Craigslist scraping?

Craigslist has used a variety of technological and legal methods to prevent unauthorized parties from violating its terms of use by scraping, linking to, or accessing user postings for their own commercial purposes.Aug 24, 2017

How do you scrape on Craigslist?

Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch. … Web scraping started in a legal grey area where the use of bots to scrape a website was simply a nuisance.

ProxyBoys