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This is the 1 post of my Scrapy Tutorial Series , in this Scrapy tutorial, I will talk about the features of Scrapy, BeautifulSoup, compare them, and help you decide which one is better for your projects.
Scrapy or Selenium? - By Low Wei Hong
BeautifulSoup is a tool which help programmer quickly extract valid data from web pages, its API is very friendly to newbie developer, and it can also handle malformed markup very well. However, in most cases, BeautifulSoup alone can not get the job done, you need use another package such as urlib2 or requests to help you download the web page and then you can use BeautifulSoup to parse the HTML source code. The doc of BeautifulSoup is very comprehensive you can get a lot of examples there and quickly learn how to use it.
BeautifulSoup works fine on Python 2 and Python 3, so compatibility will not be a problem, below is a code example of BeautifulSoup , as you can see, it is very beginner-friendly. Scrapy is a web crawling framework for developer to write code to create spider , which define how a certain site or a group of sites will be scraped.
The biggest feature is that it is built on Twisted, an asynchronous networking library, so Scrapy is implemented using a non-blocking aka asynchronous code for concurrency, which makes the spider performance is very great. When you do something synchronously, you wait for it to finish before moving on to another task. When you do something asynchronously, you can move on to another task before it finishes. Scrapy also works fine on Python 2 and Python 3, so compatibility will not be a problem.
- Learning Scrapy by Dimitrios Kouzis-loukas.
- Problems from the book;
- LEARNING SCRAPY.
The two Python web scraping tools are created to do different jobs. When you compare BeautifulSoup vs Scrapy to figure out what is the best for your project, you should consider many factors. BeautifulSoup is very easy to learn, you can quickly use it to extract the data you want, in most cases, you will also need a downloader to help you get the HTML source, it is highly recommended to use Requests package instead of urllib2 from built-in python library to implement this function.
Since Scrapy does no only deal with content extraction but also many other tasks such as downloading HTML, learning curve of Scrapy is much steeper, you need to read some Scrapy Tutorial or Scrapy Doc to understand how it works, and work hard to become a Scrapy expert.
Learning Scrapy : learn the art of efficient web scraping and crawling with Python
Language: English. Brand new Book. It is perfect for someone , who needs instant access to large amounts of semi-structured data effortlessly. It starts off by explaining the fundamentals of Scrapy framework, followed by a thorough description of how to extract data from any source, clean it up, shape it as per your requirement using Python and 3rd party APIs. Next you will be familiarised with the process of storing the scrapped data in databases as well as search engines and performing real time analytics on them with Spark Streaming.
By the end of this book, you will perfect the art of scarping data for your applications with easeStyle and approachIt is a hands on guide, with first few chapters written as a tutorial, aiming to motivate you and get you started quickly.
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Web scraping can be used to make an aggregator that you can use to compare data. For example, you want to buy a tablet, and you want to compare products and prices together you can crawl your desired pages and store in an excel file.
Here you will be scraping aliexpress. Now, you will create a custom spider for the same page. First, you need to create a Scrapy project in which your code and results will be stored. Write the following command in the command line or anaconda prompt. This will create a hidden folder in your default python or anaconda installation.
You can give any name. You can view the folder contents directly through explorer. Following is the structure of the folder:. Once you have created the project you will change to the newly created directory and write the following command:. The code in that file is as below:. Information: You can use BeautifulSoup inside parse function of the Scrapy spider to parse the html document. Note: You can extract data through css selectors using response. You will see the example of response.
You can add the extraction logic to the pass function as below:. Information: zip takes n number of iterables and returns a list of tuples. The yield keyword is used whenever you are defining a generator function. A generator function is just like a normal function except it uses yield keyword instead of return. The yield keyword is used whenever the caller function needs a value and the function containing yield will retain its local state and continue executing where it left off after yielding value to the caller function.
Here yield gives the generated dictionary to Scrapy which will process and save it!
What is included in this Scrapy tutorial
To save a CSV file, open settings. After saving the settings. Scrapy's Feed Export can also add a timestamp and the name of spider to your file name, or you can use these to identify a directory in which you want to store. The Feed changes you make in settings. You can also set custom settings for a particular spider that will override the settings in the settings.
The second link is the page 2 of the same tablets search results. It will become impractical to add all links.
If a page has subsequent pages, you will see a navigator for it at the end of the page that will allow moving back and forth the pages. In the case you have been implementing in this tutorial, you will see it like this:. Here comes a little bit of CSS! Each web page has its own structure. You will have to study the structure a little bit on how you can get the desired element.
Always try out response. After receiving the new URL, it will scrape that link executing the for body and again look for the next page. This will continue until it doesn't get a next page link. Here you might want to sit back and enjoy your spider scraping all the pages.