Easy Web Scraping Python

Easy Web Scraping Python Interview
Web Scraping Using Python
Web Scraping Python Example
Easy Web Scraping Python Tutorial

Once you’ve put together enough web scrapers, you start to feel like you can do it in your sleep. I’ve probably built hundreds of scrapers over the years for my own projects, as well as for clients and students in my web scraping course.

Occasionally though, I find myself referencing documentation or re-reading old code looking for snippets I can reuse. One of the students in my course suggested I put together a “cheat sheet” of commonly used code snippets and patterns for easy reference.

I decided to publish it publicly as well – as an organized set of easy-to-reference notes – in case they’re helpful to others.

There are two basic tasks that are used to scrape web sites: Load a web page to a string. Parse HTML from a web page to locate the interesting bits. Python offers two excellent tools for the above tasks. I will use the awesome requests to load web pages, and BeautifulSoup to do the parsing. We can put these two packages in a virtual environment. Loading Web Pages with 'request' The requests module allows you to send HTTP requests using. Aug 30, 2020 While Web Scrapping is an easy way to get a large volume of data in a relatively short time frame, it adds stress to the server where the source is hosted. This is also one of the main reasons why many websites don’t allow scraping all on their website.

Learn Python here: this video, we will talk about basics of web scraping using python.

While it’s written primarily for people who are new to programming, I also hope that it’ll be helpful to those who already have a background in software or python, but who are looking to learn some web scraping fundamentals and concepts.

Useful Libraries

For the most part, a scraping program deals with making HTTP requests and parsing HTML responses.

I always make sure I have requests and BeautifulSoup installed before I begin a new scraping project. From the command line:

Then, at the top of your .py file, make sure you’ve imported these libraries correctly.

Making Simple Requests

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)

Inspecting the Response

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)

Extracting Content from HTML

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.

Using Regular Expressions

Using Regular Expressions to look for HTML patterns is famously NOT recommended at all.

However, regular expressions are still useful for finding specific string patterns like prices, email addresses or phone numbers.

Run a regular expression on the response text to look for specific string patterns:

Using BeautifulSoup

BeautifulSoup is widely used due to its simple API and its powerful extraction capabilities. It has many different parser options that allow it to understand even the most poorly written HTML pages – and the default one works great.

Compared to libraries that offer similar functionality, it’s a pleasure to use. 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>...</li>)

Look for the tag with a specific ID attribute (eg: <div>...</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 or the href attribute of an <a> 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

Using XPath Selectors

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. I haven’t found a pattern I couldn’t parse using the above methods.

If you’re really dedicated to using them for some reason, you can use the lxml library instead of BeautifulSoup, as described here.

Storing Your Data

Now that you’ve extracted your data from the page, it’s time to save it somewhere.

Note: The implication in these examples is that the scraper went out and collected all of the items, and then waited until the very end to iterate over all of them and write them to a spreadsheet or database.

I did this to simplify the code examples. In practice, you’d want to store the values you extract from each page as you go, so that you don’t lose all of your progress if you hit an exception towards the end of your scrape and have to go back and re-scrape every page.

Writing to a CSV

Probably the most basic thing you can do is write your extracted items to a CSV file. By default, each row that is passed to the csv.writer object to be written has to be a python list.

In order for the spreadsheet to make sense and have consistent columns, you need to make sure all of the items that you’ve extracted have their properties in the same order. This isn’t usually a problem if the lists are created consistently.

If you’re extracting lots of properties about each item, sometimes it’s more useful to store the item as a python dict instead of having to remember the order of columns within a row. The csv module has a handy DictWriter that keeps track of which column is for writing which dict key.

Writing to a SQLite Database

You can also use a simple SQL insert if you’d prefer to store your data in a database for later querying and retrieval.

Learn More

If you’d like to learn more about web scraping, I currently have an ebook and online course that I offer, as well as a free sandbox website that’s designed to be easy for beginners to scrape.

You can also subscribe to my blog to get emailed when I release new articles.

In the previous post about Web Scraping with Python we talked a bit about Scrapy. In this post we are going to dig a little bit deeper into it.

Scrapy is a wonderful open source Python web scraping framework. It handles the most common use cases when doing web scraping at scale:

Multithreading
Crawling (going from link to link)
Extracting the data
Validating
Saving to different format / databases
Many more

The main difference between Scrapy and other commonly used librairies like Requests / BeautifulSoup is that it is opinionated. It allows you to solve the usual web scraping problems in an elegant way.

The downside of Scrapy is that the learning curve is steep, there is a lot to learn, but that is what we are here for :)

In this tutorial we will create two different web scrapers, a simple one that will extract data from an E-commerce product page, and a more “complex” one that will scrape an entire E-commerce catalog!

Basic overview

You can install Scrapy using pip. Be careful though, the Scrapy documentation strongly suggests to install it in a dedicated virtual environnement in order to avoid conflicts with your system packages.

I'm using Virtualenv and Virtualenvwrapper:

and

You can now create a new Scrapy project with this command:

This will create all the necessary boilerplate files for the project.

Here is a brief overview of these files and folders:

items.py is a model for the extracted data. You can define custom model (like a Product) that will inherit the scrapy Item class.
middlewares.py Middleware used to change the request / response lifecycle. For example you could create a middle ware to rotate user-agents, or to use an API like ScrapingBee instead of doing the requests yourself.
pipelines.py In Scrapy, pipelines are used to process the extracted data, clean the HTML, validate the data, and export it to a custom format or saving it to a database.
/spiders is a folder containing Spider classes. With Scrapy, Spiders are classes that define how a website should be scraped, including what link to follow and how to extract the data for those links.
scrapy.cfg is a configuration file to change some settings

Scraping a single product

In this example we are going to scrape a single product from a dummy E-commerce website. Here is the first the product we are going to scrape:

https://clever-lichterman-044f16.netlify.com/products/taba-cream.1/

We are going to extract the product name, picture, price and description.

Scrapy Shell

Scrapy comes with a built-in shell that helps you try and debug your scraping code in real time. You can quickly test your XPath expressions / CSS selectors with it. It's a very cool tool to write your web scrapers and I always use it!

You can configure Scrapy Shell to use another console instead of the default Python console like IPython. You will get autocompletion and other nice perks like colorized output.

In order to use it in your scrapy Shell, you need to add this line to your scrapy.cfg file:

Once it's configured, you can start using scrapy shell:

We can start fetching a URL by simply:

This will start by fetching the /robot.txt file.

In this case there isn't any robot.txt, that's why we can see a 404 HTTP code. If there was a robot.txt, by default Scrapy will follow the rule.

You can disable this behavior by changing this setting in settings.py:

Then you should should have a log like this:

You can now see your response object, response headers, and try different XPath expression / CSS selectors to extract the data you want.

You can see the response directly in your browser with:

Note that the page will render badly inside your browser, for lots of different reasons. This can be CORS issues, Javascript code that didn't execute, or relative URLs for assets that won't work locally.

The scrapy shell is like a regular Python shell, so don't hesitate to load your favorite scripts/function in it.

Extracting Data

Scrapy doesn't execute any Javascript by default, so if the website you are trying to scrape is using a frontend framework like Angular / React.js, you could have trouble accessing the data you want.

Now let's try some XPath expression to extract the product title and price:

In order to extract the price, we are going to use an XPath expression, we're selecting the first span after the div with the class my-4

I could also use a CSS selector:

Creating a Scrapy Spider

With Scrapy, Spiders are classes where you define your crawling (what links / URLs need to be scraped) and scraping (what to extract) behavior.

Here are the different steps used by a spider to scrape a website:

It starts by looking at the class attribute start_urls, and call these URLs with the start_requests() method. You could override this method if you need to change the HTTP verb, add some parameters to the request (for example, sending a POST request instead of a GET).
It will then generate a Request object for each URL, and send the response to the callback function parse()
The parse() method will then extract the data (in our case, the product price, image, description, title) and return either a dictionnary, an Item object, a Request or an iterable.

You may wonder why the parse method can return so many different objects. It's for flexibility. Let's say you want to scrape an E-commerce website that doesn't have any sitemap. You could start by scraping the product categories, so this would be a first parse method.

This method would then yield a Request object to each product category to a new callback method parse2()For each category you would need to handle pagination Then for each product the actual scraping that generate an Item so a third parse function.

With Scrapy you can return the scraped data as a simple Python dictionary, but it is a good idea to use the built-in Scrapy Item class.It's a simple container for our scraped data and Scrapy will look at this item's fields for many things like exporting the data to different format (JSON / CSV…), the item pipeline etc.

So here is a basic Product class:

Now we can generate a spider, either with the command line helper:

Or you can do it manually and put your Spider's code inside the /spiders directory.

Tired of getting blocked while scraping the web? Our API handles headless browsers and rotates proxies for you.

There are different types of Spiders in Scrapy to solve the most common web scraping use cases:

Spider that we will use. It takes a start_urls list and scrape each one with a parse method.
CrawlSpider follows links defined by a set of rules
SitemapSpider extract URLs defined in a sitemap
Many more

In this EcomSpider class, there are two required attributes:

name which is our Spider's name (that you can run using scrapy runspider spider_name)
start_urls which is the starting URL

The allowed_domains is optionnal but important when you use a CrawlSpider that could follow links on different domains.

Then I've just populated the Product fields by using XPath expressions to extract the data I wanted as we saw earlier, and we return the item.

You can run this code as follow to export the result into JSON (you could also export to CSV)

You should then get a nice JSON file:

Item loaders

There are two common problems that you can face while extracting data from the Web:

For the same website, the page layout and underlying HTML can be different. If you scrape an E-commerce website, you will often have a regular price and a discounted price, with different XPath / CSS selectors.
The data can be dirty and need some kind of post processing, again for an E-commerce website it could be the way the prices are displayed for example ($1.00, $1, $1,00 )

Scrapy comes with a built-in solution for this, ItemLoaders.It's an interesting way to populate our Product object.

You can add several XPath expression to the same Item field, and it will test it sequentially. By default if several XPath are found, it will load all of them into a list.

You can find many examples of input and output processors in the Scrapy documentation.

It's really useful when you need to transorm/clean the data your extract.For example, extracting the currency from a price, transorming a unit into another one (centimers in meters, Celcius degres in Fahrenheit) …

In our webpage we can find the product title with different XPath expressions: //title and //section[1]//h2/text()

Here is how you could use and Itemloader in this case:

Generally you only want the first matching XPath, so you will need to add this output_processor=TakeFirst() to your item's field constructor.

In our case we only want the first matching XPath for each field, so a better approach would be to create our own ItemLoader and declare a default output_processor to take the first matching XPath:

I also added a price_in which is an input processor to delete the dollar sign from the price. I'm using MapCompose which is a built-in processor that takes one or several functions to be executed sequentially. You can add as many functions as you like for . The convention is to add _in or _out to your Item field's name to add an input or output processor to it.

There are many more processors, you can learn more about this in the documentation

Easy Web Scraping Python Interview

Scraping multiple pages

Now that we know how to scrape a single page, it's time to learn how to scrape multiple pages, like the entire product catalog.As we saw earlier there are different kinds of Spiders.

When you want to scrape an entire product catalog the first thing you should look at is a sitemap. Sitemap are exactly built for this, to show web crawlers how the website is structured.

Most of the time you can find one at base_url/sitemap.xml. Parsing a sitemap can be tricky, and again, Scrapy is here to help you with this.

In our case, you can find the sitemap here: https://clever-lichterman-044f16.netlify.com/sitemap.xml

If we look inside the sitemap there are many URLs that we are not interested by, like the home page, blog posts etc:

Fortunately, we can filter the URLs to parse only those that matches some pattern, it's really easy, here we only to have URL thathave /products/ in their URLs:

You can run this spider as follow to scrape all the products and export the result to a CSV file:scrapy runspider sitemap_spider.py -o output.csv

Now what if the website didn't have any sitemap? Once again, Scrapy has a solution for this!

Let me introduce you to the… CrawlSpider.

The CrawlSpider will crawl the target website by starting with a start_urls list. Then for each url, it will extract all the links based on a list of Rule.In our case it's easy, products has the same URL pattern /products/product_title so we only need filter these URLs.

As you can see, all these built-in Spiders are really easy to use. It would have been much more complex to do it from scratch.

With Scrapy you don't have to think about the crawling logic, like adding new URLs to a queue, keeping track of already parsed URLs, multi-threading…

Conclusion

In this post we saw a general overview of how to scrape the web with Scrapy and how it can solve your most common web scraping challenges. Of course we only touched the surface and there are many more interesting things to explore, like middlewares, exporters, extensions, pipelines!

Web Scraping Using Python

If you've been doing web scraping more “manually” with tools like BeautifulSoup / Requests, it's easy to understand how Scrapy can help save time and build more maintainable scrapers.

I hope you liked this Scrapy tutorial and that it will motivate you to experiment with it.

For further reading don't hesitate to look at the great Scrapy documentation.

Web Scraping Python Example

We have also published our custom integration with Scrapy, it allows you to execute Javascript with Scrapy, do not hesitate to check it out.

Easy Web Scraping Python Tutorial

You can also check out our web scraping with Python tutorial to learn more about web scraping.

Happy Scraping!

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