Web Scraping Amazon Using Python

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• How To Scrape Amazon Using Python
• Web Scraping Python Example

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Table of Contents

Introduction to web scraping

Web scraping is one of the tools at a developer’s disposal when looking to gather data from the internet. While consuming data via an API has become commonplace, most of the websites online don’t have an API for delivering data to consumers. In order to access the data they’re looking for, web scrapers and crawlers read a website’s pages and feeds, analyzing the site’s structure and markup language for clues. Generally speaking, information collected from scraping is fed into other programs for validation, cleaning, and input into a datastore or its fed onto other processes such as natural language processing (NLP) toolchains or machine learning (ML) models. There are a few Python packages we could use to illustrate with, but we’ll focus on Scrapy for these examples. Scrapy makes it very easy for us to quickly prototype and develop web scrapers with Python.

Scrapy vs. Selenium and Beautiful Soup

If you’re interested in getting into Python’s other packages for web scraping, we’ve laid it out here:

Scrapy concepts

Before we start looking at specific examples and use cases, let’s brush up a bit on Scrapy and how it works.

Spiders: Scrapy uses Spiders to define how a site (or a bunch of sites) should be scraped for information. Scrapy lets us determine how we want the spider to crawl, what information we want to extract, and how we can extract it. Specifically, Spiders are Python classes where we’ll put all of our custom logic and behavior.

Selectors:Selectors are Scrapy’s mechanisms for finding data within the website’s pages. They’re called selectors because they provide an interface for “selecting” certain parts of the HTML page, and these selectors can be in either CSS or XPath expressions.

Items:Items are the data that is extracted from selectors in a common data model. Since our goal is a structured result from unstructured inputs, Scrapy provides an Item class which we can use to define how our scraped data should be structured and what fields it should have.

Reddit-less front page

Suppose we love the images posted to Reddit, but don’t want any of the comments or self posts. We can use Scrapy to make a Reddit Spider that will fetch all the photos from the front page and put them on our own HTML page which we can then browse instead of Reddit.

To start, we’ll create a RedditSpider which we can use traverse the front page and handle custom behavior.

Above, we’ve defined a RedditSpider, inheriting Scrapy’s Spider. We’ve named it reddit and have populated the class’ start_urls attribute with a URL to Reddit from which we’ll extract the images.

At this point, we’ll need to begin defining our parsing logic. We need to figure out an expression that the RedditSpider can use to determine whether it’s found an image. If we look at Reddit’s robots.txt file, we can see that our spider can’t crawl any comment pages without being in violation of the robots.txt file, so we’ll need to grab our image URLs without following through to the comment pages.

By looking at Reddit, we can see that external links are included on the homepage directly next to the post’s title. We’ll update RedditSpider to include a parser to grab this URL. Reddit includes the external URL as a link on the page, so we should be able to just loop through the links on the page and find URLs that are for images.

In a parse method on our RedditSpider class, I’ve started to define how we’ll be parsing our response for results. To start, we grab all of the href attributes from the page’s links using a basic XPath selector. Now that we’re enumerating the page’s links, we can start to analyze the links for images.

To actually access the text information from the link’s href attribute, we use Scrapy’s .get() function which will return the link destination as a string. Next, we check to see if the URL contains an image file extension. We use Python’s any() built-in function for this. This isn’t all-encompassing for all image file extensions, but it’s a start. From here we can push our images into a local HTML file for viewing.

To start, we begin collecting the HTML file contents as a string which will be written to a file called frontpage.html at the end of the process. You’ll notice that instead of pulling the image location from the ‘//a/@href/‘, we’ve updated our links selector to use the image’s src attribute: ‘//img/@src’. This will give us more consistent results, and select only images.

As our RedditSpider’s parser finds images it builds a link with a preview image and dumps the string to our html variable. Once we’ve collected all of the images and generated the HTML, we open the local HTML file (or create it) and overwrite it with our new HTML content before closing the file again with page.close(). If we run scrapy runspider reddit.py, we can see that this file is built properly and contains images from Reddit’s front page.

But, it looks like it contains all of the images from Reddit’s front page – not just user-posted content. Let’s update our parse command a bit to blacklist certain domains from our results.

If we look at frontpage.html, we can see that most of Reddit’s assets come from redditstatic.com and redditmedia.com. We’ll just filter those results out and retain everything else. With these updates, our RedditSpider class now looks like the below:

We’re simply adding our domain whitelist to an exclusionary any()expression. These statements could be tweaked to read from a separate configuration file, local database, or cache – if need be.

Extracting Amazon price data

If you’re running an ecommerce website, intelligence is key. With Scrapy we can easily automate the process of collecting information about our competitors, our market, or our listings.

For this task, we’ll extract pricing data from search listings on Amazon and use the results to provide some basic insights. If we visit Amazon’s search results page and inspect it, we notice that Amazon stores the price in a series of divs, most notably using a class called .a-offscreen. We can formulate a CSS selector that extracts the price off the page:

With this CSS selector in mind, let’s build our AmazonSpider.

A few things to note about our AmazonSpider class: convert_money(): This helper simply converts strings formatted like ‘$45.67’ and casts them to a Python Decimal type which can be used for computations and avoids issues with locale by not including a ‘$’ anywhere in the regular expression. getall(): The .getall() function is a Scrapy function that works similar to the .get() function we used before, but this returns all the extracted values as a list which we can work with. Running the command scrapy runspider amazon.py in the project folder will dump output resembling the following:

It’s easy to imagine building a dashboard that allows you to store scraped values in a datastore and visualize data as you see fit.

Considerations at scale

As you build more web crawlers and you continue to follow more advanced scraping workflows you’ll likely notice a few things:

1. Sites change, now more than ever.
2. Getting consistent results across thousands of pages is tricky.
3. Performance considerations can be crucial.

Sites change, now more than ever

On occasion, AliExpress for example, will return a login page rather than search listings. Sometimes Amazon will decide to raise a Captcha, or Twitter will return an error. While these errors can sometimes simply be flickers, others will require a complete re-architecture of your web scrapers. Nowadays, modern front-end frameworks are oftentimes pre-compiled for the browser which can mangle class names and ID strings, sometimes a designer or developer will change an HTML class name during a redesign. It’s important that our Scrapy crawlers are resilient, but keep in mind that changes will occur over time.

Getting consistent results across thousands of pages is tricky

Slight variations of user-inputted text can really add up. Think of all of the different spellings and capitalizations you may encounter in just usernames. Pre-processing text, normalizing text, and standardizing text before performing an action or storing the value is best practice before most NLP or ML software processes for best results.

Performance considerations can be crucial

You’ll want to make sure you’re operating at least moderately efficiently before attempting to process 10,000 websites from your laptop one night. As your dataset grows it becomes more and more costly to manipulate it in terms of memory or processing power. In a similar regard, you may want to extract the text from one news article at a time, rather than downloading all 10,000 articles at once. As we’ve seen in this tutorial, performing advanced scraping operations is actually quite easy using Scrapy’s framework. Some advanced next steps might include loading selectors from a database and scraping using very generic Spider classes, or by using proxies or modified user-agents to see if the HTML changes based on location or device type. Scraping in the real world becomes complicated because of all the edge cases, Scrapy provides an easy way to build this logic in Python.

This post is a part of Kite’s new series on Python. You can check out the code from this and other posts on our GitHub repository.

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In the last few years, we saw a great shift in technology, where projects are moving towards “microservice architecture” vs the old 'monolithic architecture'. This approach has done wonders for us.

As we say, “smaller things are much easier to handle”, so here we have microservices that can be handled conveniently. We need to interact among different microservices. I handled it using the HTTP API call, which seems great and it worked for me.

But is this the perfect way to do things?

The answer is a resounding, 'no,' because we compromised both speed and efficiency here.

Then came in the picture, the gRPC framework, that has been a game-changer.

Python Web Scraping Pdf

What is gRPC?

Quoting the official documentation-
“gRPC or Google Remote Procedure Call is a modern open-source high-performance RPC framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking and authentication.”

Credit: gRPC

RPC or remote procedure calls are the messages that the server sends to the remote system to get the task(or subroutines) done.

Google’s RPC is designed to facilitate smooth and efficient communication between the services. It can be utilized in different ways, such as:

• Efficiently connecting polyglot services in microservices style architecture
• Connecting mobile devices, browser clients to backend services
• Generating efficient client libraries
  

Why gRPC?

- HTTP/2 based transport - It uses HTTP/2 protocol instead of HTTP 1.1. HTTP/2 protocol provides multiple benefits over the latter. One major benefit is multiple bidirectional streams that can be created and sent over TCP connections parallelly, making it swift.

- Auth, tracing, load balancing and health checking - gRPC provides all these features, making it a secure and reliable option to choose.

- Language independent communication- Two services may be written in different languages, say Python and Golang. gRPC ensures smooth communication between them.

- Use of Protocol Buffers - gRPC uses protocol buffers for defining the type of data (also called Interface Definition Language (IDL)) to be sent between the gRPC client and the gRPC server. It also uses it as the message interchange format.

Let's dig a little more into what are Protocol Buffers.

Protocol Buffers

Protocol Buffers like XML, are an efficient and automated mechanism for serializing structured data. They provide a way to define the structure of data to be transmitted. Google says that protocol buffers are better than XML, as they are:

• simpler
• three to ten times smaller
• 20 to 100 times faster
• less ambiguous
• generates data access classes that make it easier to use them programmatically
  

Protobuf are defined in .proto files. It is easy to define them.

Types of gRPC implementation

1. Unary RPCs:- This is a simple gRPC which works like a normal function call. It sends a single request declared in the .proto file to the server and gets back a single response from the server.

CODE: https://gist.github.com/velotiotech/d2938c90ee7948186e7a3848f3558577.js

2. Server streaming RPCs:- The client sends a message declared in the .proto file to the server and gets back a stream of message sequence to read. The client reads from that stream of messages until there are no messages.

CODE: https://gist.github.com/velotiotech/0bdb7a50673c97745b37995a83f74ba3.js

3. Client streaming RPCs:- The client writes a message sequence using a write stream and sends the same to the server. After all the messages are sent to the server, the client waits for the server to read all the messages and return a response.

CODE: https://gist.github.com/velotiotech/757cef3a558b6ffbd38ff6eee37ab8ab.js

4. Bidirectional streaming RPCs:- Both gRPC client and the gRPC server use a read-write stream to send a message sequence. Both operate independently, so gRPC clients and gRPC servers can write and read in any order they like, i.e. the server can read a message then write a message alternatively, wait to receive all messages then write its responses, or perform reads and writes in any other combination.

CODE: https://gist.github.com/velotiotech/3e64bbe6b9e15c13feb31b2204f27ec0.js

**gRPC guarantees the ordering of messages within an individual RPC call. In the case of Bidirectional streaming, the order of messages is preserved in each stream.

Implementing gRPC in Python

Currently, gRPC provides support for many languages like Golang, C++, Java, etc. I will be focussing on its implementation using Python.

CODE: https://gist.github.com/velotiotech/bb3daedb9e213985122dde02190653ac.js

This will install all the required dependencies to implement gRPC.

Unary gRPC

For implementing gRPC services, we need to define three files:-

• Proto file - Proto file comprises the declaration of the service that is used to generate stubs (<package_name>_pb2.py and <package_name>_pb2_grpc.py). These are used by the gRPC client and the gRPC server.</package_name></package_name>
• gRPC client - The client makes a gRPC call to the server to get the response as per the proto file.‍
• gRPC Server - The server is responsible for serving requests to the client.

CODE: https://gist.github.com/velotiotech/28d88d9bbf29c86e0f548cb73eeaa965.js

Web Scraping Amazon Using Python Tutorial

In the above code, we have declared a service named Unary. It consists of a collection of services. For now, I have implemented a single service GetServerResponse(). This service takes an input of type Message and returns a MessageResponse. Below the service declaration, I have declared Message and Message Response.

Once we are done with the creation of the .proto file, we need to generate the stubs. For that, we will execute the below command:-

CODE: https://gist.github.com/velotiotech/bc5fbd828ba23019161c8fd25566f1da.js

Two files are generated named unary_pb2.py and unary_pb2_grpc.py. Using these two stub files, we will implement the gRPC server and the client.

Implementing the Server

CODE: https://gist.github.com/velotiotech/3e6812a7277cc765dde2e4c77a707a67.js

In the gRPC server file, there is a GetServerResponse() method which takes `Message` from the client and returns a `MessageResponse` as defined in the proto file.

server() function is called from the main function, and makes sure that the server is listening to all the time. We will run the unary_server to start the server

CODE: https://gist.github.com/velotiotech/8d067e1d1ae747b03121255492bde7af.js

Implementing the Client

CODE: https://gist.github.com/velotiotech/75f6f2f53e722db2a7343c03782a74aa.js

In the __init__func. we have initialized the stub using ` self.stub = pb2_grpc.UnaryStub(self.channel)’ And we have a get_url function which calls to server using the above-initialized stub

This completes the implementation of Unary gRPC service.

Let's check the output:-

Run -> python3 unary_client.py

Output:-

message: 'Hello Server you there?'

message: 'Hello I am up and running. Received ‘Hello Server you there?’ message from you'

received: true

Bidirectional Implementation

CODE: https://gist.github.com/velotiotech/bbabd8c23f18d1da0c480339de226eb7.js

In the above code, we have declared a service named Bidirectional. It consists of a collection of services. For now, I have implemented a single service GetServerResponse(). This service takes an input of type Message and returns a Message. Below the service declaration, I have declared Message.

Once we are done with the creation of the .proto file, we need to generate the stubs. To generate the stub, we need the execute the below command:-

CODE: https://gist.github.com/velotiotech/b33906ac7adb8a51311b58f952ff8cd8.js

Two files are generated named bidirectional_pb2.py and bidirectional_pb2_grpc.py. Using these two stub files, we will implement the gRPC server and client.

Implementing the Server

CODE: https://gist.github.com/velotiotech/81b63c1a92f23b9c4478d09433a2f281.js

In the gRPC server file, there is a GetServerResponse() method which takes a stream of `Message` from the client and returns a stream of `Message` independent of each other. server() function is called from the main function and makes sure that the server is listening to all the time.

We will run the bidirectional_server to start the server:

CODE: https://gist.github.com/velotiotech/11e327c95e9fed1fb1be84357ee0566a.js

Implementing the Client

CODE: https://gist.github.com/velotiotech/ad7b026cad3b523de876cf131d52d4d2.js

In the run() function. we have initialised the stub using ` stub = bidirectional_pb2_grpc.BidirectionalStub(channel)’

And we have a send_message function to which the stub is passed and it makes multiple calls to the server and receives the results from the server simultaneously.

This completes the implementation of Bidirectional gRPC service.

Let's check the output:-

Run -> python3 bidirectional_client.py

Output:-

Hello Server Sending you the First message

Hello Server Sending you the Second message

Hello Server Sending you the Third message

Hello Server Sending you the Fourth message

Hello Server Sending you the Fifth message

Hello from the server received your First message

Hello from the server received your Second message

Hello from the server received your Third message

Hello from the server received your Fourth message

Hello from the server received your Fifth message‍

For code reference, please visit here.

How To Scrape Amazon Using Python

Conclusion‍

Web Scraping Python Example

gRPC is an emerging RPC framework that makes communication between microservices smooth and efficient. I believe gRPC is currently confined to inter microservice but has many other utilities that we will see in the coming years. To know more about modern data communication solutions, check out this blog.