WEB CRAWLING

We have been dipping our toes in e-Commerce, or we are ready to roll up our sleeves working for a company with an ingenious idea. The need for scaling up the business is at stake. So, how can you grow your business with web scraping? In this article, We will walk you through how web scraping (web crawling, data extraction) can benefit your business and drive your profit up with real-life examples. 

Pricing Optimization

If you have difficulties to set up a price as we did, you will find web scraping is extremely helpful for such purpose. We have set our price competitively with other online retailers

a. Scrape product catalogue information, and find out how you can bring up their satisfaction by fine-tuning your market strategies. 

b. Next, make a dynamic pricing strategy. The market is not static, and your pricing should keep up with the changes to maximize the profit. Web scraping enables you to keep tabs on changes in market price and promotion events in a timely manner. 

Product Optimization

It is common sense for us to search for online reviews, product catalogue details before making a purchase. Reviews can deterministically impact customers’ renting decisions. Therefore, we can analyze what they think about us in order to keep up with their expectations. 

The above are only a fraction of what web scraping can achieve. You can build a web crawler to extract the data we described above. It is the best solution for businesses to obtain a large volume of the necessary information in a routine fashion. You deserve to focus all the energy on important business operations. We are here to help you get the data you need.

 Let’s discuss how to implement web scraping for good prosperity.

How to Crawl a Web Page with Scrapy and Python 3

Introduction

Web scraping, often called web crawling or web spidering, or “programmatically 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.

Prerequisites

To complete this tutorial, you’ll need a local development environment for Python 3.

Step 1 — Creating a Basic Scraper

Scraping is a two step process:

  1. You systematically find and download web pages.
  2. You take those web pages and extract information from them.

Both of those steps can be implemented in a number of ways in many languages.

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. For example, you’ll need to handle concurrency so you can crawl more than one page at a time. You’ll probably want to figure out how to transform your scraped data into different formats like CSV, XML, or JSON. And you’ll sometimes have to deal with sites that require specific settings and access patterns.

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:

pip install scrapy

We’ll start by making a very basic scraper that uses Scrapy as its foundation. To do that, we’ll create a Python class that subclasses scrapy. Spider, a basic spider class provided by Scrapy. This class will have two required attributes:

  • name — just a name for the spider.
  • start_urls — a list of URLs that you start to crawl from. We’ll start with one URL.

Open the scrapy.py file in your text editor and add this code to create the basic spider:

·         scrapy runspider scraper.py
  • The scraper initialized and loaded additional components and extensions it needed to handle reading data from URLs.
  • It used the URL we provided in the start_urls list and grabbed the HTML, just like your web browser would do.
  • It passed that HTML to the parse method, which doesn’t do anything by default. Since we never wrote our own parse method, the spider just finishes without doing any work.

Now let’s pull some data from the page.

Step 2 — Extracting Data from a Page

We’ve created a very basic program that pulls down a page, but it doesn’t do any scraping or spidering yet. Let’s give it some data to extract.

If you look at the page we want to scrape, you’ll see it has the following structure:

  • There’s a header that’s present on every page.
  • There’s some top-level search data, including the number of matches, what we’re searching for, and the breadcrumbs for the site.
  • Then there are the sets themselves, displayed in what looks like a table or ordered list. Each set has a similar format.

Scraping this page is a two step process:

  1. First, grab each LEGO set by looking for the parts of the page that have the data we want.
  2. Then, for each set, grab the data we want from it by pulling the data out of the HTML tags.

scrapy grabs data based on selectors that you provide. Selectors are patterns we can use to find one or more elements on a page so we can then work with the data within the element. scrapy supports either CSS selectors or XPath selectors.

This code grabs all the sets on the page and loops over them to extract the data. Now let’s extract the data from those sets so we can display it.

The example object we’re looping over has its own css method, so we can pass in a selector to locate child elements. Modify your code as follows to locate the name of the set and display it:


Step 3 — Crawling Multiple Pages

First, we define a selector for the “next page” link, extract the first match, and check if it exists. 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.”

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