How To Scrape Amazon Using Python Scrapy Library [Tutorial]

How to install Scrapy ?

Python : As Scrapy has been built using Python language, one has to install it first.

pip : pip is a python package manager tool which maintains a package repository and install python libraries, and its dependencies automatically. It is better to install pip according to system OS, and then try to follow the standard way for installing Scrapy.

lxml : This is an optional package but needs to be installed if one is willing to scrap html data. lxml is a python library which helps to structure html tree, as web pages use html hierarchy to organise information or Data.One can install Scrapy using pip (which is the canonical way to install Python packages).

pip install scrapy

How to get started with Scrapy ?

Enter a directory where you’d like to store your code and run:

scrapy startproject test_project

test_project/
    scrapy.cfg
 test_project/
     __init__.py
     items.py
     pipelines.py
     settings.py
     spiders/
          __init__.py

Scrapy, as an application framework follows a project structure along with Object Oriented style of programming to define items and spiders for overall applications.

1. scrapy.cfg

2. test_project

3. items.py 

Items are containers that will be loaded with the scraped data; they work like simple Python dicts. While one can use plain Python dicts with Scrapy, Items provide additional protection against populating undeclared fields, preventing typos. They are declared by creating a 

scrapy.Item

scrapy.Field

4. pipelines.py 

After an item has been scraped by a spider, it is sent to the Item Pipeline which processes it through several components that are executed sequentially. Each item pipeline component is a Python class which has to implement a method called 

process_item

5. settings.py 

6. spiders 

Spiders is a directory which contains all spiders/crawlers as Python classes. Whenever one runs/crawls any spider then scrapy looks into this directory and tries to find the spider with its name provided by user.

In other words, Spiders are the place where one defines the custom behavior for crawling and parsing pages for a particular site.Spiders have to define three major attributes i.e start_urls which tells which URLs are to be scrapped, allowed_domains which defines only those domain names which need to scraped and parse is a method which is called when any response comes from lodged requests. These attributes are important because these constitute the base of Spider definitions.

Let’s Scrape Amazon Web Page 

To understand how scrapy works and how can we use it in practical scenarios, lets take an example in which we will scrap data related to a product , for example product name, its price, category and its availability on 

amazon.com

scrapy startproject amazon 

1. Update items.py with fields which we want to extract. Here for example

   product name, category, price

   etc. 
2. Create a new Spider in which we need to define the necessary elements, like allowed_domains, start_urls, parse method to parse  response object.
3. Update pipelines.py for further data processing.

Lets start with items.py first. Below is the code which describes multiple required fields in the framework.

# -*- coding: utf-8 -*-
 
# Define here the models for your scraped items
#
# See documentation in:
# //doc.scrapy.org/en/latest/topics/items.html
 
import scrapy
 
class AmazonItem(scrapy.Item):
  # define the fields for your item here like:
  product_name = scrapy.Field()
  product_sale_price = scrapy.Field()
  product_category = scrapy.Field()
  product_original_price = scrapy.Field()
  product_availability = scrapy.Field()

1. We can create simple Python class in spiders directory and import essential modules to it
2. We can use default utility which is provided by scrapy framework itself.

Here we are going to use the default utility called genspider to create Spiders in framework. It will automatically create a class with default template in spiders directory.

scrapy genspider AmazonProductSpider

In newly created AmazonProductSpider, we need to define its name,  URLs and possible domains to scrap data. We also need to implement parse method where  custom commands can be defined  for filling item fields and further processing can be done on the response object.

This parse method does not return but yield things. Yield in python means that python will start the execution from where it has been stopped last time.

Here is the code for Spider. A name is defined for Spider, which should be unique throughout all the Spiders, because scrapy searches for Spiders using its name. allowed_domains is initialized with 

amazon.com

start_urls

# -*- coding: utf-8 -*-
import scrapy
from amazon.items import AmazonItem

class AmazonProductSpider(scrapy.Spider):
  name = "AmazonDeals"
  allowed_domains = ["amazon.com"]
  
  #Use working product URL below
  start_urls = [
     "//www.amazon.com/dp/B0046UR4F4", "//www.amazon.com/dp/B00JGTVU5A",
     "//www.amazon.com/dp/B00O9A48N2", "//www.amazon.com/dp/B00UZKG8QU"
     ]
 
  def parse(self, response):
  items = AmazonItem()
  title = response.xpath('//h1[@id="title"]/span/text()').extract()
  sale_price = response.xpath('//span[contains(@id,"ourprice") or contains(@id,"saleprice")]/text()').extract()
  category = response.xpath('//a[@class="a-link-normal a-color-tertiary"]/text()').extract()
  availability = response.xpath('//div[@id="availability"]//text()').extract()
  items['product_name'] = ''.join(title).strip()
  items['product_sale_price'] = ''.join(sale_price).strip()
  items['product_category'] = ','.join(map(lambda x: x.strip(), category)).strip()
  items['product_availability'] = ''.join(availability).strip()
  yield items

In parse method,  an item object is defined and is filled with required information using xpath utility of response object. xpath is a search function which is used to find elements in html tree structure. Lastly lets yield the items object, so that scrapy can do further processing on it.

Next, after scraping data, scrapy calls Item pipelines to process them. These are called as Pipeline classes and we can use these classes to store data in a file or database or in any other way. It is a default class like Items which scrapy generates for users.

# -*- coding: utf-8 -*-
# Define your item pipelines here
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: //doc.scrapy.org/en/latest/topics/item-pipeline.html

class AmazonPipeline(object):
  def process_item(self, item, spider):
  return item

Pipeline classes implement process_item method which is called each and every time whenever items is being yielded by a Spider. It takes item and spider class as arguments and returns a dict object. So for this example, we are just returning item dict as it is.

# Configure item pipelines
# See //scrapy.readthedocs.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
  'amazon.pipelines.AmazonPipeline': 300,
}

scrapy crawl AmazonDeals

scrapy crawl AmazonDeals -o items.json

So the above command will save the item objects in items.json file. As we are returning item objects in pipeline class, scrapy will automatically store these item objects into items.json. Here is the output of this process.

[
{"product_category": "Electronics,Computers & Accessories,Data Storage,External Hard Drives", "product_sale_price": "$949.95", "product_name": "G-Technology G-SPEED eS PRO High-Performance Fail-Safe RAID Solution for HD/2K Production 8TB (0G01873)", "product_availability": "Only 1 left in stock."},
{"product_category": "Electronics,Computers & Accessories,Data Storage,USB Flash Drives", "product_sale_price": "", "product_name": "G-Technology G-RAID with Removable Drives High-Performance Storage System 4TB (Gen7) (0G03240)", "product_availability": "Available from these sellers."},
{"product_category": "Electronics,Computers & Accessories,Data Storage,USB Flash Drives", "product_sale_price": "$549.95", "product_name": "G-Technology G-RAID USB Removable Dual Drive Storage System 8TB (0G04069)", "product_availability": "Only 1 left in stock."},
{"product_category": "Electronics,Computers & Accessories,Data Storage,External Hard Drives", "product_sale_price": "$89.95", "product_name": "G-Technology G-DRIVE ev USB 3.0 Hard Drive 500GB (0G02727)", "product_availability": "Only 1 left in stock."}
]

Ta da! We have successfully covered scraping using . We have covered most of the stuff related to  and its related modules and also understood how can we can use it independently through an example.

References 

This article post was originally published at //blog.datahut.co/tutorial-how-to-scrape-amazon-data-using-python-scrapy/