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We provide the Alibaba web scraper to extract product data such as product names, images, reviews, prices, descriptions, specifications, etc
3i Data Scraping provide Alibaba data scraping services, it becomes easy and quick to extract Alibaba product data like product information, price details, and wholesaler data from Alibaba.com and you can download the data in your required format (HTML, JSON, XML, API) with reasonably priced web scraping service in the USA.
This guide shows you how to use python and beautiful soup with a database to extract product data from the Alibaba website and create an excel spreadsheet from the accumulated data.
Read More: Extract Alibaba Product Data Using Python
How to Scrape Alibaba.com Product Data Using Scrapy?
Scrapy is the most common open-source data scraping framework. Created in Python, this has the majority of modules that you would require to proficiently scrape, process, as well as store information from the websites in almost all structured data formats. Scrapy is the best option for web data crawlers that extracts data from different kinds of pages.
In this tutorial blog, we will exhibit you how to extract product data from Alibaba.com which is the world’s top marketplace.
Requirements
Installing Python 3 with Pip
We will utilize Python 3 in this tutorial. For starting, you require a computer having Python 3 as well as PIP.
You can use the guides given below for installing Python 3 as well as pip:
For Linux, use
http://docs.python-guide.org/en/latest/starting/install3/linux/
For Mac, use
http://docs.python-guide.org/en/latest/starting/install3/osx/
Package Installation
pip3 install scrapy selectorlib
If you want more information on installation, you can find from this links– https://doc.scrapy.org/en/latest/intro/
How to Start a Scrapy Project?
Let’s start a scrapy project with the given command.
scrapy startproject scrapy_alibaba
The given command makes the Scrapy project using a Project Name like (scrapy_alibaba) as a folder name. This will have all the required files having the suitable structure as well as fundamental docstrings for every file, having the structure close to
scrapy_alibaba/ # Project root directory scrapy.cfg # Contains the configuration information to deploy the spider scrapy_alibaba/ # Project's python module __init__.py items.py # Describes the definition of each item that we’re scraping middlewares.py # Project middlewares pipelines.py # Project pipelines file settings.py # Project settings file spiders/ # All the spider code goes into this directory __init__.py
Making a Spider
Scrapy is having an in-built command named genspider for generating the fundamental spider templates.
scrapy genspider <spidername> <website>
Now, it’s time to produce our spider
scrapy genspider alibaba_crawler alibaba.com
as well as it will make the spiders/scrapy_alibaba.py file with the primary template for crawling alibaba.com.
Its code will look like that:
# -*- coding: utf-8 -*- import scrapy class AlibabaCrawlerSpider(scrapy.Spider): name = 'alibaba_crawler' allowed_domains = ['alibaba.com'] start_urls = ['http://alibaba.com/'] def parse(self, response): pass
The Alibaba Crawler class receives the basic class scrapy.spider as the Spider class understand how to use links as well as scrape data from the web pages however it doesn’t identify where to see or which data to scrape. We would add this data later.
Functions & Variables
name is a spider’s name which was provided in a typical generation command.
You can utilize this name for starting the spider from a command line.
The listing of allowed_domains are domains, which a spider is permitted to crawl
The start_urls is a URL that a spider would start scraping whenever it is entreated.
parse() is a default callback technique of Scrapy that is asked for the requests without any explicitly given callback. The parse function becomes invoked after every start_url gets crawled. You may use this utility to parse the response, scrape the extracted data, as well as get newer URLs to trail by making newer requests (Request) through them.
Scrapy offers comprehensive data about crawling as you experience the logs and understand what's going through in a spider.
This spider is prepared with a bot name called “scrapy_alibaba” as well as prints all the packages utilized in a project having version numbers.
Scrapy searches for the spider modules that are positioned in a /spiders directory. You can set the default values with variables like CONCURRENT_REQUESTS_PER_DOMAIN, CONCURRENT_REQUESTS, SPIDER_MODULES, and DOWNLOAD_TIMEOUT respectively.
Burdened different components like extensions, middlewares, as well as pipelines that are required to cope with the requests
Utilize the URLs given in the start_urls as well as rescued the HTML contents of a page. As we didn’t identify callbacks for start_urls then the reply is expected at parse() function. Also, we did not compose any lines for handling the response expected, so that the spider ended with the stats like pages extracted in a crawl, bandwidth utilized in the bytes, status code counts, total items scraped, etc.
Scraping Data from Alibaba
For this tutorial blog, we will scrape the following data fields from all search result pages of Alibaba:
Product’s Name
Pricing Range
Product’s Image
Product Links
Minimum Order
Seller’s Name
Seller’s Response Rate
Total Years as a Seller
You can go extra and extract pricing and product details depending on the orders and filters. Nowadays, we’ll make it simple as well as stick to the fields.
Whenever you search any keywords like “earphones”, you will observe that a result page is having a URL similar to https://www.alibaba.com/trade/search?fsb=y&IndexArea=product_en&CatId=&SearchText=earphones&viewtype=G in which a parameter SearchText has the keywords you have searched for.
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