Extracting information from E-Commerce sites such as Alibaba, Amazon, eBay, help to provide enormous opportunity for competitors, market research, and price comparison firm. Being among the foremost e-commerce companies, Alibaba products catalog is huge and handy to anyone who is looking to extract data. Extracting Alibaba Product Data can be difficult if you are not having accurate resources and team to perform Alibaba Product Data Extracting. Outsourcing Alibaba extracting helps you to fulfill all your requirements with dedicated scraping services.

Installing Python 3 with Pip

We utilize Python 3 in this Blog. To begin, you require a PC using Python 3 as well as PIP.

  • Mac: - http://docs.python-guide.org/en/latest/starting/install3/osx/
  • Linux: - http://docs.python-guide.org/en/latest/starting/install3/linux/
  • Window: - https://www.retailgators.com/how-to-install-python3-in-windows-10/
pip3 install scrapyselectorlib

Find out more information by installing here -


Creating Scrapy Projects

Let us create scrapy task using the command given below.


It can help to create Scrapy task with the help of Name of Project (scrapy_alibaba) as folder name. This contains all required files with accurate structure as well as basics with each file.

from selectorlib import Extractor
scrapy_alibaba/ # Project root directory
scrapy.cfg # Contains the configuration information to deploy the spider
scrapy_alibaba/ # Project's python module
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
Creating a Spider

The Scrapy has built a command named genspiderso that you can produce the fundamental spider templet.


Let’s produce our spider


This will help to create a file spider/scrapy_alibaba.py for recent templets for crawling Alibaba.com

This code is shown here:
name = 'alibaba_crawler'
allowed_domains = ['alibaba.com']
start_urls = ['http://alibaba.com/']
defparse(self, response):
Searching Keywords from the file

Let us make the CSV file it named keywords.csv.

This file shows that if we want to search distinctly for earplugs and headphones.


It’s time to use CSV Python’s standard module for reading the keyword file.

defparse(self, response):
"""Function to read keywords from keywords file"""
keywords = csv.DictReader(open(os.path.join(os.path.dirname(__file__),"../resources/keywords.csv")))
for keyword in keywords:
search_text = keyword["keyword"]
url = "https://www.alibaba.com/trade/search?fsb=y&IndexArea=product_en&CatId=&SearchText={0}&viewtype=G".format(search_text)
yieldscrapy.Request(url, callback=self.parse_listing, meta={"search_text":search_text})
A Complete Scrapy Spider’s Code

You can see the whole code at - https://contactus/retailgators/alibaba-scraper

A spider called alibaba_crawler will look at


https://contactus/retailgators/Let’s run this scraper with

scrapy crawl alibaba_crawler
DEBUG: Forbidden by robots.txt:

It is because Alibaba’s website has discovered to crawl different URLs array /trade. So, you can easily that by visiting robots.txt file, positioned at https://www.alibaba.com/robots.txt

Export Products data inCSV & JSON using Scrapy

The Scrapy offers in-built JSON & CSV formats for output.

scrapy crawl (spidername) -o output_filename.csv -t csv
scrapy crawl (spidername) -o output_filename.json -t json
CSV output:
JSON Output:
List of Data Fields

At RetailGators, we extract data for Alibaba Web Data Scraping Services. Data Fields are given below:

  • Name of Product
  • Product Price Range
  • Images of Product
  • Product Links
  • Minimum Product Order
  • Name of Seller
  • Seller Reply Rate
  • Number of sellers on Alibaba
Key Features of Alibaba Web Scraping Solutions

RetailGators help you to provide fully customized eCommerce Data Scraping that are accessible to deal with data requirements for big companies. Quality and Stability are one of the most important factors if data crawling is concerned. Many DIY Tools are available for scraping through in-house resources.

Here are some of the Key Advantages which is given below: -

  • Fully-Customized
  • Many Alternative Data Delivery
  • Fully manageable Solutions
  • High-Quality & Well-Structured Data
What we can scrape from Alibaba?

Website data can help the company to fill the intelligence gap in the association. Here are few things you can do with data scraping from Alibaba.

  • Price Comparison Data
  • Cataloging Data
  • Analyses

Why RetailGators?

If you are looking for the best Alibaba Web Data Scraping Services, then you can contact RetailGators for all your queries.