How to Extract Alibaba Product Data with Scrapy | Extract Alibaba Product Data
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/
PackagesInstall
pip3 install scrapyselectorlib
Find out more information by installing here -
https://doc.scrapy.org/en/latest/intro/
Creating Scrapy Projects
Let us create scrapy task using the command given below.
scrapystartprojectscrapy_alibaba
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
__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
Creating a Spider
The Scrapy has built a command named genspiderso that you can produce the fundamental spider templet.
scrapygenspider(spidername)(website)
Let’s produce our spider
scrapygenspideralibaba_crawleralibaba.com
This will help to create a file spider/scrapy_alibaba.py for recent templets for crawling Alibaba.com
This code is shown here:
importscrapy
classAlibabaCrawlerSpider(scrapy.Spider):
name = 'alibaba_crawler'
allowed_domains = ['alibaba.com']
start_urls = ['
http://alibaba.com/']
defparse(self, response):
pass
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.
keywords
headphones
earplugs
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/alibaba-scraper/blob/master/scrapy_alibaba/spiders/alibaba_crawler.py 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:
scrapycrawlalibaba_crawler-oalibaba.csv-tcsv
JSON Output:
scrapycrawlalibaba_crawler-oalibaba.csv-tjson
List of Data Fields
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: -
source code:
https://www.retailgators.com/how-to-extract-alibaba-products-data-with-scrapy.php How to Extract Alibaba Product Data with Scrapy | Extract Alibaba Product Data
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/
PackagesInstall
pip3 install scrapyselectorlib
Find out more information by installing here -
https://doc.scrapy.org/en/latest/intro/
Creating Scrapy Projects
Let us create scrapy task using the command given below.
scrapystartprojectscrapy_alibaba
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
__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
Creating a Spider
The Scrapy has built a command named genspiderso that you can produce the fundamental spider templet.
scrapygenspider(spidername)(website)
Let’s produce our spider
scrapygenspideralibaba_crawleralibaba.com
This will help to create a file spider/scrapy_alibaba.py for recent templets for crawling Alibaba.com
This code is shown here:
importscrapy
classAlibabaCrawlerSpider(scrapy.Spider):
name = 'alibaba_crawler'
allowed_domains = ['alibaba.com']
start_urls = ['http://alibaba.com/']
defparse(self, response):
pass
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.
keywords
headphones
earplugs
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/alibaba-scraper/blob/master/scrapy_alibaba/spiders/alibaba_crawler.py
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:
scrapycrawlalibaba_crawler-oalibaba.csv-tcsv
JSON Output:
scrapycrawlalibaba_crawler-oalibaba.csv-tjson
List of Data Fields
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: -
source code: https://www.retailgators.com/how-to-extract-alibaba-products-data-with-scrapy.php