Have you heard about amazon Data Scraping ?? It is a way to Scrape amazon Products data from amazon.com by Automated way using python.
amazon data scraper provides updated product information along with changing prices, reviews ,and more..
Screen shot of product page from we can extract data.
Inspecting element for data extractions from amazon.com
To find appropriate data from website first we have to inspecting and understanding html tag which is associated with given data ..
please follow below steps to finding tags
Here we have explained for finding price tag how can we find it , like this other tags can easily find…
How to set up your computer for web scraper development
We will use Python 3 for this tutorial. The code will not run if you are using Python 2.7. To start, you need a computer with Python 3 and PIP installed in it.
Let’s check your python version. Open a terminal ( in Linux and Mac OS ) or Command Prompt ( on Windows ) and type
python –version
and press enter. If the output looks something like Python 3.x.x, you have Python 3 installed. If it says Python 2.x.x you have Python 2. If it prints an error, you don’t probably have python installed.
If you don’t have Python 3, install it first.
Install Python 3 and Pip
Linux – https://www.python.org/downloads/source/
Mac Users can follow this guide – https://www.python.org/downloads/mac-osx/
Windows Users go here – https://www.python.org/downloads/windows/
For PIP installation visit this link – https://www.liquidweb.com/kb/install-pip-windows/
Install Packages
Python Code to Scrape amazon.com
import requests
from lxml import html
import requests.packages.urllib3.exceptions
import json
from urllib3.exceptions import InsecureRequestWarning
import urllib3
from lxml import etree
# below code send http get request to yellowpages.com
# return content in form of string
# lib Refernce
# 1 :- request
def getRequest(url):
headers = {‘Accept’: ‘text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8’,
‘Accept-Encoding’: ‘gzip, deflate, br’,
‘Accept-Language’: ‘en-GB,en;q=0.9,en-US;q=0.8,ml;q=0.7’,
‘User-Agent’: ‘Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.140 Safari/537.36’
}
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
response = requests.get(url, verify=False, headers=headers)
return response.text
# This method is use to parse data from string
# Return object with data
# lib Refrence
# 1 :- lxml
# 2 : json
def parseData(strHtml):
parser = html.fromstring(strHtml)
# Parse Data Using
URL = parser.xpath(‘//link[@rel=”canonical”]’)[0].attrib[‘href’]
ASIN = parser.xpath(‘//div[@id=”cerberus-data-metrics”]’)[0].attrib[‘data-asin’]
nodes = parser.xpath(‘//div[@class=”content”]/ul/li’)
for node in nodes:
if ‘UPC’ in ”.join(node.itertext()):
UPC = ”.join(node.itertext()).replace(‘UPC:’, ”).strip()
for node in nodes:
if ‘Item model number’ in ”.join(node.itertext()):
ItemModelNumber = ”.join(node.itertext()).replace(
‘Item model number:’, ”).strip()
for node in nodes:
if ‘Average Customer Review’ in ”.join(node.itertext()):
NoofReviews = ”.join(node.itertext()).replace(
‘Average Customer Review:’, ”).strip()
SalesrankFinal = ”.join(parser.xpath(‘//ul[@class=”zg_hrsr”]’)[0].itertext()).strip()
NoofRatings = parser.xpath(‘//span[@id =”acrPopover”]/span[1]/a/i[1]/span’)[0].text.strip()
productDescription = ”.join(parser.xpath(‘//div[@id=”productDescription”]’)[0].itertext()).strip()
for node in nodes:
if ‘Product Dimensions’ in ”.join(node.itertext()):
Productdimensions = ”.join(node.itertext()).replace(
‘Product Dimensions:’, ”).strip()
#Availability = parser.xpath(‘//span[@id=”availability”]’)[0].text.strip()
BSR = parser.xpath(‘//li[@id=”SalesRank”]/text()’)[1].strip()
for node in nodes:
if ‘Shipping Weight’ in ”.join(node.itertext()):
ShippingWeight = ”.join(node.itertext()).replace(‘Shipping Weight:’, ”).strip()
Category = parser.xpath(‘//span[@id=”productTitle”]’)[0].text.strip()
Price = parser.xpath(‘//span[@class=”a-color-price”]’)[0].text.strip()
product_title = parser.xpath(‘//span[@id=”productTitle”]’)[0].text.strip()
#Shippingcost = parser.xpath(‘//span[@id=”ourprice_shippingmessage”]/span’)[0].text.strip()
return {
‘URL’: URL,
‘ASIN’: ASIN,
‘UPC’: UPC,
‘Item Model Number’: ItemModelNumber,
‘No Of Reviews’: NoofReviews,
‘Sales Rank Final’: SalesrankFinal,
‘No Of Ratings’: NoofRatings,
‘product Description’: productDescription,
‘Product Dimensions’: Productdimensions,
#’Availability’: Availability,
‘Best Seller Rank’: BSR,
‘Shipping Weight’: ShippingWeight,
‘Category’: Category,
‘Price’: Price,
‘product_title’: product_title
#’Shippingcost’: Shippingcost
}
if __name__ == “__main__”:
print(‘Scraping Data from yellow Pages’)
url = ‘https://www.amazon.com/Natrol-5-HTP-Release-Tablets-200mg/dp/B001HCHGPC/’
print(‘Url :- ‘ + url)
strHtml = getRequest(url)
result = parseData(strHtml)
print(result)
Above code is developed for Python 3.X .. Run in any IDE like PyCharm , sublime text etc… We got here json file , we can also extract these data into sql database , export in CSV , Excel with modification in coding..
Here using lxml library data is extracted , you can do using beautifulsoup 4 also we can extract data from any website..
Run above code in any IDE of python and you will get result in JSON.. for test you can use another url from eBay..
Clarification :- This code available in this tutorial is only learning purpose . We are not responsible for how it is used and assume no liability for any detrimental usage of the source code. This code is only use for knowledge expansion regarding programming field.. by this tutorial we are not encourage eBay scraping or web scraping but will help to understand scraping.. also we are not responsible to provide any support for this code .. user can modify for learning purpose..
