web scraping

10 min read

How to monitor price difference across multiple sellers on Amazon

ScrapeHero Content
Last Updated: December 10, 2024

Why scrape prices from Offer Listing Page?
What data are we extracting?
Building the Scraper
The Code
Running the Code
Known Limitations

This tutorial shows you how to scrape the seller information and prices from Amazon’s Offer Listing page.

The price that you usually see for a product on Amazon is the buy box price. But how is it determined? Few of the biggest factors include fulfillment method, price, and seller rating. Usually, the sellers who use FBA ( Fulfilled by Amazon ) for their products “win” their way to the buy box. If there are multiple sellers who use FBA, the buy box pricing is usually shared or rotated.

Amazon has been seeing a dramatic increase in the number of third-party sellers who partner with them to make up the Amazon Marketplace. Ebay is at number two now and has only 52% merchants as compared to Amazon.

Why scrape prices from Offer Listing Page?

Understand your share standings and feasibility of approaching the buying box
To effectively monitor competition, gain insight about seller prices and product movement.
Analyze distribution channel, track how your products are being sold and detect any harm to your brand
Learn more about customer behavior so as to align products and pricing towards maximum profit

What data are we extracting?

Here is a list of the product details are going to extract:

Seller Name
Price
Product Conditions
Delivery Options
Product Shipping
Seller Rating
Seller’s Positive Percentage

Below is an annotated screenshot of some of the data fields we will be extracting:

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Building the Scraper

You need a computer with Python 3 and PIP installed in it. The code will not run on Python 2.7.

Most UNIX operating systems like Linux and Mac OS comes with Python pre-installed. But, not all the Linux Operating Systems ship with Python 3 by default.

Let’s check your python version. Open a terminal (in Linux and Mac OS) or Command Prompt (on Windows) and type

python -V

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

Here is a guide to install Python 3 in Linux – http://docs.python-guide.org/en/latest/starting/install3/linux/

Mac Users can follow this guide – http://docs.python-guide.org/en/latest/starting/install3/osx/

Windows Users – https://www.scrapehero.com/how-to-install-python3-in-windows-10/

Install Packages

Python Requests, to make requests and download the HTML content of the pages (http://docs.python-requests.org/en/master/user/install/).
Python LXML, for parsing the HTML Tree Structure using Xpaths (Learn how to install that here – http://lxml.de/installation.html)
UnicodeCSV for handling Unicode characters in the output file. Install it using pip install unicodecsv.
IPython, to check our Xpaths. You can install it using pip install ipython

Constructing the URL

Identify the product ASIN. In this product – Pockit Lightweight Stroller (https://www.amazon.com/GB-616230013-Pockit-Lightweight-Stroller/dp/B01DQ2B8UY/ref=olp_product_details?_encoding=UTF8&me=), the ASIN is B01DQ2B8UY
Construct the URL to get the information of other sellers. Open your browser again and go to the Offer Listing page of a product on Amazon.com. The URL of the Offer Listings of a product looks like this: https://www.amazon.com/gp/offer-listing/B01DQ2B8UY/

To find the offer listings of a product you just need to replace the ASIN at the end of the link. This is the URL when you don’t apply any filters. Now, Click on the checkbox for prime eligibility.

The URL changes to this:

https://www.amazon.com/gp/offer-listing/B01DQ2B8UY/ref=olp_f_primeEligible?ie=UTF8&f_all=true&f_primeEligible=true

Note

For constructing the URL based on a Filter:

To get the list of products that only have prime eligibility, keep the parameter as rel=f_primeEligible=true:

https://www.amazon.com/gp/offer-listing/B01DQ2B8UY/f_primeEligible=true

Initially, there were ten products for this product but under the Prime Eligibility filter, there is only four.

Next step is building a scraper that extracts the data from each offer listing – Price, Condition, Delivery, and Seller information according to each filter applied.

Applying Filters:

Each argument is passed through a command line and is obtained and stored. We need arguments for ASIN, product condition, and shipping. The GIF below shows how to get the URL based on the filter applied:

For each filter applied, we have to create a URL. We are going to create a filter to URL mapping.

    condition_dict = {'new':'&f_new=true',
                    'used': '&f_used=true',
                    'all': '&condition=all',
                    'like_new':'&f_usedLikeNew=true',
                    'good':'&f_usedGood=true',
                    'verygood':'&f_usedVeryGood=true',
                    'acceptable':'&f_usedAcceptable=true'
    }
    shipping_dict = {'prime': '&f_primeEligible=true',
                    'all':'&shipping=all'
    }

    url = 'https://www.amazon.com/gp/offer-listing/'+asin+'/ref='+condition_dict.get(condition)+shipping_dict.get(shipping)
    data = parse_offer_details(url)

parseDetails()

The URL constructed will contain the ASIN, product condition and shipping which will look like this:

https://www.amazon.com/gp/offer-listing/B01DQ2B8UY/ref=&f_new=true&f_primeEligible=true

Finding the XPath

XPaths are used to tell the script where each field we need is present in the HTML. An XPath tells you the location of an element, just like a catalog card does for books. We’ll find XPaths for each of the fields we need and put that into our scraper. After we extract the information, we’ll save it into a CSV file.

Learn More: XPaths and their relevance in Web Scraping

Now let’s check our XPaths using IPython:

In [1]: import requests

ln [2]: url="https://www.amazon.com/gp/offer-listing/B00BD741WM"

ln [3]: from lxml import html

In [4]: response = requests.get("https://www.amazon.com/gp/offer-listing/B01DQ2B8UY/")

In [5]: response
Out[5]: Response [200];

The request has succeeded.

In [6]: parser = html.fromstring(response.text)

In [7]: parser
Out[7]: <Element html at 0x1a48a2fd138>

Let’s get the XPath of each listing:

The GIF shows where we’ll find the data present in the HTML.

In [8]: parser.xpath("//div[contains(@class, 'a-row a-spacing-mini olpOffer')]") 
Out[8]: 
 [<Element div at 0x7fea59c10c00>,
 <Element div at 0x7fea59bea470>,
 <Element div at 0x7fea59bea4c8>,
 <Element div at 0x7fea59bea6d8>,
 <Element div at 0x7fea59bea730>,
 <Element div at 0x7fea59bea788>,
 <Element div at 0x7fea59bea7e0>,
 <Element div at 0x7fea59bea838>,
 <Element div at 0x7fea59bea890>,
 <Element div at 0x7fea59bea8e8>]

So now we know there are 10 seller listings for this product. Let’s check the price for the first listing:

In [9] listing[0].xpath(".//span[contains(@class, 'olpOfferPrice')]//text()") 
Out [9] ['$152.21']

Getting the Required Data:

Now we can check for a couple of data fields that we need in our scraper to see if the data we need is correct:

Prime Eligibility:

In [10]: listing[0].xpath(".//i/@aria-label")
Out[10]: ['Amazon Prime TM']

Conditions:

In [11]: listing[0].xpath(".//span[contains(@class, 'olpCondition')]//text()")
Out[11]: ['\n\n New\n ']

Shipping:

In [12]: listing[0].xpath(".//span[contains(@class, 'a-color-secondary')]//text()") 
Out[12]: ['\n\n\n\n\n & ',
'FREE Shipping',
' ',
'Details',
'\n\n ']

Delivery:

In [13]: listing[0].xpath(".//div[contains(@class, 'olpDeliveryColumn')]//text()") 
Out[13]: ['\n \n\n\n\n\n\n\n\n\n\n\n\n\n ', '\n \n\n\n\n\n \n\n\n \n', '\n\n\n\n\n\n\n\n ', '\n ', 'Shipping rates', '\n and ', 'return policy', '.\n ', '\n', '\n\n\n\n\n \n\n\n

Seller Name:

In [14]: listing[0].xpath(".//h3[contains(@class, 'olpSellerName')]//img//@alt") 
Out[14]: ['Amazon.com']

Iterating the list to get all the seller data:

for listing in listings:

  XPATH_PRODUCT_PRICE = ".//span[contains(@class, 'olpOfferPrice')]//text()"
  XPATH_PRODUCT_PRIME = ".//i/@aria-label"
  XPATH_PRODUCT_SHIPPING = ".//p[contains(@class, 'olpShippingInfo')]//text()"
  XPATH_PRODUCT_CONDITION = ".//span[contains(@class, 'olpCondition')]//text()"
  XPATH_PRODUCT_DELIVERY = ".//div[contains(@class, 'olpDeliveryColumn')]//text()"
  XPATH_PRODUCT_SELLER1 = ".//h3[contains(@class, 'olpSellerName')]//a/text()"
  XPATH_PRODUCT_SELLER2 = ".//h3[contains(@class, 'olpSellerName')]//img//@alt"
  XPATH_PRODUCT_SELLER_RATTING = ".//div[contains(@class, 'olpSellerColumn')]//span[contains(@class, 'a-icon-alt')]//text()"
  XPATH_PRODUCT_SELLER_PERCENTAGE = ".//div[contains(@class, 'olpSellerColumn')]//b/text()"
  XPATH_PRODUCT_SELLER_URL = ".//h3[contains(@class, 'olpSellerName')]//a/@href"

  product_price = listing.xpath(XPATH_PRODUCT_PRICE)
  product_price = product_price[0].strip()
  product_prime = listing.xpath(XPATH_PRODUCT_PRIME)
  product_condition = listing.xpath(XPATH_PRODUCT_CONDITION)
  product_shipping = listing.xpath(XPATH_PRODUCT_SHIPPING)
  delivery = listing.xpath(XPATH_PRODUCT_DELIVERY)
  seller1 = listing.xpath(XPATH_PRODUCT_SELLER1)
  seller2 = listing.xpath(XPATH_PRODUCT_SELLER2)
  seller_ratting =listing.xpath(XPATH_PRODUCT_SELLER_RATTING)
  seller_percentage = listing.xpath(XPATH_PRODUCT_SELLER_PERCENTAGE)
  seller_url = listing.xpath(XPATH_PRODUCT_SELLER_URL)

The Code

import requests
from lxml import html
from lxml.etree import ParserError
import json
from time import sleep
import argparse
import unicodecsv as csv
import traceback


def parse_offer_details(url):
    '''
    Function to parse seller details from amazon offer listing page
    eg:https://www.amazon.com/gp/offer-listing/
    :param url:offer listing url
    :rtype: seller details as json
    '''
    # Add some recent user agent to prevent blocking from amazon
    headers = {
                'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36'
    }

    for retry in range(5):
        try:
            print("Downloading and processing page :", url)
            response = requests.get(url, headers=headers)
            if response.status_code == 403:
                raise ValueError("Captcha found. Retrying")

            response_text = response.text
            parser = html.fromstring(response_text)
            XPATH_PRODUCT_LISTINGS = "//div[contains(@class, 'a-row a-spacing-mini olpOffer')]"
            # Parsing seller list
            listings = parser.xpath(XPATH_PRODUCT_LISTINGS)
            offer_list = []

            if not listings:
                print("no sellers found")
                return offer_list

            # parsing individual seller
            for listing in listings:
                XPATH_PRODUCT_PRICE = ".//span[contains(@class, 'olpOfferPrice')]//text()"
                XPATH_PRODUCT_PRIME = ".//i/@aria-label"
                XPATH_PRODUCT_SHIPPING = ".//p[contains(@class, 'olpShippingInfo')]//text()"
                XPATH_PRODUCT_CONDITION = ".//span[contains(@class, 'olpCondition')]//text()"
                XPATH_PRODUCT_DELIVERY = ".//div[contains(@class, 'olpDeliveryColumn')]//text()"
                XPATH_PRODUCT_SELLER1 = ".//h3[contains(@class, 'olpSellerName')]//a/text()"
                XPATH_PRODUCT_SELLER2 = ".//h3[contains(@class, 'olpSellerName')]//img//@alt"

                product_price = listing.xpath(XPATH_PRODUCT_PRICE)
                product_price = product_price[0].strip()
                product_prime = listing.xpath(XPATH_PRODUCT_PRIME)
                product_condition = listing.xpath(XPATH_PRODUCT_CONDITION)
                product_shipping = listing.xpath(XPATH_PRODUCT_SHIPPING)
                delivery = listing.xpath(XPATH_PRODUCT_DELIVERY)
                seller1 = listing.xpath(XPATH_PRODUCT_SELLER1)
                seller2 = listing.xpath(XPATH_PRODUCT_SELLER2)
                
                # cleaning parsed data
                product_prime = product_prime[0].strip() if product_prime else None
                product_condition = ''.join(''.join(product_condition).split()) if product_condition else None
                product_shipping_details = ' '.join(''.join(product_shipping).split()).lstrip("&").rstrip("Details") if product_shipping else None
                cleaned_delivery = ' '.join(''.join(delivery).split()).replace("Shipping rates and return policy.", "").strip() if delivery else None
                product_seller = ''.join(seller1).strip() if seller1 else ''.join(seller2).strip()

                offer_details = {
                                'price': product_price,
                                'shipping_detais': product_shipping_details,
                                'condition': product_condition,
                                'prime': product_prime,
                                'delivery': cleaned_delivery,
                                'seller': product_seller,
                                'asin': asin,
                                'url': url
                }
                offer_list.append(offer_details)
            return offer_list

        except ParserError:
            print("empty page found")
            break
        except:
            print(traceback.format_exc())
            print("retying :", url)
        
if __name__ == '__main__':
    # defining arguments   
    parser = argparse.ArgumentParser()
    parser.add_argument('asin', help='unique product id, eg "B01DQ2B8UY"')
    parser.add_argument('condition', help='product condition eg "new", "used", "all", "like_new", "verygood", "acceptable", "good"', default="all")
    parser.add_argument('shipping', help='product shipping eg "prime", "all"', default="all")
    args = parser.parse_args()
    asin = args.asin
    condition = args.condition
    shipping = args.shipping

    # for creating url according to the filter applied
    condition_dict = {'new': '&f_new=true',
                    'used': '&f_used=true',
                    'all': '&condition=all',
                    'like_new': '&f_usedLikeNew=true',
                    'good': '&f_usedGood=true',
                    'verygood': '&f_usedVeryGood=true',
                    'acceptable': '&f_usedAcceptable=true'
    }
    shipping_dict = {'prime': '&f_primeEligible=true',
                    'all': '&shipping=all'
    }

    url = 'https://www.amazon.com/gp/offer-listing/'+asin+'/ref='+condition_dict.get(condition)+shipping_dict.get(shipping)
    data = parse_offer_details(url)
    
    if data:
        print ('Writing results to  the file: ', asin, '-sellers.csv')
        with open(asin+'-sellers.csv', 'wb')as csvfile:
            fieldnames = ['seller', 'price', 'prime', 'condition', 'shipping_detais', 'delivery', 'url', 'asin']
            writer = csv.DictWriter(csvfile, fieldnames=fieldnames, quoting=csv.QUOTE_ALL)
            writer.writeheader()
            for row in  data:
                writer.writerow(row)

Read More – How to Monitor Competitor PPC data on Amazon

Running the Code

Assume the script is named amazon_seller.py. If you type in the script name in command prompt or terminal along with a -h

python amazon_seller.py -h

usage: amazon_seller-final.py [-h] asin condition shipping

positional arguments:
asin unique product id ,eg "B01DQ2B8UY"
condition product condition eg
"new","used","all","like_new","verygood","acceptable","good"
shipping product shipping eg "prime", "all"

optional arguments:
-h, --help show this help message and exit

Here is are some examples for extracting the seller listings with the ASIN ‘B01DQ2B8UY’:

python amazon_seller.py B01DQ2B8UY “all” “all”

To find the seller listings under the condition ‘prime’ and ‘new:

python amazon_seller.py B01DQ2B8UY "new" "prime"

You can download the code at https://github.com/scrapehero/amazon-seller-list

Let us know in the comments below how this scraper worked for you.

If you want to learn how to scrape product pages and reviews on Amazon you can check out our blog posts:

Learn More:

Known Limitations

This code should work for extracting details from the first page of the results. If you want to scrape the details of thousands of pages you should read Scalable do-it-yourself scraping – How to build and run scrapers on a large scale.

If you need professional help with scraping complex websites, contact us by filling up the form below:

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Please DO NOT contact us for any help with our Tutorials and Code using this form or by calling us, instead please add a comment to the bottom of the tutorial page for help

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