How to Extract Amazon Prices and Product Data with Python 3?

Extract Amazon Prices and Product Data with Python3

Web data scraping assists in automating web scraping from the websites. In this blog, we will create an Amazon data scraper for scraping product prices and details. We will create this easy web scraper using SelectorLib and Python and run that in the console.

How to Scrape Amazon Product Data from Amazon Product Pages?

• Markup all data fields to be extracted using Selectorlib
• Then copy as well as run the given code

Setting up your Computer for Amazon Scraping

We will utilize Python 3 for the Amazon Data Scraper. This code won’t run in case, you use Python 2.7. You require a computer having Python 3 as well as PIP installed.

Follow the guide given to set up the computer as well as install packages in case, you are using Windows.

Packages for Installing Amazon Data Scraping

Python Requests for making requests as well as download HTML content from Amazon’s product pages

SelectorLib python packages to scrape data using a YAML file that we have created from webpages that we download

Using pip3,

pip3 install requests selectorlib

Scrape Product Data from Amazon Product Pages

An Amazon product pages scraper will extract the following data from product pages.

• Product Name
• Pricing
• Short Description
• Complete Product Description
• Ratings
• Images URLs
• Total Reviews
• Optional ASINs
• Link to Review Pages
• Sales Ranking

Markup Data Fields with Selectorlib

As we have marked up all the data already, you can skip the step in case you wish to have the rights to the data.

Let’s save it as the file named selectors.yml in the same directory with our code

name:

css: '#productTitle'

type: Text

price:

css: '#price_inside_buybox'

type: Text

short_description:

css: '#featurebullets_feature_div'

type: Text

images:

css: '.imgTagWrapper img'

type: Attribute

attribute: data-a-dynamic-image

rating:

css: span.arp-rating-out-of-text

type: Text

number_of_reviews:

css: 'a.a-link-normal h2'

type: Text

variants:

css: 'form.a-section li'

multiple: true

type: Text

children:

name:

css: ""

type: Attribute

attribute: title

asin:

css: ""

type: Attribute

attribute: data-defaultasin

product_description:

css: '#productDescription'

type: Text

sales_rank:

css: 'li#SalesRank'

type: Text

link_to_all_reviews:

css: 'div.card-padding a.a-link-emphasis'

type: Link

The markup will look like this:

Selectorlib is the combination of different tools for the developers, who make marking up as well as scraping data from pages easier. The Chrome Extension of Selectorlib helps you mark the data, which you require to scrape and create the XPaths or CSS Selectors required to scrape the data and previews about how that data will look like.

The Code

Make a folder named amazon-scraper as well as paste the selectorlib yaml template file like selectors.yml

Let’s make a file named amazon.py as well as paste the code given below in it. It includes:

• Read the listing of Amazon Product URLs from the file named urls.txt
• Extract the Data
• Save Data in the JSON Format

from selectorlib import extractor

import requests

import json

from time import sleep

# Create an Extractor by reading from the YAML file

e = Extractor.from_yaml_file('selectors.yml')

def scrape(url):

headers = {

'authority': 'www.amazon.com',

'pragma': 'no-cache',

'cache-control': 'no-cache',

'dnt': '1',

'upgrade-insecure-requests': '1',

'user-agent': 'Mozilla/5.0 (X11; CrOS x86_64 8172.45.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.64 Safari/537.36',

'accept':

'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',

'sec-fetch-site': 'none',

'sec-fetch-mode': 'navigate',

'sec-fetch-dest': 'document',

'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',

}

# Download the page using requests

print("Downloading %s"%url)

r = requests.get(url, headers=headers)

# Simple check to check if page was blocked (Usually 503)

if r.status_code > 500:

if "To discuss automated access to Amazon data please contact" in r.text:

print("Page %s was blocked by Amazon. Please try using better proxies\n"%url)

else:

print("Page %s must have been blocked by Amazon as the status code was %d"%(url,r.status_code))

return None

# Pass the HTML of the page and create

return e.extract(r.text)

# product_data = []

with open("urls.txt",'r') as urllist, open('output.jsonl','w') as outfile:

for url in urllist.readlines():

data = scrape(url)

if data:

json.dump(data,outfile)

outfile.write("\n")

# sleep(5)

Run the Amazon Product Pages Scraper

Get a complete code from the link Github –

https://www.3idatascraping.com/contact-us.php

You may start the scraper through typing this command:

python3 amazon.py

When scraping gets completed, then you can see the file named output.jsonl having the data. Let’s see the example of it:

https://www.amazon.com/HP-Computer-Quard-Core-Bluetooth-Accessories/dp/B085383P7M/

"name": "2020 HP 15.6\" Laptop Computer, 10th Gen Intel Quard-Core i7 1065G7 up to 3.9GHz, 16GB DDR4 RAM, 512GB PCIe SSD, 802.11ac WiFi, Bluetooth 4.2, Silver, Windows 10, YZAKKA USB External DVD + Accessories",

"price": "$959.00",

"short_description": "Powered by latest 10th Gen Intel Core i7-1065G7 Processor @ 1.30GHz (4 Cores, 8M Cache, up to 3.90 GHz); Ultra-low-voltage platform. Quad-core, eight-way processing provides maximum high-efficiency power to go.\n15.6\" diagonal HD SVA BrightView micro-edge WLED-backlit, 220 nits, 45% NTSC (1366 x 768) Display; Intel Iris Plus Graphics\n16GB 2666MHz DDR4 Memory for full-power multitasking; 512GB Solid State Drive (PCI-e), Save files fast and store more data. With massive amounts of storage and advanced communication power, PCI-e SSDs are great for major gaming applications, multiple servers, daily backups, and more.\nRealtek RTL8821CE 802.11b/g/n/ac (1x1) Wi-Fi and Bluetooth 4.2 Combo; 1 USB 3.1 Gen 1 Type-C (Data Transfer Only, 5 Gb/s signaling rate); 2 USB 3.1 Gen 1 Type-A (Data Transfer Only); 1 AC smart pin; 1 HDMI 1.4b; 1 headphone/microphone combo\nWindows 10 Home, 64-bit, English; Natural silver; YZAKKA USB External DVD drive + USB extension cord 6ft, HDMI cable 6ft and Mouse Pad\n› See more product details",

"images": "{\"https://images-na.ssl-images-amazon.com/images/I/61CBqERgZ7L._AC_SX425_.jpg\":[425,425],\"https://images-na.ssl-images-amazon.com/images/I/61CBqERgZ7L._AC_SX466_.jpg\":[466,466],\"https://images-na.ssl-images-amazon.com/images/I/61CBqERgZ7L._AC_SY355_.jpg\":[355,355],\"https://images-na.ssl-images-amazon.com/images/I/61CBqERgZ7L._AC_SX569_.jpg\":[569,569],\"https://images-na.ssl-images-amazon.com/images/I/61CBqERgZ7L._AC_SY450_.jpg\":[450,450],\"https://images-na.ssl-images-amazon.com/images/I/61CBqERgZ7L._AC_SX679_.jpg\":[679,679],\"https://images-na.ssl-images-amazon.com/images/I/61CBqERgZ7L._AC_SX522_.jpg\":[522,522]}",

"variants": [ { "name": "Click to select 4GB DDR4 RAM, 128GB PCIe SSD", "asin": "B01MCZ4LH1" }, { "name": "Click to select 8GB DDR4 RAM, 256GB PCIe SSD", "asin": "B08537NR9D" }, { "name": "Click to select 12GB DDR4 RAM, 512GB PCIe SSD", "asin": "B08537ZDYH" }, { "name": "Click to select 16GB DDR4 RAM, 512GB PCIe SSD", "asin": "B085383P7M" }, { "name": "Click to select 20GB DDR4 RAM, 1TB PCIe SSD", "asin": "B08537NDVZ" } ],

"product_description": "Capacity:16GB DDR4 RAM, 512GB PCIe SSD\n\nProcessor\n\n Intel Core i7-1065G7 (1.3 GHz base frequency, up to 3.9 GHz with Intel Turbo Boost Technology, 8 MB cache, 4 cores)\n\nChipset\n\n Intel Integrated SoC\n\nMemory\n\n 16GB DDR4-2666 SDRAM\n\nVideo graphics\n\n Intel Iris Plus Graphics\n\nHard drive\n\n 512GB PCIe NVMe M.2 SSD\n\nDisplay\n\n 15.6\" diagonal HD SVA BrightView micro-edge WLED-backlit, 220 nits, 45% NTSC (1366 x 768)\n\nWireless connectivity\n\n Realtek RTL8821CE 802.11b/g/n/ac (1x1) Wi-Fi and Bluetooth 4.2 Combo\n\nExpansion slots\n\n 1 multi-format SD media card reader\n\nExternal ports\n\n 1 USB 3.1 Gen 1 Type-C (Data Transfer Only, 5 Gb/s signaling rate); 2 USB 3.1 Gen 1 Type-A (Data Transfer Only); 1 AC smart pin; 1 HDMI 1.4b; 1 headphone/microphone combo\n\nMinimum dimensions (W x D x H)\n\n 9.53 x 14.11 x 0.70 in\n\nWeight\n\n 3.75 lbs\n\nPower supply type\n\n 45 W Smart AC power adapter\n\nBattery type\n\n 3-cell, 41 Wh Li-ion\n\nBattery life mixed usage\n\n Up to 11 hours and 30 minutes\n\n Video Playback Battery life\n\n Up to 10 hours\n\nWebcam\n\n HP TrueVision HD Camera with integrated dual array digital microphone\n\nAudio features\n\n Dual speakers\n\nOperating system\n\n Windows 10 Home 64\n\nAccessories\n\n YZAKKA USB External DVD drive + USB extension cord 6ft, HDMI cable 6ft and Mouse Pad",

"link_to_all_reviews": "https://www.amazon.com/HP-Computer-Quard-Core-Bluetooth-Accessories/product-reviews/B085383P7M/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews"}

Scraping Amazon Products from Search Results Pages

The Amazon search results pages scraper will extract the following data from different search result pages:

• Product’s Name
• Pricing
• URL
• Ratings
• Total Reviews

The code and steps for extracting the search results are similar to a product pages scraper.

Markup Data Fields with Selectorlib

Here is a selectorlib yml file. Let’s calls that search_results.yml

products:

css: 'div[data-component-type="s-search-result"]'

xpath: null

multiple: true

type: Text

children:

title:

css: 'h2 a.a-link-normal.a-text-normal'

xpath: null

type: Text

url:

css: 'h2 a.a-link-normal.a-text-normal'

xpath: null

type: Link

rating:

css: 'div.a-row.a-size-small span:nth-of-type(1)'

xpath: null

type: Attribute

attribute: aria-label

reviews:

css: 'div.a-row.a-size-small span:nth-of-type(2)'

xpath: null

type: Attribute

price:

css: 'span.a-price:nth-of-type(1) span.a-offscreen'

xpath: null

type: Text

The Code

This code is nearly matching to the earlier scraper, excluding that we repeat through every product as well as save them like a separate line.

Let’s make a file searchresults.py as well as paste the code given in it. This is what a code does:

• Open the file named search_results_urls.txt as well as read the search results pages URLs
• Extract the data
• Then save to the JSON Line files named search_results_output.jsonl

from selectorlib import Extractor

import requests

import json

from time import sleep

# Create an Extractor by reading from the YAML file

e = Extractor.from_yaml_file('search_results.yml')

def scrape(url):

headers = {

'dnt': '1',

'upgrade-insecure-requests': '1',

'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.61 Safari/537.36',

'accept':

'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',

'sec-fetch-site': 'same-origin',

'sec-fetch-mode': 'navigate',

'sec-fetch-user': '?1',

'sec-fetch-dest': 'document',

'referer': 'https://www.amazon.com/',

'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',}

# Download the page using requests print("Downloading %s"%url)

r = requests.get(url, headers=headers)

# Simple check to check if page was blocked (Usually 503) if r.status_code > 500:

if "To discuss automated access to Amazon data please contact" in r.text:

print("Page %s was blocked by Amazon. Please try using better proxies\n"%url)

else:

print("Page %s must have been blocked by Amazon as the status code was %d"%(url,r.status_code))

return None

# Pass the HTML of the page and create

return e.extract(r.text)

# product_data = []

with open("search_results_urls.txt",'r') as urllist,

open('search_results_output.jsonl','w') as outfile:

for url in urllist.read().splitlines():

data = scrape(url)

if data:

for product in data['products']:

product['search_url'] = url

print("Saving Product: %s"%product['title'])

json.dump(product,outfile)

outfile.write("\n")

# sleep(5)

Run an Amazon Scraper for Scraping Search Results

You can begin your scraper through typing this command:

python3 searchresults.py

When the scraping is completed, you need to see the file named search_results_output.jsonl with the data.

An example of it is:

https://www.amazon.com/s?k=laptops

https://www.3idatascraping.com/contact-us.php

What Should You Do If You are Blocked When Scraping Amazon?

Amazon may consider you as the “BOT” in case, you start extracting hundreds of pages by the code given here. The thing is to avoid having flagged as a BOT while extracting as well as running the problems. How to cope with such challenges?

Imitate the human behavior to the maximum

As there is no assurance that you won’t get blocked. Just follow these tips about how to evade being blocked by Amazon:

Use Proxies as well as Switch Them

Let us assume that we are extracting thousands of products on Amazon.com using a laptop that normally has only a single IP address. Amazon would assume us as a bot because NO HUMAN visits thousands of product pages within minutes. To look like the human – make some requests to Amazon using the pool of proxies or IP Addresses. The key rule is to have only 1 IP address or proxy making not over 5 requests for Amazon in one minute. In case, you scrape around 100 pages for every minute, then we need around 100/5 = 20 Proxies.

Specify User Agents of the Newest Browsers as well as Switch Them

If you observe the code given, you would get a line in which we had set the User-Agent String for requests we are doing.

'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.90 Safari/537.36'

Like proxies, it’s good to get the pool of different User Agent Strings. So, ensure that you use user-agent strings for the popular and latest browsers as well as rotate these strings for every request you do to Amazon. It is a good idea of creating a grouping of (IP Address, User-Agent) so it looks more human than the bot.

Decrease the Total ASINs Extracted Every Minute

You can also try to slow down the scrapping a bit forgiving Amazon lesser chances of considering you like the bot. However, around 5 requests for every IP per minute isn’t throttling much. If you want to go quicker, add additional proxies. You can adjust the speed by decreasing or increasing the delay within the sleep functions.

Continue Retrying

Whenever you get blocked by Amazon, ensure you retry the request. If you are looking at a code block given we have included 20 retries. Our codes retry immediately after scraping fails, you can do a better job by making the retry queues using the list, as well as retry them when all the products get scraped from Amazon.

If you are looking to get Amazon product data and prices scraping using Python 3 then contact 3i Data Scraping!

• Source Article: https://www.3idatascraping.com/how-to-extract-amazon-prices-and-product-data-with-python-3.php