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In today's world of online shopping, everyone enjoys scoring the best deals on Amazon for their coveted electronic gadgets. Many of us maintain a wishlist of items we're eager to buy at the perfect price. With intense competition among e-commerce platforms, prices are constantly changing.
The savvy move here is to stay ahead by tracking price drops and seizing those discounted items promptly. Why rely on commercial Amazon price tracker software when you can create your solution for free? It is the perfect opportunity to put your programming skills to the test.
Our objective: develop a price tracking tool to monitor the products on your wishlist. You'll receive an SMS notification with the purchase link when a price drop occurs. Let's build your Amazon price tracker, a fundamental tool to satisfy your shopping needs.
An Amazon price tracker is a tool or program designed to monitor and track the prices of products listed on the Amazon online marketplace. Consumers commonly use it to keep tabs on price fluctuations for items they want to purchase. Here's how it typically works:
Amazon price trackers are valuable tools for savvy online shoppers who want to save money by capitalizing on price drops. They can help users stay updated on changing market conditions and make more cost-effective buying choices.
Let's break down the process we'll follow in this blog. We will create two Python web scrapers to help us track prices on Amazon and send price drop alerts.
Step 1: Building the Master File
Our first web scraper will collect product name, price, and URL data. We'll assemble this information into a master file.
Step 2: Regular Price Checking
We'll develop a second web scraper to check the prices and perform hourly checks periodically. This Python script will compare the current prices with the data in the master file.
Step 3: Detecting Price Drops
Since Amazon sellers often use automated pricing, we expect price fluctuations. Our script will specifically look for significant price drops, let's say more than a 10% decrease.
Step 4: Alert Mechanism
Our script will send you an SMS price alert if a substantial price drop is detected. It ensures you'll be informed when it's the perfect time to grab your desired product at a discounted rate.
Let's kick off the process of creating a Python-based Amazon web scraper. We focus on extracting specific attributes using Python's requests, BeautifulSoup, and the lxml parser, and later, we'll use the csv library for data storage.
Here are the attributes we're interested in scraping from Amazon:
Product Name
Sale Price (not the listing price)
To start, we'll import the necessary libraries:
These libraries will empower us to fetch web data, parse HTML content, and organize the collected information into a structured format for further handling.
In the realm of e-commerce web scraping, websites like Amazon often harbor a deep-seated aversion to automated data retrieval, employing formidable anti-scraping mechanisms that can swiftly detect and thwart web scrapers or bots. Amazon, in particular, has a robust system to identify and block such activities. Incorporating headers into our HTTP requests is an intelligent strategy to navigate this challenge.
Headers play a pivotal role in every HTTP request by furnishing critical metadata about the incoming requests to the target website. We've meticulously examined the headers using tools like Postman, allowing us to craft a customized header for our scraping endeavors. This header, as defined below, is instrumental in mimicking legitimate user interactions with the website, reducing the likelihood of being flagged as an e-commerce scraper:
Now, let's move on to assembling our bucket list. In my instance, we've curated a selection of five items that comprise my personal bucket list, and we've included them within the program as a list. If your bucket list is more extensive, storing it in a text file and subsequently reading and processing the data using Python is prudent.
For price tracking, a Python list suffices when dealing with a compact bucket list. However, if your list grows in size and complexity, employing a file to store and manage the data would be the optimal approach. We're primarily concerned with price monitoring and product names of these items on Amazon.
We will create two functions to extract Amazon pricing and product names that retrieve the price when called. For this task, we'll rely on Python's BeautifulSoup and lxml libraries, which enable us to parse the webpage and extract the e-commerce product data. To pinpoint the specific elements on the web page, we'll use Xpaths.
Here's a visual guide to the process: open Chrome Developer Tools and select the pricing section on the Amazon webpage. The pricing data is typically enclosed within a class labeled "a-offscreen" and resides inside a element. We'll craft Xpaths that precisely locate this data and then validate our Xpaths using Chrome Developer Tools to ensure accuracy and reliability in our web scraping process.
Our next step involves extracting the price data from Amazon and comparing it with the data stored in our master list to identify any price drops. Amazon data scraping services employ string manipulation techniques to format the data in the desired structure. This process will enable us to effectively monitor and detect any significant changes in the product prices, helping us catch potential discounts on our bucket list items.
To construct the master file containing our scraped data, we'll utilize Python's csv module. The code for this process is below.
Here are a few key points to keep in mind:
The master file consists of three columns: product name, price, and the product URL.
We iterate through each item on our bucket list, parsing the necessary information from their URLs.
To ensure responsible web scraping and reduce the risk of detection, we incorporate random time delays between each request.
Once you execute the code snippets mentioned above, you'll find a CSV file as "master_data.csv" generated. It's important to note that you can run this program once to create the master file.
To develop our Amazon price tracking tool, we already have the essential master data to facilitate comparisons with the latest scraped information. Now, let's craft the second script, which will extract data from Amazon and perform comparisons with the data stored in the master file.
In this tracker script, we'll introduce two additional libraries:
The Pandas library will be instrumental for data manipulation and analysis, enabling us to work with the extracted data efficiently.
The Twilio library: We'll utilize Twilio for SMS notifications, allowing us to receive price alerts on our mobile devices.
With these additional libraries in place, our tracking tool will be well-equipped to monitor Amazon prices and inform us of any noteworthy price changes.
Pandas: Pandas is a powerful open-source Python library for data analysis and manipulation. It's renowned for its versatile data structure, the pandas DataFrame, which facilitates the handling of tabular data, much like spreadsheets, within Python scripts. If you aspire to pursue a career in data science, learning Pandas is essential.
Twilio: Regarding programmatically sending SMS notifications, Twilio's APIs are a top choice. We opt for Twilio because it provides free credits, which suffice for our needs.
We will leverage many previously defined functions to kickstart the data extraction process. In addition, we'll introduce a new function that retrieves the corresponding price from the master file for the URLs we are scraping.
We are also setting up two lists to hold information about products that experience a price reduction. These lists will store the URLs and names of the products.
Commencing monitoring price fluctuations on Amazon, we will systematically inspect each page, retrieve the current price of products, compare with the master data file, and determine if there's a price variance exceeding 10%. If such a significant price alteration occurs, we will include the respective products in the previously established lists.
Without a price drop, the program should gracefully terminate, and there will be no need to trigger the Twilio API.
However, in the event of a price change, we must activate the Twilio API to dispatch a message. The first step is to establish the content for our SMS, which will serve as the message body.
The subsequent step involves registering for Twilio services and acquiring the Account SID and authentication Token.
To streamline the scraper and ensure it runs every hour, we aim to automate the process. Given my full-time job, manually initiating the program every two hours is impractical. We prefer to set up a schedule that triggers the program's execution hourly.
We can use the Python schedule library to achieve this automation. Below is a code snippet demonstrating how to implement this scheduling functionality.
To verify the program's functionality, manually adjust the price values within the master data file and execute the tracker program. You'll observe SMS notifications as a result of these modifications.
For further details, contact iWeb Data Scraping now! You can also reach us for all your web scraping service and mobile app data scraping needs.
