How to Scrape Amazon Prices With Python – Towards Data …
With only a few lines of Python, you can build your own web scraping tool to monitor multiple stores so you never miss a great deal! Stay alert to those Amazon deals! (photo by Gary Bendig)I think it’s fair to assume that at one point, we all had a bookmarked product page from Amazon, which we refreshed frantically hoping for the price to go, maybe not frantically, but definitely several times a day. I will show you how to write a simple Python script that can scrape Amazon product pages from any of their stores and check the price, among other things. I make an effort to keep it simple, and if you already know the basic stuff you should be able to follow along smoothly. Here’s what we will do in this project:Create a csv file with the links for the products we want, as well as the price we are willing to buy themWrite a function with Beautiful Soup that will cycle through the links in the csv file and retrieve information about themStore everything in a “database” and keep track of product price over time so we can see the historical trendSchedule the script to run at specific times during the dayExtra — creating an email alert for when a price is lower than your limitI am also working on a script that takes search terms instead of the product links and returns the most relevant products from all stores. If you feel like that could be useful let me know in the comments and I’ll write another article about it! Update: I just created a video with the whole process! Check it out and let me know how much you saved:)The saddest thing about expanding your hobbies into something more professional is usually the need to purchase better equipment. Consider photography, for example. Purchasing a new camera is a decision that can impact your sleeping time dramatically. Buying a new lens is also known to have a similar effect— at least that is my experience! Not only are the technical aspects very relevant and require some research, but you also want — or hope — to get the best possible the research is done, it is time to search for the chosen model in every online store we ltiply the number of stores you know by the number of selected models and you get an approximate number of tabs open on my browser. Most of the time I end up visiting Amazon again… In my case, since I am in Europe, it usually involves searching Amazon stores from countries like Italy, France, UK, Spain, and Germany. I have found very different prices for the same products, but more importantly, there are occasional deals specific to each market. And we don’t want to miss out on that…This is where knowing a bit of Python can save you money. Literally! I started testing some web scraping to help me automate this task, and it turns out that the HTML structure of each store is pretty much the same. This way we can use our script in all of them to quickly have all the prices from all the you would like to have access to my other web scraping articles — and pretty much everything else on Medium — have you considered subscribing? You would be supporting my work tremendously! I have written a few articles about web scraping before where I explain how Beautiful Soup works. This python package is very easy to use and you can check this article I wrote to scrape prices from house listings. In a nutshell, Beautiful Soup is a tool that you can use to access specific tags from an HTML page. Even if you haven’t heard about it before, I bet you can understand what it is doing once you see the will need a file named with the links for the products you want to stalk. A template is provided in this Github repository. After each run, the scraper saves the results in a different file called “search_history_[whatever date]”. These are the files inside the search_history folder. You can also find it in the is the folder and file structure we needSince I do not want to overcomplicate things, I will use a Jupyter notebook to show you the code outputs first. But in the end, we will give this code some steroids and turn it into a nice function inside a file. To top it up we’ll create a scheduled task to run it from time to time! Soup is all we needLet’s start with the notebook view. Use these next snippets on your Jupyter notebook so you can see what each part of the code least known package here is probably glob. It’s a nice package that allows you to get things like a list of filenames inside a folder. Requests will take care of fetching the URLs we set up in the tracker list. Beautiful Soup is our web scraping tool for this challenge. If you need to install any of them, a simple pip/conda install will do. There are plenty of resources that can help you out, but usually, the Python Package Index page will have HEADERS variable is needed to pass along the get method. I also discuss it in my other articles mentioned above, but you can think of it as your ID card when visiting URLs with the requests package. It tells the server what kind of browser you are using. You can read more about this might be a good time to get that csv file called from the repository and place it in the folder “trackers” we run it through Beautiful Soup. This will tame the HTML into something more “accessible”, (cleverly) named Prince throwback to keep you entertained! Which ingredients should we pick? We start with simple things like the title of the product. Although this is almost redundant, it’s a nice thing to give some detail about the product once we check the excel file. I added the other fields like the review score/count and the availability, not because they are relevant to the purchase decision, but because I am a bit OCD with these things and I prefer to have variables that “can be useful someday”… It could be interesting to see the evolution of the review score for a particular product. Maybe not, but we’ll keep it anyway! Whenever you see, it means we are trying to find an element of the page using its HTML tag (like div, or span, etc. ) and/or attributes (name, id, class, etc. ). With, we are using CSS selectors. You can use the Inspect feature on your browser and navigate the page code, but I recently came across a very handy Chrome extension called SelectorGadget, and I highly recommend it. It makes finding the right codes way this point, if you find it difficult to follow how the selectors work, I encourage you to read this article, where I explain a bit more in you can see what each part returns. If you are starting out with web scraping and still don’t understand entirely how it works, I recommend you break the code into bits and slowly figure out what each variable is doing. I always write this disclaimer in web scraping articles, but there is a chance that if you read this article months from now, the code here may not be working correctly anymore — it may happen if Amazon changes the HTML structure of the page, for that happens, I would encourage you to fix it! Once you break up the code and use my examples it’s really no big deal. Just need to fix the selectors/tags your code is fetching. You can always leave a comment below and I’ll try to help you out. (De)constructing the soupGetting the title of our product should be a piece of cake. The price part is a bit more challenging and in the end, I had to add a few lines of code to get it right, but in this example, this part is will see later in the final version of the script, that I also added the functionality to get prices in USD, for my readers who shop from the US store! This led to an increase in try/except controls, to make sure I was getting the right field every time. When writing and testing a web scraper, we always face the same choice sooner or can waste 2 hours trying to get the exact piece of HTML that will get the right part of the page every time — without a guarantee that it’s even possible! Or we can simply improvise some error handling conditions, that will get us to a working tool faster. I don’t always do one or the other, but I did learn to comment a lot more when I’m writing code. That really helps to increase the quality of your code and even when you want to go back and restart working on a previous is the script in actionAs you can see, getting the individual ingredients is quite easy. After the testing is done, it is time to write a proper script that will:get the URLs from a csv fileuse a while loop to scrape each product and store the informationsave all the results, including previous searches in an excel fileTo write this you will need your favorite code editor (I use Spyder, which comes with the Anaconda installation — sidenote: version 4 is quite good) and create a new file. We’ll call it are a few additions to the bits and pieces we have seen above, but I hope the comments help to make it clear. This file is also in the acker ProductsA few notes about the file It’s a very simple file, with three columns (“url”, “code”, “buy_below”). This is where you will add product URLs that you want to can even place this file in some part of your synced Dropbox folder (update the script with the new file paths afterward), so you can update it anytime with your mobile. If you have the script set up to run on a server or on your own laptop at home, it would pick that new product link from the file in its next HistoryThe same with the SEARCH_HISTORY files. On the first run you need to add an empty file (find it in the repository) to the folder “search_history”. In line 116 from the script above, when defining the last_search variable we are trying to find the last file inside the search history folder. That is why you also need to map your own folder here. Just replace the text with the folder where you are running this project (“Amazon Scraper” in my example) AlertStill in the script above, line 97 you have a section that you can use to send an email with some kind of alert if the price is lower than your happens every time your price hits the reason why it is inside a try command is that not every time we get an actual price from the product page so the logical comparison would return an error — if the product was unavailable for instance. I did not want to overcomplicate the script too much so I left it out of the final code. However, you can have the code from a very similar project in this article. I left a print instruction with a buying alert in the script, so you just need to replace it with the email part. Consider it your homework! Dear Mac/Linux readers, this part will be solely about the scheduler in Windows, as it is the system I use. Sorry Mac/Linux users! I am 100% positive there are alternatives for those systems too. If you can let me know in the comments, I’ll add them tting up an automated task to execute our little script is far from difficult:1 — You start by opening “Task Scheduler” (simply press the windows key and type it). You then chose “Create Task” and pick the “Triggers” tab. I have mine to run every day at 10h00, and 19h30. 2 — Next you move to the actions tab. Here you will add an action and pick your Python folder location for the “Program/script” box. Mine is located in the Program Files directory as you can see in the image. 3 — In the arguments box, you want to type the name of our file with the function. 4 — And we are going to tell the system to start this command in the folder where our file here the task is ready to run. You can explore more options and make a test run to make sure it works. This is the basic way to schedule your scripts to run automatically with Windows Task Scheduler! I think we covered a lot of interesting features you can now use to explore other websites or to build something more complex. Thank you for reading and if you have any questions or suggestions, I try to reply to all messages! I might consider doing a video tutorial if I see some requests below, so let me know if you would like that to go along with the you want to see my other Web Scraping examples, here are two different projects:If you read this far, you probably realized already that I like photography, so as a token of my appreciation, I’ll leave you with one of my photos! Thank you for reading! As always, I welcome feedback and constructive criticism. If you’d like to get in touch, you can contact me here or simply reply to the article below.
How to Scrape Amazon Product Data: Names, Pricing, ASIN, etc.
Amazon offers numerous services on their ecommerce thing they do not offer though, is easy access to their product ’s currently no way to just export product data from Amazon to a spreadsheet for any business needs you might have. Either for competitor research, comparison shopping or to build an API for your app scraping easily solves this Amazon Web ScrapingWeb scraping will allow you to select the specific data you’d want from the Amazon website into a spreadsheet or JSON file. You could even make this an automated process that runs on a daily, weekly or monthly basis to continuously update your this project, we will use ParseHub a free and powerful web scraping that can work with any website. Make sure to download and install ParseHub for free before getting raping Amazon Product DataFor this example, we will scrape product data from ’s results page for “computer monitor”. We will extract information available both on the results page and information available on each of the product tting StartedFirst, make sure to download and install ParseHub. We will use this web scraper for this ParseHub, click on “New Project” and use the URL from Amazon’s result page. The page will now be rendered inside the raping Amazon Results PageOnce the site is rendered, click on the product name of the first result on the page. In this case, we will ignore the sponsored listings. The name you’ve clicked will become green to indicate that it’s been rest of the product names will be highlighted in yellow. Click on the second one on the list. Now all of the items will be highlighted in green.
On the left sidebar, rename your selection to product. You will notice that ParseHub is now extracting the product name and URL for each product.
On the left sidebar, click the PLUS(+) sign next to the product selection and choose the Relative Select command.
Using the Relative Select command, click on the first product name on the page and then on its listing price. You will see an arrow connect the two selections.
Expand the new command you’ve created and then delete the URL that is also being extracted by default.
Repeat steps 4 through 6 to also extract the product star rating, the number of reviews and product image. Make sure to rename your new selections Tip: The method above will only extract the image URL for each product. Want to download the actual image file from the site? Read our guide on how to scrape and download images with have now selected all the data we wanted to scrape from the results page. Your project should now look like this:Scraping Amazon Product PageNow, we will tell ParseHub to click on each of the products we’ve selected and extract additional data from each page. In this case, we will extract the product ASIN, Screen Size and Screen, on the left sidebar, click on the 3 dots next to the main_template your template to search_results_page. Templates help ParseHub keep different page layouts separate.
Now use the PLUS(+) button next to the product selection and choose the “Click” command. A pop-up will appear asking you if this link is a “next page” button. Click “No” and next to Create New Template input a new template name, in this case, we will use product_page.
ParseHub will now automatically create this new template and render the Amazon product page for the first product on the list.
Scroll down the “Product Information” part of the page and using the Select command, click on the first element of the list. In this case, it will be the Screen Size item.
Like we have done before, keep on selecting the items until they all turn green. Rename this selection to labels.
Expand the labels selection and remove the begin new entry in labels command.
Now click the PLUS(+) sign next to the labels selection and use the Conditional command. This will allow us to only pull some of the info from these items.
For our first Conditional command, we will use the following expression:
We will then use the PLUS(+) sign next to our conditional command to add a Relative Select command. We will now use this Relative Select command to first click on the Screen Size text and then on the actual measurement next to it (in this case, 21. 5 inches).
Now ParseHub will extract the product’s screen size into its own column. We can copy-paste the conditional command we just created to pull other information. Just make sure to edit the conditional expression. For example, the ASIN expression will be:$(“ASIN”)
Lastly, make sure that your conditional selections are aligned properly so they are not nested amongst themselves. You can drag and drop the selections to fix this. The final template should look like this:Want to scrape reviews as well? Check our guide on how to Scrape Amazon reviews using a free web, you might want to scrape several pages worth of data for this project. So far, we are only scraping page 1 of the search results. Let’s setup ParseHub to navigate to the next 10 results the left sidebar, return to the search_results_page template. You might also need to change the browser tab to the search results page as on the PLUS(+) sign next to the page selection and choose the Select command.
Then select the Next page link at the bottom of the Amazon page. Rename the selection to next_button.
By default, ParseHub will extract the text and URL from this link, so expand your new next_button selection and remove these 2 commands.
Now, click on the PLUS(+) sign of your next_button selection and use the Click command.
A pop-up will appear asking if this is a “Next” link. Click Yes and enter the number of pages you’d like to navigate to. In this case, we will scrape 9 additional pages. Running and Exporting your ProjectNow that we are done setting up the project, it’s time to run our scrape the left sidebar, click on the “Get Data” button and click on the “Run” button to run your scrape. For longer projects, we recommend doing a Test Run to verify that your data will be formatted the scrape job is completed, you will now be able to download all the information you’ve requested as a handy spreadsheet or as a JSON ThoughtsAnd that’s it! You are now ready to scrape Amazon data to your heart’s why stop there? With the skills you’ve just learned, you could scrape almost any other out our guides you may be interested in:How to scrape data from Yellow Pages How to scrape data from to use a data extraction tool to scrape AutoTraderScraping Rakuten dataBetter yet, become a certified Web Scraping expert with our free courses! Enroll for free today and get your certificates! Download ParseHub for freeThis post was originally published on August 29th, 2019 and last updated on November 9th, 2020.
How Scraping Amazon Data can help you price your products right – Datahut
Amazon is one of the largest e-commerce platforms across the globe. It has one of the largest customer bases and one of the most versatile and adaptive product portfolios. It definitely gets the advantage of a large amount of data and better operational processes in place due to its standing as one of the largest retailers. Having said that, even you can use Amazon’s data as an advantage to yourself to design a better product and price portfolio. A simple tool for the same is Web Scraping! Let us see why scraping Amazon data could be useful! Amazon has an exceptional pricing strategy that makes it an undisputed choice for its customers. You can extract information from Amazon either by using its APIs or scraping from it. Although Amazon has a Product Advertising API, it does not give you all the information you need. Thus, scraping data from Amazon might be useful. Let us look at how you can use this data to price your products rights. Learn more about your products If you are a manufacturer or a product supplier, Amazon can give you data about your products that you might not have as a standalone entity. Amazon places your products among a plethora of other similar and competing products. This can give you information about your products relative to the market and other competitors’ products. For instance, if you play in the electronics industry, you can learn about the sales, pricing, design and other characteristics of your product with respect to the other products in the same industry. You could get all this data in one place if you start scraping information from Amazon! Amazon’s product comparison table shows a very customized comparison for the type of product you are looking for. Customers use this information to learn about the competitors, their attributes including product prices and then make a decision of what product to buy. As a manufacturer or a supplier, you can use this information to design your products better or even to price your products differently. You can also learn who your direct competitors are, for a given product. Learn what your customer feels about your product Customers can give you a large amount of detailed information on your products. You can learn about their sentiments about your product and thus know more a fair deal about your products’ performance in the market. Amazon has incorporated a customers’ Questions and Answers section on the products page. This section lets customers ask basic questions about the products and people who have bought/used the product can answer the same. This section enables users to make an informed purchase decision. Additionally, there is a review section which is another rich source of information. Scraping information from Amazon could give you access to this as well! If you scrape information from Amazon on a product, it makes complete sense to also pull the review section as a few comments can give a detailed review of your product, the pros and cons, the general sentiment of the customers and also some basic information of the customer who is reviewing your product. If you can use a few text analytics techniques like Natural Language Processing or even keyword analysis on this text data, you can use the insights to make amends to the pricing strategy of your products among other things. Why should you scrape information for pricing from Amazon? Besides the fact that Amazon is one of the biggest sources of product pricing and competitor performance in your market, there are several other reasons to scrape data from Amazon for pricing. These advantages are more about the logistic convenience of scraping and the infrastructural advantage of the same. It automates the process and removes the manual dependency. You can now schedule the scraper script to run at your required frequency, mention the scope of scraping and it will collect the desired information for you. This also means more reliable data! You can set a well-structured pipeline for storing and using data that will serve as your data source for all pricing analytics and beyond. You do not need to store data in localized hardware. Instead, you can incorporate cloud storage for all the large data can get up-to-date information in almost no time! Since most of the process will now be automated, it should not take a lot of time to pull the required data. This means you can design your pricing strategy on the basis of regularly updated information and not outdated pricing analytics. All these advantages can also be clubbed with the point that pricing a product is not just comparative analysis but also includes other possible routes. For instance, one needs to look at the shelf-life of a product, age of a product in the market, general characteristics and even historical performance of a product given its price points. The price elasticity of a product determines the relationship of its demand at different price points in the past. If your organization has a data-based outlook and has the necessary infrastructure in place, you can club the insights from the scraped data with all the other analyses mentioned above to build a sound pricing framework. Scraping Information from Amazon While you could use your script to scrape data from Amazon, it could be a tough act given all the complications. The website discourages scraping in its policy and page-structure. In addition to that, the ever-evolving and complicated web page structure make scraping information from Amazon difficult. However, there are dedicated service providers that can scrape the necessary information from the required web site. We at Datahut have helped several enterprises scrape information from Amazon and other e-commerce websites and thus improve their pricing and marketing strategies. We take care of all assumptions, infrastructural and operational processes to design a transparent and simple process of extracting data from Amazon or other sites for you. Connect with Datahut for your data extraction needs. We love what we do, and extracting data to help you generate leads is one of our specialities.
#amazon #amazonscraping #pricingstrategy #webscrapingforamazon #pricecomparison
Frequently Asked Questions about how to scrape amazon prices
Can you scrape Amazon prices?
Amazon has an exceptional pricing strategy that makes it an undisputed choice for its customers. You can extract information from Amazon either by using its APIs or scraping from it.Dec 6, 2019
Does Amazon let you scrape?
Before we start discussing data extraction, you should know that Amazon does not encourage scraping its website. This is why the structure of the pages differs if the products fall into different categories.
How do I crawl data on Amazon?
One method for scraping data from Amazon is to crawl each keyword’s category or shelf list, then request the product page for each one before moving on to the next. This is best for smaller scale, less-repetitive scraping.Jun 28, 2021