How to scrape Instamart grocery product data

Introduction

Instamart is an e-commerce service offering delivery of essentials and groceries in just 10 minutes or less. It is the most convenient way to order urgently needed items. Customers with the membership of Instamart can get free delivery, and get other benefits when purchasing groceries or food items. In this emerging quick commerce epoch, where time matters a lot, staying ahead in the dynamic market landscape is essential. Researchers and startups can use the official Swiggy Instamart API or develop a scraper to extract product details from it to empower customer satisfaction and increase business sales.

In today’s comprehensive blog post, we will follow a step-by-step approach to scrape Instamart grocery data.

What is The Importance of Scraping Instamart Product Data?

Before we dive into technical details, it is important to know the importance of scraping Instamart product details. The following are some reasons that you must know:

Price Monitoring:

For retailers, understanding every aspect of a competitor is crucial. Retailers can use product details in order to track the product pricing, offers, and discounts provided by Instamart. By knowing this information, they can develop their own pricing strategies.

Competitor Intelligence:

Retailers can utilize Instamart product details to know what their competitors are offering, their position, and their move on new opportunities and threats. Competitor Intelligence helps businesses to understand their competitive environment and the challenges it presents. It offers information that affects the competitive advantage of the business.

Market Research:

Scraping Instamart data is highly beneficial to market researchers. By using product descriptions and stock availability, they have a great chance to understand both customer preferences and seasonal trends. Professional market researchers can develop a highly effective model to predict customer demand based on the availability of the product. 

Product Catalog Enrichment:

Organizations can scrape Instamart for collecting product images, descriptions, prices, and features to manage their online store or inventory. They can also use this data to dynamically update their product price and offers.

Supplier and Vendor Coordination:

Instamart can be a great online platform to gather catalogue details related to product brand and size. This level of information is highly utilized by suppliers, retailers, and researchers. 

Various Techniques to Extract Instamart Data

There are many different techniques to scrape Instamart product details. Here are some of them:

Web Scraping

Web Scraping is the most common technique for extracting product data from Instamart. This technique uses Python to extract the needed data from a website. You can develop your own customized web scraper to extract an immense amount of data. Web scraping highly requires technical expertise to beat security challenges.

APIs

API, an acronym of Application Programming Interface, is a protocol used to communicate between two computer programs. Many websites offer APIs to access the structured data of the website without developing a custom scraper. It is a safer and easier way to extract data from a website. The downside is that you can access limited data, and you may need API keys for this purpose.

Third-Party Data Providers

Some organizations provide services that enable you to collect product data from the website of your choice. Here, you have to subscribe to a data provider to gather data through an analytical dashboard. This is an easy-to-use and time-saving method. The con of using a third-party data provider service is that it is costly.

Optical Character Recognition

This is a general technique to get data from a screenshot of product details. OCR (Optical Character Recognition) tools on the text images and convert them into an editable, readable format. This is a simple and easy way to get data, but it is suitable for non-text-based sources. In case the screenshot quality is low, the data will be inaccurate.  

List of Various Data Fields

Store/Grocer Name

Product SKU

Product Name

Product Specifications

Address

Product Description

Geocoordinates

Product Price

Customer Reviews/Ratings

Best Offers

Product Image

Product Category

Discounted Price

Services Available

Customer Reviews

Brand Name

Customer Ratings

Product Availability

Steps to Extract Swiggy Instamart Product Details

If you are a developer, then you have a basic idea of various programming languages like Java, Python, PHP, JavaScript, Ruby, etc. All these languages can be leveraged to extract Instramart product data. However, in this blog post, we will give priority to Python for fulfilling our needs because it is a beginner-friendly programming language. Anybody with little technical knowledge can effortlessly write code in Python. It offers important libraries such as Selenium Beautifulsoup. These libraries offer a better way to parse static HTML and interact with dynamic website content. Ready to write PHP code and extract Instamart Product Details? Let’s go ahead step by step.

Step 1: Install Libraries:

Install two libraries 1) selenium and 2) beautifulsoup4. This can be done by entering the following command in the terminal.

pip install beautifulsoup4 selenium requests

Code Reference: ChatGpt

Step 2: Download WebDriver:

Once you have installed beautifulsoup4 and selenium, you have to download WebDriver. If you are using the Chrome browser, then you need to download ChromeDriver; For Firefox, download GeckoDriver.

Step 3: Import Libraries:

You have to use Selenium to interact with the HTML page and BeautifulSoup to parse product details.

from selenium import webdriver

from selenium.webdriver.common.by import By

from selenium.webdriver.common.keys import Keys

from bs4 import BeautifulSoup

import time

Code Reference: ChatGpt

Step 4: Setup WebDriver

Once you have imported libraries, you have to initialize the browser with Selenium and load the page. We are using the Chrome browser as an example. Write the following code:

driver_path = ‘/path/to/chromedriver’  # Modify this to the location of your ChromeDriver

driver = webdriver.Chrome(executable_path=driver_path)

url = ‘https://www.instamart.com/products’  # Replace with the actual URL

driver.get(url)

Code Reference: ChatGpt

Step 5: Wait for Page Load:

Because the Swiggy Instamart site has dynamic content, you need to rely on JavaScript time.sleep() method to load the needed product details.

time.sleep(5) 

Code Reference: ChatGpt

Step 6: Scroll (This is an optional step)

This step is applicable if the product listing is long. In this case, you have to use Selenium to scroll down and load more products.

driver.execute_script(“window.scrollTo(0, document.body.scrollHeight);”)

time.sleep(3)

Code Reference: ChatGpt

Step 7: Extract Page Source

When the page is fully loaded, you have to use HTML for parsing.

page_source = driver.page_source

Code Reference: ChatGpt

Step 8: Parse Source with BeautifulSoup

After getting HTML content, the next step is to use BeautifulSoup to pull out product details.

soup = BeautifulSoup(page_source, ‘html.parser’)

Code Reference: ChatGpt

Step 9: Extract Product Detail

In this step, you have to go through the HTML structure of the product details you would like to scrape. Here we are extracting basic product information, such as product name and price. After this, you have to inspect the element in Chrome by pressing F12 and then find your desired tags. 

products = soup.find_all('div', class_='product-item')  # Modify the class name based on actual structure

for product in products:

product_name = product.find('span', class_='product-name').text.strip()  # Modify based on actual HTML

product_price = product.find('span', class_='product-price').text.strip()  # Modify based on actual HTML

print(f'Product Name: {product_name}')

print(f'Price: {product_price}')

print('---')

Code Reference: ChatGpt

Step 10: Close Chrome

After scraping product details, you need to close the browser, i.e., Chrome.

driver.quit()

Code Reference: ChatGpt

Full Example Code

from selenium import webdriver

from selenium.webdriver.common.by import By

from selenium.webdriver.common.keys import Keys

from bs4 import BeautifulSoup

import time

driver_path = '/path/to/chromedriver'  # Modify this path

driver = webdriver.Chrome(executable_path=driver_path)

url = 'https://www.instamart.com/products'  # Replace with actual URL

driver.get(url)

time.sleep(5)

driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")

time.sleep(3)

page_source = driver.page_source

soup = BeautifulSoup(page_source, 'html.parser')

products = soup.find_all('div', class_='product-item')  # Adjust based on actual structure

for product in products:

product_name = product.find('span', class_='product-name').text.strip()  # Modify based on actual structure

product_price = product.find('span', class_='product-price').text.strip()  # Modify based on actual structure

print(f'Product Name: {product_name}')

print(f'Price: {product_price}')

print('---')

driver.quit()

Code Reference: ChatGpt

Conclusion

This blog is all about scraping Swiggy Instamart Product Details. Here, we knew the importance of extracting Instamart, various techniques to pull out data from it, and followed the step-by-step approach to scrape Instamart grocery Data using Python libraries called Selenium and BeautifulSoup.

Organizations, researchers, and startups can extract data from Instamart to gather product name, description, price, customer ratings, brand, availability, and more. This data helps them to not only understand market trends but also customer preferences. Ultimately, it enables them to stay ahead in the highly competitive e-commerce market.   Everybody knows that every coin has two sides, positive and negative. We already understood how scraping Swiggy Instamart data helps businesses to stay competitive. But scraping this e-commerce platform requires organizations to follow ethical and legal practices. Organizations can use the above code to scrape basic product details.