Scraping Dunzo for Real-Time Product Availability and Pricing Insights

Introduction

Dunzo is an on-demand delivery service across various cities that updates product availability and pricing information in real-time across several categories from groceries, medicines, and daily essentials. Businesses and researchers can obtain a competitive edge by extracting and analyzing this data.

This blog will shed light on the methodology, tools, and challenges one may face while scraping Dunzo's product availability and pricing insights while adhering to ethical and legal considerations.

Understanding Dunzo's Data Structure

To effectively extract Dunzo product data, you must first understand its structure. Data is generated through both the application and web interface, displayed in real-time based on location. Key components include:

Tools and Technologies for Scraping Dunzo Data

Several tools and technologies facilitate effective web scraping of Dunzo data:

Methodology for Extracting Dunzo Product Data

import requests
from bs4 import BeautifulSoup

url = "https://www.dunzo.com/{city}/store/{store_id}"
headers = {"User-Agent": "Mozilla/5.0"}
response = requests.get(url, headers=headers)

if response.status_code == 200:
    soup = BeautifulSoup(response.text, 'html.parser')
    products = soup.find_all('div', class_='product')
    
    for product in products:
        name = product.find('h2').text
        price = product.find('span', class_='price').text
        availability = 'Out of Stock' if 'out-of-stock' in product['class'] else 'In Stock'
        print(f"{name} - {price} - {availability}")
else:
    print("Failed to fetch data")

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager

service = Service(ChromeDriverManager().install())
driver = webdriver.Chrome(service=service)
driver.get("https://www.dunzo.com")

products = driver.find_elements(By.CLASS_NAME, "product")
for product in products:
    print(product.text)

driver.quit()

Challenges in Web Scraping Dunzo Real-Time Data

Data Processing and Analysis

Once the data is extracted, it needs to be structured and analyzed for meaningful insights:

Using Python's Pandas and Matplotlib, one can analyze and visualize the data:

import pandas as pd
import matplotlib.pyplot as plt

# Sample data
data = {'Product': ['Milk', 'Bread', 'Eggs'], 'Price': [50, 30, 60]}
df = pd.DataFrame(data)

# Visualization
df.plot(kind='bar', x='Product', y='Price', legend=False)
plt.title('Product Price Comparison')
plt.show()

Example Output:

Conclusion

Real-time product availability and price insights scraped from Dunzo can provide businesses with valuable market intelligence. Through web scraping, businesses can analyze product trends, observe price fluctuation patterns, and build inventory strategies.

For companies that want to scale their scraping operations, they can opt to use headless browsers with rotating proxies, plus AI-powered data capturing, to increase productivity while retaining accuracy. Where possible, using Dunzo's official API is the preferred approach to avoid any legal issues in data extraction.