Web Scraping Dunzo: How to Extract Grocery and Delivery Data

Apr 14, 2025

In today’s fast-paced e-commerce landscape, data-driven decision-making is crucial for businesses to remain competitive. Dunzo is a well-known hyperlocal delivery service in India that provides live information on the delivery of groceries, essentials, and other necessary items. Scraping the Dunzo data gives businesses useful insights into grocery price points, delivery timelines, product availability, and customer trends.

In this detailed guide, we will cover:

• What Dunzo data scraping is and its benefits.
• Tools and technologies required for effective scraping.
• A step-by-step tutorial with Python code examples.
• How to bypass anti-scraping mechanisms.
• Data cleaning, storage, and visualization.
• Legal and ethical considerations.
• Why choose CrawlXpert for Dunzo data scraping?

1. What is Dunzo Data Scraping?

Dunzo data scraping is the process of programmatically extracting grocery and delivery information from Dunzo’s website or mobile application. By automating this data collection process, businesses can gain real-time insights into:

• Product Listings: Names, categories, descriptions, and brands.
• Pricing Data: Base prices, discounts, offers, and dynamic pricing changes.
• Delivery Information: Delivery fees, estimated times, and service areas.
• Availability Status: Whether products are in stock, out of stock, or limited in quantity.
• Customer Reviews: Ratings, reviews, and feedback insights.
• Location-based Data: Store-specific pricing, offers, and delivery variations.

2. Why Scrape Dunzo Data?

Scraping Dunzo’s grocery and delivery data offers several strategic advantages, including:

(a) Competitive Pricing Analysis

• Monitor Competitor Prices: Extract grocery prices across multiple vendors and identify pricing strategies.
• Dynamic Pricing: Adjust your pricing strategies in real-time based on market fluctuations.
• Price Comparison: Compare pricing patterns across different locations and vendors.

(b) Delivery Insights and Optimization

• Delivery Time Analysis: Identify average delivery times by location.
• Cost Optimization: Extract delivery charges and fees to streamline your logistics expenses.
• Service Area Insights: Identify popular delivery zones and expansion opportunities.

(c) Product and Stock Availability Insights

• Track Stock Levels: Identify frequently stocked-out products.
• Popular Products: Recognize trending items and customer preferences.
• New Product Listings: Stay updated on new grocery items and offers.

(d) Marketing and Customer Insights

• Customer Feedback: Analyze ratings and reviews for sentiment analysis.
• Promotional Opportunities: Identify frequently discounted or promoted products.
• Targeted Campaigns: Use insights to run geo-targeted and product-specific marketing campaigns.

3. Tools and Technologies for Scraping Dunzo

(a) Python Libraries for Scraping

• requests: To send HTTP requests and retrieve webpage content.
• BeautifulSoup: For HTML parsing and data extraction.
• Selenium: To handle dynamic content and JavaScript-rendered pages.
• pandas: For organizing and storing the scraped data.
• lxml: An XML and HTML parsing library with fast performance.

(b) Proxy and Anti-Bot Solutions

• ScraperAPI: Handles IP rotation and CAPTCHA solving.
• Bright Data: Provides residential proxies to avoid IP blocking.
• Smartproxy: Offers rotating proxy networks to bypass restrictions.

(c) Browser Automation Tools

• Playwright: Efficient for headless browser automation.
• Puppeteer: A Node.js library for controlling Chrome, ideal for JavaScript-heavy pages.

(d) Data Storage Options

• CSV/JSON: For storing small-scale data locally.
• MongoDB or MySQL: For large-scale structured data.
• Cloud Storage: Amazon S3, Google Cloud, or Azure for large datasets.

4. Setting Up Your Dunzo Scraper

(a) Install Required Libraries

Use pip to install the necessary libraries:

pip install requests beautifulsoup4 selenium pandas

(b) Inspect Dunzo’s Website Structure

• Open Dunzo in Chrome.
• Right-click → Inspect → Select Elements.
• Identify HTML tags containing product and delivery data.
• Note dynamic content that may require Selenium.

(c) Sending HTTP Requests

import requests
from bs4 import BeautifulSoup

url = 'https://www.dunzo.com/bangalore/groceries'
headers = {'User-Agent': 'Mozilla/5.0'}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.content, 'html.parser')

(d) Extracting Grocery and Delivery Data

products = soup.find_all('div', class_='product-card')

for product in products:
    title = product.find('h2', class_='product-title').text
    price = product.find('span', class_='product-price').text
    delivery_time = product.find('div', class_='delivery-time').text
    print(f'Product: {title}, Price: {price}, Delivery Time: {delivery_time}')

5. Bypassing Dunzo’s Anti-Scraping Measures

(a) Using Proxies and IP Rotation

proxies = {'http': 'http://user:pass@proxy-server:port'}
response = requests.get(url, headers=headers, proxies=proxies)

(b) User-Agent Rotation

import random
user_agents = [
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64)',
    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)'
]
headers = {'User-Agent': random.choice(user_agents)}

(c) Handling Dynamic Content with Selenium

from selenium import webdriver

options = webdriver.ChromeOptions()
options.add_argument('--headless')
driver = webdriver.Chrome(options=options)

driver.get(url)
data = driver.page_source
driver.quit()

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

6. Data Cleaning, Storage, and Visualization

(a) Cleaning and Organizing the Data

import pandas as pd

data = {'Product': titles, 'Price': prices, 'Delivery Time': delivery_times}
df = pd.DataFrame(data)

(b) Storing Data in CSV

df.to_csv('dunzo_grocery_data.csv', index=False)

(c) Visualizing the Data

import matplotlib.pyplot as plt

df['Price'] = df['Price'].str.replace('₹', '').astype(float)
plt.hist(df['Price'], bins=20, color='skyblue')
plt.xlabel('Price')
plt.ylabel('Frequency')
plt.title('Dunzo Grocery Price Distribution')
plt.show()

7. Legal and Ethical Considerations

• Respect Dunzo’s Terms of Service and avoid aggressive scraping.
• Rate limit your requests to prevent server overload.
• Use publicly available data and avoid scraping sensitive information.

8. Why Choose CrawlXpert for Dunzo Data Scraping?

CrawlXpert offers industry-leading web scraping services, making them the ideal partner for Dunzo data extraction. With scalable infrastructure, proxy management, and real-time data extraction capabilities, CrawlXpert ensures accurate and reliable results.

• Advanced Anti-Bot Evasion: Bypass Dunzo’s anti-scraping measures efficiently.
• Real-Time Data Extraction: Continuous data updates for dynamic pricing insights.
• Custom Scraping Solutions: Tailored solutions for unique business needs.
• Secure and Compliant: Legal and ethical data scraping practices.

Conclusion

Web scraping Dunzo grocery or delivery data provides businesses with the required insight into pricing, availability, and delivery trends. Large-scale Dunzo data can be collected and analyzed with the usage of Python, proxies, and anti-bot techniques. Choosing CrawlXpert makes sure that the data extraction will be accurate, reliable, and scalable, thus enabling smarter business decision-making.