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  • Dedicated account manager
  • Customized solutions to fulfill data scraping goals
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Big Basket Grocery Data Scraping API – Web Scraping Big Basket APIs

Actowiz Solutions offers the Big Basket Grocery Data Scraping API, a powerful tool for businesses looking to scrape Big Basket supermarket data efficiently. Web Scraping Big Basket APIs enables you to gather comprehensive grocery data, including product listings, prices, and availability across multiple countries, including the USA, UK, India, UAE, Japan, Italy, Germany, Canada, Australia, China, Switzerland, Qatar, Singapore, Ireland, Macao SAR, Luxembourg, Austria, Denmark, and Norway. With our Grocery Data Scraping Services, you can effortlessly scrape Big Basket product data and integrate it into your systems through seamless Big Basket API Integration. Enhance your competitive edge with accurate, real-time grocery data at your fingertips.

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Key Features

Data-Accuracy

Data Accuracy

Ensure high accuracy in scraped product information and pricing.

Multi-Country-Support

Multi-Country Support

Access supermarket data from multiple countries effortlessly and efficiently.

Comprehensive-Listings

Comprehensive Listings

Retrieve detailed grocery product listings, including descriptions and images.

Price-Tracking

Price Tracking

Monitor price changes and promotional offers across various stores.

Custom-Extraction

Custom Extraction

Tailor data scraping to focus on specific product categories.

User-Friendly

User-Friendly API

Simple integration process with existing systems for seamless use.

Automated-Scraping

Automated Scraping

Set automated schedules for regular data collection without manual intervention.

Reliable-Performance

Reliable Performance

Consistent and dependable data extraction with minimal downtime or errors.

Use Cases

Market Analysis

Utilize Big Basket Product Scraping to analyze competitor pricing strategies and adjust marketing plans accordingly for better market positioning.

Market-Analysis
Inventory-Management
Use Cases

Inventory Management

Integrate the Big Basket Order Data API to streamline inventory management, ensuring stock levels meet customer demands effectively and efficiently.

Use Cases

Customer Insights

Leverage Big Basket Customer Reviews Scraping to gather feedback and improve product offerings based on customer preferences and satisfaction levels.

Customer-Insights
Data-Integration
Use Cases

Data Integration

Employ the Big Basket Product Data API to integrate product information into e-commerce platforms, enhancing the online shopping experience for customers.

Use Cases

Efficiency Boost

Implement Automated Big Basket Data Extraction to save time and resources by regularly collecting data without manual effort, ensuring data freshness.

Efficiency-Boost

API Endpoints

/products

Description: Fetch detailed product information based on various criteria.

Parameteres:

keyword (string): Search term to find products.

category (string): Filter by specific product categories.

sort (string): Sorting options (e.g., price, popularity).

page (int): Pagination for large datasets.

Response: JSON object containing product name, ID, price, rating, and description.

/product/{product_id}

Description: Retrieve detailed information for a specific product using its unique ID.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object with product details including title, price, features, and customer reviews.

/reviews

Description: Fetch customer reviews and ratings for a specific product.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object containing reviewer names, ratings, review texts, and helpfulness votes.

/offers

Description: Retrieve current offers, discounts, and availability for a specific product.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object with offer details, including price, discount percentages, and stock availability.

/related

Description: Get recommendations for related products based on a specific product.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object containing a list of related product IDs and names.

/categories

Description: Retrieve a list of available product categories on Big Basket.

Response: JSON object containing category names and IDs for filtering products.

/search

Description: Search for products based on various criteria like keywords and filters.

Parameteres:

query (string): Search term to find products.

sort (string): Sorting options (e.g., price, rating).

page (int): Pagination for large datasets.

Response: JSON object with a list of products matching the search criteria.

/price-history

Description: Retrieve historical pricing data for a specific product.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object containing price changes over time, including dates and corresponding prices.

/store-locations

Description: Fetch store locations that offer delivery or pickup options.

Parameteres:

zipcode (string): ZIP code for location-based searching.

Response: JSON object containing store names, addresses, and operating hours.

/cart

Description: Manage the shopping cart for a user session.

Parameteres:

user_id (string): Unique identifier for the user.

action (string): Action to perform (add, remove, update).

product_id (string): Unique ID of the product.

Response: JSON object confirming cart status and updated totals.

/checkout

Description: Process the checkout for a user’s cart.

Parameteres:

user_id (string): Unique identifier for the user.

payment_info (object): Payment details for transaction processing.

Response: JSON object containing order confirmation details and estimated delivery time.

API Response Format

All responses are returned in JSON format for easy integration into your application.

from flask import Flask, jsonify, request

app = Flask(__name__)

# Sample data for demonstration purposes
products = [
    {"id": "1", "name": "Apple", "price": 1.00, "rating": 4.5, "description": "Fresh apples"},
    {"id": "2", "name": "Banana", "price": 0.50, "rating": 4.0, "description": "Ripe bananas"},
    # Add more products as needed
]

# Sample data for reviews
reviews = {
    "1": [
        {"reviewer": "Alice", "rating": 5, "text": "Great apples!", "helpfulness": 10},
        {"reviewer": "Bob", "rating": 4, "text": "Very fresh.", "helpfulness": 5}
    ],
    "2": [
        {"reviewer": "Charlie", "rating": 4, "text": "Tasty bananas.", "helpfulness": 3}
    ]
}

@app.route('/products', methods=['GET'])
def get_products():
    keyword = request.args.get('keyword', '')
    category = request.args.get('category', '')
    sort = request.args.get('sort', 'name')
    page = int(request.args.get('page', 1))

    # Filter and sort products (basic implementation)
    filtered_products = [p for p in products if keyword.lower() in p['name'].lower()]
    sorted_products = sorted(filtered_products, key=lambda x: x[sort])

    return jsonify(sorted_products)

@app.route('/product/', methods=['GET'])
def get_product(product_id):
    product = next((p for p in products if p['id'] == product_id), None)
    if product:
        return jsonify(product)
    return jsonify({"error": "Product not found"}), 404

@app.route('/reviews', methods=['GET'])
def get_reviews():
    product_id = request.args.get('product_id')
    product_reviews = reviews.get(product_id, [])
    return jsonify(product_reviews)

@app.route('/offers', methods=['GET'])
def get_offers():
    product_id = request.args.get('product_id')
    # This is a placeholder; implement offer logic as needed
    offers = {"product_id": product_id, "offers": [{"discount": "10%", "availability": "In Stock"}]}
    return jsonify(offers)

@app.route('/related', methods=['GET'])
def get_related():
    product_id = request.args.get('product_id')
    # This is a placeholder; implement related product logic as needed
    related_products = [{"id": "3", "name": "Orange"}, {"id": "4", "name": "Grapes"}]
    return jsonify(related_products)

@app.route('/categories', methods=['GET'])
def get_categories():
    categories = [{"id": "1", "name": "Fruits"}, {"id": "2", "name": "Vegetables"}]
    return jsonify(categories)

@app.route('/search', methods=['GET'])
def search_products():
    query = request.args.get('query', '')
    sort = request.args.get('sort', 'name')
    page = int(request.args.get('page', 1))

    # Implement search logic (basic implementation)
    searched_products = [p for p in products if query.lower() in p['name'].lower()]
    sorted_products = sorted(searched_products, key=lambda x: x[sort])

    return jsonify(sorted_products)

@app.route('/price-history', methods=['GET'])
def get_price_history():
    product_id = request.args.get('product_id')
    # This is a placeholder; implement price history logic as needed
    price_history = [{"date": "2023-10-01", "price": 1.00}, {"date": "2023-10-10", "price": 1.20}]
    return jsonify(price_history)

@app.route('/store-locations', methods=['GET'])
def get_store_locations():
    zipcode = request.args.get('zipcode')
    # This is a placeholder; implement location logic as needed
    locations = [{"name": "Store A", "address": "123 Main St", "hours": "9am-9pm"}]
    return jsonify(locations)

@app.route('/cart', methods=['POST'])
def manage_cart():
    user_id = request.json.get('user_id')
    action = request.json.get('action')
    product_id = request.json.get('product_id')
    # Implement cart management logic here
    return jsonify({"status": "success", "message": f"Product {action} to cart for user {user_id}."})

@app.route('/checkout', methods=['POST'])
def checkout():
    user_id = request.json.get('user_id')
    payment_info = request.json.get('payment_info')
    # Implement checkout logic here
    return jsonify({"status": "success", "message": "Checkout completed successfully."})

if __name__ == '__main__':
    app.run(debug=True)

Optimize Product Data with Our Big Basket Scraping API

Optimize your product data with our Big Basket Grocery Data Scraping API. Our powerful Big Basket Scraping API allows businesses to efficiently scrape Big Basket product data from various supermarkets, ensuring you stay updated with the latest product information. With our comprehensive Grocery Data Scraping Services, you can access critical insights into pricing, availability, and promotions across multiple regions. This enables informed decision-making and enhances competitive advantage in the ever-evolving market. Trust our reliable solutions to streamline your data extraction processes, ensuring you have the most accurate and timely data at your fingertips. Experience the difference with our Big Basket data solutions today!

Optimize-Product-Data-with-Our-Scraping-API

Maximize your competitive advantage with our Big Basket Scraping API—Start your data journey now!

Start Your Project with Us

Whatever your project size is, we will handle it well with all the standards fulfilled! We are here to give 100% satisfaction.

  • Any feature, you ask, we develop
  • 24x7 support worldwide
  • Real-time performance dashboard
  • Complete transparency
  • Dedicated account manager
  • Customized solutions to fulfill data scraping goals