Flipkart Scraper is a powerful tool designed to collect detailed product data directly from Flipkart. It simplifies large-scale data extraction, enabling users to gather pricing, images, variants, reviews, and category information with ease. This scraper helps businesses, analysts, and developers access structured ecommerce data quickly and reliably.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts structured product information from Flipkart pages, including listings, product detail pages, and category URLs. It solves the challenge of gathering clean and consistent ecommerce data at scale. Ideal for price trackers, analysts, researchers, and developers building data-driven applications.
- Retrieves complete product information including images, prices, variants, specs, and seller details.
- Supports category pages, product URLs, and search result pages.
- Automatically paginates through listings to collect large datasets.
- Provides detailed variant-level information when enabled.
- Ensures consistent and normalized output across multiple product types.
| Feature | Description |
|---|---|
| Extract full product details | Captures name, price, images, brand, specs, variants, and seller data. |
| Detailed variant scraping | Optionally fetches variant-specific specs, pricing, and attributes. |
| Category & search URL support | Works with any Flipkart listing or search results page. |
| Pagination handling | Automatically navigates through multiple listing pages. |
| Rating & review metrics | Extracts rating summaries and review counts at product or variant level. |
| Flexible item limits | Control scraping size with max items or max items per URL. |
| Field Name | Field Description |
|---|---|
| url | URL of the product or variant. |
| title | Full product name as displayed on Flipkart. |
| brand | Brand associated with the product. |
| main_image | Primary image of the product. |
| specs | Technical specifications grouped by category. |
| attributes | Variant attributes like color, storage, or RAM. |
| prices | List of pricing objects including selling and special price. |
| seller_info | Seller ID, name, and rating. |
| rating_info | Average rating, rating count, review count, and aspect ratings. |
| variants | All available product variants with URLs and metadata. |
| breadcrumbs | Category hierarchy showing product's placement. |
[
{
"url": "https://www.flipkart.com/samsung-galaxy-z-flip5-graphite-512-gb/p/itm68c9544497769",
"main_image": "http://rukmini1.flixcart.com/image/823/823/xif0q/mobile/u/w/t/galaxy-flip5-sm-f731bliains-samsung-original-imagru5pgwsfnrmq.jpeg?q=100",
"brand": "SAMSUNG",
"title": "SAMSUNG Galaxy Z Flip5 (Graphite, 512 GB)",
"prices": [
{ "amount": 113999, "price_type": "FSP" },
{ "amount": 109999, "price_type": "SPECIAL_PRICE" }
],
"rating_info": {
"average": 4.4,
"count": 431
},
"seller_info": {
"name": "BTPLD",
"rating": 5
}
}
]
Flipkart Scraper/
├── src/
│ ├── main.py
│ ├── extractors/
│ │ ├── product_parser.py
│ │ ├── variants_parser.py
│ │ └── utils_format.py
│ ├── outputs/
│ │ └── exporter.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── input.sample.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- Ecommerce analysts use it to track product price changes so they can monitor competitor pricing trends.
- Researchers extract product specifications to analyze market shifts and device capabilities.
- Developers integrate the scraper into dashboards to automate catalog updates.
- Retailers use it to compare seller listings and identify opportunities to optimize pricing.
- Marketing teams gather rating and review metrics to analyze customer sentiment.
Q: Can this scraper handle category pages with thousands of products? Yes, it automatically paginates through listing URLs and can scrape large datasets using item limit controls.
Q: Does it scrape reviews? It collects rating summaries but not full reviews; review scraping requires a separate tool.
Q: Can the scraper follow variant URLs? Yes, when detailed variant mode is enabled, it retrieves variant-specific data including images and specs.
Q: What type of URLs can I use? You can input product URLs, category URLs, search result pages, or any listing that displays products.
Primary Metric: Handles an average of 30–50 product pages per minute depending on page complexity. Reliability Metric: Maintains a stable success rate above 97% across diverse product categories. Efficiency Metric: Optimized request flow reduces bandwidth and avoids redundant page loads. Quality Metric: Data completeness typically exceeds 95%, including images, specs, and variants.
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“Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time.” Nathan Pennington
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“Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on.” Eliza
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“Exceptional results, clear communication, and flawless delivery. Bitbash nailed it.” Syed
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