phantomunit4mqg/instacart-product-search-scraper
Instacart product pricing intelligence
Instacart Product Search Scraper
A powerful tool for extracting structured product data from Instacart category pages, including prices, sizes, and images. It helps transform large grocery catalogs into clean datasets for analysis, monitoring, and decision-making.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for instacart-product-search-scraper you've just found your team — Let’s Chat. 👆👆
Introduction
The Instacart Product Search Scraper automates the collection of grocery product data from category-based pages.
It solves the challenge of manually tracking thousands of items across fast-changing food categories.
This project is built for analysts, researchers, and businesses that rely on accurate product intelligence.
Grocery Product Intelligence at Scale
- Collects products across multiple food categories in one run
- Handles pagination and category depth reliably
- Produces structured, analysis-ready datasets
- Designed for repeatable market and pricing studies
Features
| Feature | Description |
|---|---|
| Category-based extraction | Collects products directly from category listing pages. |
| Structured product records | Normalized fields for easy storage and analysis. |
| Configurable limits | Control how many items are collected per category. |
| Image data support | Captures product image references for catalogs. |
| Resilient execution | Retries failed requests to improve completion rates. |
What Data This Scraper Extracts
| Field Name | Field Description |
|---|---|
| id | Unique identifier assigned to each product. |
| url | Direct link to the product detail page. |
| name | Full product name including brand and description. |
| size | Package size and unit information. |
| landing_param | Internal product routing and categorization value. |
| image | Product image reference used for visual identification. |
| from_url | Source category URL where the product was found. |
Example Output
[
{
"id": "16695070",
"url": "https://www.instacart.com/products/16695070-healthy-choice-cafe-steamers-grilled-chicken-marinara-with-parmesan-9-5-oz",
"name": "Healthy Choice Café Steamers Grilled Chicken Marinara With Parmesan",
"size": "9.5 oz",
"landing_param": "16695070-healthy-choice-cafe-steamers-grilled-chicken-marinara-with-parmesan-9-5-oz",
"image": "https://d2lnr5mha7bycj.cloudfront.net/product-image/file.png",
"from_url": "https://www.instacart.com/categories/316-food/627-frozen-food?page=2"
}
]
Directory Structure Tree
instacart-product-search-scraper/
├── src/
│ ├── runner.py
│ ├── extractors/
│ │ ├── category_parser.py
│ │ └── product_mapper.py
│ ├── utils/
│ │ ├── request_handler.py
│ │ └── validators.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
Use Cases
- Market analysts use it to track grocery pricing trends, so they can identify shifts in consumer demand.
- Retail strategists use it to monitor competitor product ranges, so they can adjust assortments.
- Data teams use it to build grocery datasets, so they can power dashboards and reports.
- Researchers use it to study category-level availability, so they can analyze supply patterns.
FAQs
Does this scraper work with multiple categories at once?
Yes, it accepts multiple category URLs and processes them sequentially while keeping results organized by source.
Can I limit how many products are collected?
Yes, configurable limits allow you to control the number of items per category to balance depth and performance.
Is the output suitable for spreadsheets or databases?
Absolutely. The structured format can be directly imported into spreadsheets, SQL databases, or analytics pipelines.
How often should categories be refreshed?
For active grocery categories, daily or weekly runs provide accurate snapshots of pricing and availability.
Performance Benchmarks and Results
Primary Metric: Average extraction of 20–30 products per category page within seconds.
Reliability Metric: Consistent completion rate above 95% across repeated category runs.
Efficiency Metric: Optimized requests minimize redundant page loads and reduce runtime.
Quality Metric: High data completeness with stable product IDs and clean field values.
"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. Syed
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