Build Your First Extract Template

pip install nimble_python

import json
import os
from nimble_python import Nimble

nimble = Nimble(api_key=os.environ["NIMBLE_API_KEY"])

KEYWORD = "running shoes"
NIKE_URL = "https://www.nike.com/w/mens-running-shoes"


def fetch_amazon():
    result = nimble.extract.templates.run(
        template="amazon_serp",
        params={"keyword": KEYWORD}
    )
    products = result.to_dict()["data"]["parsing"]
    return [
        {
            "source": "Amazon",
            "name": p.get("product_name"),
            "price": p.get("price"),
            "rating": p.get("average_rating"),
            "url": p.get("product_url"),
        }
        for p in products
        if p.get("price")
    ]


def fetch_walmart():
    result = nimble.extract.templates.run(
        template="walmart_search",
        params={"keyword": KEYWORD}
    )
    products = result.to_dict()["data"]["parsing"]
    return [
        {
            "source": "Walmart",
            "name": p.get("product_name"),
            "price": p.get("price"),
            "rating": p.get("rating"),
            "url": p.get("product_url"),
        }
        for p in products
        if p.get("price")
    ]


def fetch_nike():
    result = nimble.extract.templates.run(
        template="nike_running_shoes_plp",
        params={"url": NIKE_URL}
    )
    products = result.to_dict()["data"]["parsing"]
    return [
        {
            "source": "Nike",
            "name": p.get("product_name"),
            "price": p.get("price"),
            "colors": p.get("colors", []),
            "url": p.get("url"),
        }
        for p in products
        if p.get("price")
    ]


def run_analysis():
    print(f'Competitive analysis: "{KEYWORD}"\n')

    all_products = []

    print("Fetching Amazon...")
    all_products.extend(fetch_amazon())

    print("Fetching Walmart...")
    all_products.extend(fetch_walmart())

    print("Fetching Nike...")
    all_products.extend(fetch_nike())

    sorted_products = sorted(all_products, key=lambda x: x["price"])

    print(f"\n{'=' * 55}")
    print(f"  RESULTS — {len(all_products)} products across 3 retailers")
    print(f"{'=' * 55}\n")

    for i, p in enumerate(sorted_products[:15], 1):
        stars = f" ★{p['rating']}" if p.get("rating") else ""
        colors = f" ({len(p['colors'])} colors)" if p.get("colors") else ""
        print(f"{i:2}. [{p['source']:7}] ${p['price']:<8.2f} {p['name']}{stars}{colors}")

    with open("shoe_analysis.json", "w") as f:
        json.dump(all_products, f, indent=2)

    print(f"\nFull results saved to shoe_analysis.json")


if __name__ == "__main__":
    run_analysis()

python shoe_analysis.py

Competitive analysis: "running shoes"

Fetching Amazon...
Fetching Walmart...
Fetching Nike...

=======================================================
  RESULTS — 62 products across 3 retailers
=======================================================

 1. [Walmart ] $19.98   Athletic Works Men's Knit Running Shoe ★4.1
 2. [Walmart ] $29.98   Starter Men's Athletic Running Shoe ★4.0
 3. [Walmart ] $44.97   Skechers Go Run Consistent ★4.3
 4. [Amazon  ] $54.99   New Balance Men's 515 V3 Sneaker ★4.5
 5. [Walmart ] $54.98   Nike Downshifter 12 ★4.5
 6. [Amazon  ] $64.99   Adidas Men's Runfalcon 3.0 ★4.6
 7. [Amazon  ] $69.99   Nike Men's Revolution 7 ★4.6
 8. [Nike    ] $110.00  Nike Pegasus Trail 5 (3 colors)
 9. [Amazon  ] $119.95  Brooks Ghost 15 ★4.7
10. [Nike    ] $130.00  Nike Pegasus 41 (5 colors)
...

Full results saved to shoe_analysis.json