Vexaloom Labs

Use Naver Shopping Price Comparison Scraper with Python

Search Naver Shopping by keyword, pull product details by ID, browse a category or fetch trending products, with price range across sellers, review and purchase counts, brand and mall name.

1. Choose the data you need

Review the published README for supported collection modes, examples, and limits. The recipe below uses the current published input definition.

2. Prepare a source-based input

This JSON contains published prefills and required-field defaults. Review every value for your task before running it; it is not a promise of successful collection.

{
  "includeAds": false,
  "maxItems": 2,
  "searchQueries": [
    "스마트폰"
  ],
  "sort": "relevance"
}

Download input.example.json and save your reviewed copy as input.json.

FieldTypeRequiredDescription
searchQueriesarrayoptionalOne Naver Shopping search per keyword, in Korean or English. Each keyword returns up to 90 product cards, including ads. Leave empty only when an old task supplies a single `keyword`.
includeAdsbooleanoptionalKeep sponsored product cards in the results. Ads take a large share of the up to 90 cards, so ads-off runs return the organic subset. Every record carries `isAd`.
minPriceintegeroptionalDrop fetched products cheaper than this price in Korean Won. Applied after collection, to the products this run fetched.
maxPriceintegeroptionalDrop fetched products more expensive than this price in Korean Won. Applied after collection, to the products this run fetched.
sortstringoptionalOrder the products this run fetched. Naver's mobile page does not accept a server-side sort, so this is a local sort, not a re-query.
maxItemsintegeroptionalTotal products to save across all keywords, after ad and price filtering (1–1000). The source still caps each keyword at 90 cards.

3. Start an Actor run

Set APIFY_TOKEN in your environment. Python 3.10 or newer is sufficient; this recipe uses no third-party packages.

Execution may incur charges. Review current pricing on Apify. The request asks for a 120-second timeout and a charge limit; these settings are subject to Apify’s pricing and do not guarantee an all-inclusive spending cap.

import json
import os
import urllib.request

# Save the schema-derived example as input.json; review and edit it first.
with open("input.json", encoding="utf-8") as stream:
    actor_input = json.load(stream)

token = os.environ["APIFY_TOKEN"]
request = urllib.request.Request(
    "https://api.apify.com/v2/acts/RyxNt6M9XZi7gJCQL/runs?timeout=120&maxTotalChargeUsd=0.50",
    data=json.dumps(actor_input).encode("utf-8"),
    headers={"Authorization": "Bearer " + token, "Content-Type": "application/json"},
    method="POST",
)
# This starts a potentially billable run. Do not automatically retry this POST.
with urllib.request.urlopen(request, timeout=40) as response:
    run = json.load(response)["data"]
print("Run ID:", run["id"])
print("Status:", run["status"])
# Inspect and download results in Apify Console once the run succeeds.

4. Inspect the actual results

Open the run in Apify Console and wait for a successful completion. Use the published output schema to locate results and the dataset schema to understand declared views. Missing or optional record fields must not be treated as guaranteed values.

One record per Naver Shopping product — price and discount, review rating and count, sellers, delivery, variants, category IDs, image and product link.

Questions before you run

Which inputs are required?

No top-level fields are marked required in the published schema. Conditional requirements can still apply; check the README.

What does this guide verify?

The fields, prefills, and schema links are rendered from the published build. This is a usage recipe, not a paid live extraction test. Source availability and collection results can vary.

What if the run request times out?

Check Apify Console before retrying. The first request may already have created a run. Do not automatically repeat a run-creation POST.

Generated from source build dJUrNu0nTry9REFAJ and its README and schemas. Source changes regenerate this guide; no third-party editorial claims are inferred.