Vexaloom Labs

Use Pinterest Trending Topics & Shopping Category Scraper with Python

Collect Pinterest's public featured and editorial trend topics, its shopping category taxonomy, and shopping-category click metrics with history and forecast, by region.

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.

{
  "ageGroup": "all",
  "categoryNames": [],
  "days": 90,
  "gender": "all",
  "includeAudience": false,
  "includeImages": false,
  "includeRelated": false,
  "includeSuggestions": false,
  "includeTopCategories": false,
  "interest": "all",
  "keywords": [],
  "keywordsToExclude": [],
  "keywordsToInclude": [],
  "lookbackWindow": "30D",
  "maxItems": 3,
  "mode": "trendingSearches",
  "moment": "all",
  "predictedDays": 0,
  "region": "US",
  "shoppingEvent": "OUTBOUND_CLICK",
  "trendType": "topMonthly"
}

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

FieldTypeRequiredDescription
modestringrequiredChoose a Pinterest Trends dataset.
regionstringoptionalPinterest Trends market. Combined codes such as `GB+IE` cover Pinterest's published regional groups.
trendTypestringoptionalTrending list to collect (mode=trendingSearches).
lookbackWindowstringoptionalComparison window used by Pinterest for trending searches (mode=trendingSearches).
endDatestringoptionalHistorical end date in `YYYY-MM-DD` format. Leave empty for Pinterest's latest available date (mode=trendingSearches, keywordResearch, shoppingCategories, shoppingMetrics).
intereststringoptionalPinterest interest filter for trending searches (mode=trendingSearches).
momentstringoptionalPinterest seasonal moment filter for trending searches (mode=trendingSearches).
keywordsToIncludearrayoptionalOnly keep trending searches containing at least one of these words (mode=trendingSearches).
keywordsToExcludearrayoptionalDrop trending searches containing any of these words (mode=trendingSearches).
ageGroupstringoptionalPinterest audience age filter for trending searches and keyword research.
genderstringoptionalPinterest audience gender filter for trending searches and keyword research.
keywordsarrayoptionalTerms to research (mode=keywordResearch). Leave empty to use Pinterest's current top trending searches.
daysintegeroptionalHistory length in days for keyword research and shopping metrics.
predictedDaysintegeroptionalPinterest forecast horizon. Forecasts are published for the US only (mode=keywordResearch, shoppingMetrics).
includeRelatedbooleanoptionalAdd Pinterest's related terms and their weekly history for each keyword (mode=keywordResearch).
includeAudiencebooleanoptionalAdd age and gender distributions for each keyword (mode=keywordResearch).
includeImagesbooleanoptionalAdd a representative Pinterest image for each keyword (mode=keywordResearch).
includeSuggestionsbooleanoptionalAdd Pinterest search suggestions for each keyword (mode=keywordResearch).
shoppingEventstringoptionalPinterest shopping signal used for category metrics and ranked shopping trends.
includeTopCategoriesbooleanoptionalAdd Pinterest's fastest-growing shopping categories with related searches (mode=shoppingCategories).
categoryNamesarrayoptionalExact Pinterest shopping category names for metrics, such as `Sofas` or `Wedding dress` (mode=shoppingMetrics). Leave empty to use Pinterest's featured level-2 categories.
maxItemsintegeroptionalMaximum records to return. Trending lists are partitioned by interest to go beyond Pinterest's 100-term list ceiling.

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/i5MAeZzs7WKBrkouR/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.

Public Pinterest trending searches, keyword history and forecasts, related terms, audience demographics, images, suggestions, seasonal moments, featured topics, editorial stories and shopping trends.

Questions before you run

Which inputs are required?

The published required fields are mode

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 LAKEQaRBcKlEf3ayl and its README and schemas. Source changes regenerate this guide; no third-party editorial claims are inferred.