Google Trends API for Python in 2025: pytrends vs Scraper

Google Trends is one of the most valuable free datasets in SEO and market research. But there is no official API. The unofficial workarounds break constantly. This guide covers what works in 2025.

The problem with pytrends

pytrends is the most popular Python library for Google Trends. It reverse-engineers the same endpoint your browser uses. The problem: Google rate-limits it aggressively.

# This works for about 100 requests before you hit 429
from pytrends.request import TrendReq

pytrends = TrendReq(hl='en-US', tz=360)
pytrends.build_payload(['python', 'javascript'], timeframe='today 12-m')
df = pytrends.interest_over_time()

After a few hundred requests you get ResponseError: The request failed: Google returned a response with code 429. Adding time.sleep() helps but does not solve it at scale.

What data Google Trends actually provides

Google Trends returns four datasets per query:

Interest over time — a 0-100 index per time period, normalized to peak search volume. Not absolute search counts.

Interest by region — the same index broken down by country, state, or city depending on zoom level.

Related queries — the top 25 related search terms and 25 rising terms (those with a significant recent increase).

Related topics — broader topic entities related to your keyword.

Getting clean data with the Apify actor

The Google Trends Scraper Pro handles rate limiting via residential proxy rotation. Input is simple:

import apify_client

client = apify_client.ApifyClient('YOUR_APIFY_TOKEN')

run_input = {
    "keywords": ["python", "javascript", "typescript"],
    "timeframe": "today 12-m",
    "geo": "US",
    "category": 0,
}

run = client.actor('themineworks/google-trends-pro').call(run_input=run_input)

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item['keyword'], item['interest_over_time'])

Comparing keywords

Google Trends normalizes all keywords to the same 0-100 scale relative to the keyword with the highest search interest. When comparing “python” vs “javascript”, the one with more searches gets 100 and the others scale proportionally.

This means you cannot compare data from two separate queries — you must put all keywords in the same payload.

Regional breakdown

To get state-level data for the US:

run_input = {
    "keywords": ["python"],
    "timeframe": "today 5-y",
    "geo": "US",
    "resolution": "REGION",  # returns states
}

For city-level: use "resolution": "CITY". For country-level: set "geo": "" (empty string = worldwide).

Scheduling trend monitoring

Run the actor on a weekly schedule via Apify Scheduler and push results to a spreadsheet or database. This lets you track rising trends before they peak — useful for content planning and keyword research.

Pricing

Pay only per result returned. Zero charge on failed or empty runs.