For years, pytrends was the default way to pull Google Trends data into Python. Today its GitHub repository is archived (read-only): no new releases, no fixes, and well over a hundred open issues — dozens of them about the same error:

pytrends.exceptions.TooManyRequestsError: The request failed: Google returned a response with code 429

If your scripts or notebooks broke, this guide covers what still works in 2026, with code for each option.

Why pytrends fails with 429 errors

Google Trends has never had a public, general-purpose API. pytrends worked by calling the same internal endpoints the Google Trends website uses. Google rate-limits those endpoints per IP address — and much more aggressively for IPs of cloud providers (AWS, GCP, Colab, CI runners). A few dozen quick requests are often enough to get 429 Too Many Requests for minutes or hours. With the library unmaintained, nobody is adapting it when Google tightens limits or changes response formats.

Google announced an official Google Trends API in July 2025. It returns consistently scaled data (so numbers from different requests are comparable), up to 5 years back, with daily, weekly, monthly and yearly aggregation and region filters.

The catch: it is still an application-only alpha. You apply, join a queue and wait; there is no self-serve key, no published pricing and no date for general availability. Apply if you have a long-term project — but you probably can’t build on it today.

Option 2: Your own minimal client

You can call the same endpoints pytrends used. Two requests give you a timeline: explore returns widget tokens, and widgetdata/multiline returns the data. Using curl_cffi (it looks like a real Chrome browser to Google) instead of requests helps:

import json
from curl_cffi import requests

s = requests.Session(impersonate="chrome")
s.get("https://trends.google.com/trends/?geo=US")  # sets the cookie the API expects

req = {"comparisonItem": [{"keyword": k, "geo": "US", "time": "today 12-m"} for k in ["python", "javascript"]],
       "category": 0, "property": ""}
r = s.get("https://trends.google.com/trends/api/explore",
          params={"hl": "en-US", "tz": "0", "req": json.dumps(req)})
widgets = json.loads(r.text[r.text.index("{"):])["widgets"]      # responses start with )]}' — skip it
ts = next(w for w in widgets if w["id"] == "TIMESERIES")

r = s.get("https://trends.google.com/trends/api/widgetdata/multiline",
          params={"hl": "en-US", "tz": "0", "req": json.dumps(ts["request"]), "token": ts["token"]})
points = json.loads(r.text[r.text.index("{"):])["default"]["timelineData"]
for p in points[:3]:
    print(p["formattedTime"], p["value"])

This is fine for occasional use. For anything regular you’ll need what pytrends lacked:

  • Retries with backoff and rotating IPs — from a single IP you will hit 429 again.
  • More than 5 keywords — Google compares at most 5 per request, and every request is scaled so its own maximum is 100. Numbers from different requests aren’t comparable until you rescale them through a shared keyword (how that works).
  • Maintenance when Google changes these internal endpoints.

If you’d rather not run proxies and rescaling yourself, I maintain Google Trends Scraper — Unlimited Keywords on Apify. You call it like any API:

  • no 429 handling on your side — sessions and proxies rotate automatically,
  • any number of keywords on one common 0–100 scale (not just 5 per request), with a precision label per keyword,
  • interest over time, interest by region, related queries, and trending-now searches per country,
  • $2 per 1,000 keywords; Apify’s free plan includes monthly credits, enough for a couple of thousand keywords a month.

pip install apify-client pandas, then:

import pandas as pd
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("ambitious_vagabond/google-trends-unlimited").call(run_input={
    "searchTerms": ["python", "javascript", "typescript", "rust", "go", "kotlin"],
    "timeRange": "today 12-m",
    "geo": "US",
})
items = list(client.dataset(run.default_dataset_id).iterate_items())

# same shape as pytrends' interest_over_time(): dates as rows, one column per keyword
df = pd.DataFrame({i["keyword"]: {p["date"]: p["value"] for p in i["timeline"]} for i in items})
df.index = pd.to_datetime(df.index)
print(df.tail())

Each item also has a summary — average, max, peakDate, latest, changePercent, trend — and the timeline marks the current, unfinished period with isPartial.

Migrating from pytrends

pytrends Managed API input / output
build_payload(kw_list=[...]) (max 5) searchTerms — any number, one common scale
timeframe="today 12-m" timeRange — same codes (now 7-d, today 5-y, all…) or customTimeRange: "2024-01-01 2024-12-31"
geo="US" geo: "US" (or a sub-region like US-CA)
cat=... category
gprop="youtube" searchProperty: "youtube" (also images, news, froogle)
interest_over_time() timeline on each item (+ summary fields)
interest_by_region(resolution="REGION") includeRegions: true, regionResolution: "REGION"
related_queries() includeRelatedQueries: true
trending_searches(...) trendingNowCountries: ["US"]
related_topics() not available — Google currently returns empty data for related topics

FAQ

Can I still pip install pytrends? Yes, the package still installs and may work for a handful of requests from a home connection. It is unmaintained, though, and 429 errors are very common from cloud servers and notebooks.

Is there an official Google Trends API? Only as an application-based alpha (since July 2025). If you’re accepted, it’s the most “official” option; otherwise use a client like Option 2 or a managed API like Option 3.

Why are my numbers different from the Google Trends website? Google samples searches and rounds to whole numbers, so two requests can differ by a few percent. Compare keywords within one consistent dataset rather than across separate downloads.

Is it legal to collect Google Trends data? Google Trends shows aggregated, anonymous search-interest statistics — no personal data. You’re responsible for how you use the data and for following Google’s terms.

Related guide: How to compare more than 5 keywords in Google Trends — the anchor-keyword method explained step by step.