DEV Community: TrendLab

How to get Google Trends data in Python in 2026 (now that pytrends is archived)

TrendLab — Fri, 06 Nov 2026 13:22:24 +0000

For years the answer to "how do I get Google Trends data in Python?" was pytrends. That's no longer a good answer:

The pytrends GitHub repo is archived. The last commit was in August 2024, and it has 150+ open issues.
The most common one is TooManyRequestsError / HTTP 429: Google rate-limits you after a handful of requests, especially from cloud servers.
Google still has no official public Google Trends API.

This post shows how to get the same data (interest over time, interest by region, related queries, comparisons and Trending Now) from Python in a few lines, without fighting 429s.

Disclosure: I built the tool used below, Google Trends Scraper - Fast, No Browser on Apify. It's paid per search, and Apify's free plan includes monthly credit, enough for roughly 2,500 searches.

Setup

Create a free account at apify.com (no card needed).
Copy your API token from Settings → API & Integrations.
Install the client:

pip install "apify-client>=3" pandas

Keep the token out of your code:

export APIFY_TOKEN="your-token-here"

1. Interest over time

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
actor = client.actor("vp916/google-trends-fast")

run = actor.call(run_input={
    "searchTerms": ["chatgpt"],
    "geo": "US",               # country code, or "" for worldwide
    "timeRange": "today 12-m", # past 12 months
})

item = next(client.dataset(run.default_dataset_id).iterate_items())

for point in item["interestOverTime"][:3]:
    print(point["date"], point["values"]["chatgpt"])
print("average:", item["averages"]["chatgpt"])

Each run returns one item per search line, with interestOverTime, averages, interestByRegion and relatedQueries already parsed.

2. Compare up to 5 terms

Put the terms on one line, separated by commas. That's the same as typing them into the Google Trends compare box, so all terms share one 0-100 scale:

run = actor.call(run_input={
    "searchTerms": ["iphone, samsung, pixel"],
    "geo": "IN",
    "timeRange": "today 3-m",
})
item = next(client.dataset(run.default_dataset_id).iterate_items())
print(item["averages"])   # {'iphone': ..., 'samsung': ..., 'pixel': ...}

Each separate line in searchTerms is a separate search. You can send hundreds in one run.

3. Interest by region: states, cities or US metros

run = actor.call(run_input={
    "searchTerms": ["taylor swift"],
    "geo": "US",
    "regionResolution": "DMA",   # COUNTRY, REGION, CITY or DMA (US metro areas)
    "includeInterestOverTime": False,
})
item = next(client.dataset(run.default_dataset_id).iterate_items())
top = sorted(item["interestByRegion"], key=lambda r: -(r["values"]["taylor swift"] or 0))[:5]
for r in top:
    print(r["geoName"], r["values"]["taylor swift"])

4. Related queries, including "Breakout"

rq = item["relatedQueries"]["taylor swift"]
print([q["query"] for q in rq["top"][:5]])
print([(q["query"], q["formattedValue"]) for q in rq["rising"][:5]])

rising is where the SEO gold is. "Breakout" means growth above 5,000%.

5. Trending Now for any country

run = actor.call(run_input={
    "trendingNowGeos": ["US", "IN"],
    "trendingNowHours": "24",     # 4, 24, 48 or 168
    "trendingNowMaxItems": 50,
})
for t in client.dataset(run.default_dataset_id).iterate_items():
    print(t["geo"], t["title"], t["searchVolume"], t["categories"])

Each trend comes with search volume, % increase, start time, whether it's still active, category and related searches.

6. Straight into pandas

Set outputFormat to "flat" and you get one row per data point, which loads directly into a DataFrame:

import pandas as pd

run = actor.call(run_input={
    "searchTerms": ["python, javascript, rust"],
    "timeRange": "today 5-y",
    "outputFormat": "flat",
})
df = pd.DataFrame(client.dataset(run.default_dataset_id).iterate_items())

ts = df[df["type"] == "interestOverTime"].pivot(index="date", columns="keyword", values="value")
print(ts.tail())
ts.plot(title="Search interest, worldwide, 5 years")

Why not just retry pytrends?

You can, and for 3 searches on your laptop it may work. It gets painful when you need hundreds of keywords, run it from a server, or want it on a schedule. That's the gap this fills. It runs on Apify's infrastructure, rotates IPs when Google slows down, and talks to Google's JSON endpoints directly instead of opening a browser, so 40 searches take under a minute.

What else would you want from a Google Trends API? I'm adding features based on requests.

What India is searching for before Diwali 2026 (Google Trends data)

TrendLab — Fri, 06 Nov 2026 13:21:04 +0000

Diwali is this Sunday, 8 November. I pulled Google Trends data for India to see what people are searching for in the run-up: when interest takes off, which states care most, and what people actually want to buy.

All numbers below are Google Trends interest scores. 100 is the peak for that search, place and time range, and everything else is relative to it. The data was collected on 4 November 2026, so the last day (3 November) is still partial.

1. Interest grew 4.5x in a month

On 1 October, "diwali" sat at 20. By 2 November it was at 90, and the partial day after that is already the 3-month peak.

The most common Diwali searches are not about shopping at all. They are people checking the date: "diwali 2026", "diwali date", and in Hindi, "diwali kab hai" and "diwali 2026 kab hai".

2. North and West India search the most

Haryana (100), Delhi (93), Daman and Diu (90) and Punjab (88) lead. Gujarat comes in at 65.

At the other end are Kerala (11), Mizoram (14), Meghalaya (20) and West Bengal (22). Bengal celebrates Kali Puja on the same night, which is one likely reason it searches for "Diwali" less.

3. What people want: outfits, gifts and an iPhone

These are the fastest-rising Diwali searches over the past 3 months:

Search	Growth
iphone 17 pro flipkart diwali sale	+1,300%
diwali outfit	+900%
diwali dress for women	+750%
diwali kurta for men	+700%
diwali gift ideas	+500%
diwali gift hampers	+500%
diwali decoration items	+450%

Gift searches show a clear budget and office angle. All of these are "Breakout" (Google's label for growth above +5,000%): "diwali gifts under 500", "diwali gifts for staff", "diwali office gifts" and "silver diwali gifts". Delhi searches for Diwali gifts the most, followed by Haryana and then Maharashtra.

Sale searches are mostly about timing: "when will diwali sale start", "flipkart diwali sale end date", "meesho diwali sale 2026" and "nykaa diwali sale" are all Breakout.

4. Rangoli peaked on Dussehra, not Diwali (yet)

Rangoli's biggest day so far was 20 October, Dussehra. Its rising searches confirm it: "dussehra special rangoli designs" and "dasara rangoli designs" are both Breakout.

In the last week rangoli and crackers have been neck and neck. Crackers led on 31 October and 1 November, and rangoli pulled ahead again on 2 November.

5. Gold searches doubled in a week

Dhanteras (Friday, 6 November) is the traditional day to buy gold, and it shows. "gold price" went from 54 on 29 October to 100 on 2 November.

"november 2 gold silver rates" became a trending search with 500K+ searches. Gold also trended in Hindi ("सोने", 20K+) and Malayalam ("സ്വര്ണം", 10K+), and "चांदी का सिक्का" (silver coin) trended too.

6. But cricket still wins

In the past 7 days Google listed 2,426 trending searches in India. 761 of them (31%) were sports, mostly cricket: "ind vs nz", "bangladesh vs india" and "mohammed siraj" each passed 2M+ searches. The single biggest search was weather ("मौसम", 5M+).

The festival still made the trending list, with "chhath puja" (10K+), "govardhan puja 2026" (5K+) and "bhai dooj 2026 date" (5K+).

How I got the data

I used a Google Trends scraper I built and published on Apify: Google Trends Scraper - Fast, No Browser. It pulls interest over time, interest by state, related queries and Trending Now in one run. This whole post took 4 searches plus one Trending Now run for India.

The input was:

{
  "searchTerms": [
    "diwali",
    "diwali gifts, diwali sale, rangoli, diwali decoration, crackers",
    "gold price, silver price",
    "diwali gifts"
  ],
  "geo": "IN",
  "timeRange": "today 3-m",
  "trendingNowGeos": ["IN"],
  "trendingNowHours": "168"
}

The charts are plain SVG drawn from the JSON output. If you'd like the Python for any of this, ask in the comments.

Happy Diwali! 🪔