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Explore relative search interest over time and across regions for any topic or keyword using Google Trends. Commonly used in data engineering pipelines for demand forecasting, seasonality analysis, and enriching product or marketing datasets with search trend signals via the pytrends Python library.
The `pytrends` library provides an unofficial Python API for Google Trends. Engineers use `pytrends.build_payload()` and `interest_over_time()` to collect normalized search interest time-series. Rate limiting requires batching and delays between requests.
Google Trends data is a powerful feature for AI demand forecasting models — search interest often leads actual demand by days or weeks. Incorporate trends data as an exogenous variable in LLM-based forecasting pipelines, or build a RAG system that retrieves current trend signals to contextualize market analysis.
# pip install pytrends pandas
from pytrends.request import TrendReq
import pandas as pd
pytrends = TrendReq(hl="en-US", tz=360)
pytrends.build_payload(["data engineering", "data science"], timeframe="today 12-m")
df = pytrends.interest_over_time()
print(df.tail(5))Official dataset source
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