Usage
Query Google Trends: interest over time, interest by region, related queries, and exports — with runnable examples.
Usage
import trendflow
from trendflow import Region, Timeframe, Resolution, ExportFormat
# Initialize client (optional API config)
tf = trendflow.Client(language="en", timeout=10)
# --- Enums for type safety ---
# Region.US, Region.GB, Region.DE ...
# Timeframe.PAST_DAY, Timeframe.PAST_WEEK, Timeframe.PAST_YEAR, Timeframe.PAST_5_YEARS
# Resolution.COUNTRY, Resolution.REGION, Resolution.CITY
# Fetch interest over time
data = tf.interest_over_time(
keywords=["Python", "JavaScript", "Rust"],
timeframe=Timeframe.PAST_YEAR,
region=Region.US,
)
# Dataclass-backed results
print(data.keywords) # ["Python", "JavaScript", "Rust"]
print(data.granularity) # "weekly"
print(data.points) # list of TrendPoint(date, scores: dict)
# Get regional breakdown (region defaults to Region.US)
regional = tf.interest_by_region(
keyword="Python",
resolution=Resolution.COUNTRY,
)
# Trending searches right now
trending = tf.trending_now(region=Region.US)
for item in trending.results:
print(item.title, item.traffic, item.articles) # TrendingItem dataclass
# Related queries — returns RelatedResult dataclass
related = tf.related_queries("machine learning")
for query in related.top:
print(query.term, query.value) # RelatedQuery(term, value)
for query in related.rising:
print(query.term, query.breakout) # RelatedQuery(term, breakout%)
# --- Exports ---
data.export(ExportFormat.CSV, path="trends.csv")
data.export(ExportFormat.JSON, path="trends.json")
data.to_dataframe() # pandas DataFrame