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A fork of TrendsPy which does not use pandas/numpy
Explore
interest_over_time
)interest_by_region
)related_queries
, related_topics
)Trending Now
trending_now
, trending_now_by_rss
)trending_now_news_by_ids
)trending_now_showcase_timeline
)Search Utilities
categories
)geo
)Flexible Time Formats
'now 123-H'
, 'today 45-d'
'2024-02-01 10-d'
'2024-01-01 2024-12-31'
pip install trendspy
from trendspy import Trends
tr = Trends()
df = tr.interest_over_time(['python', 'javascript'])
df.plot(title='Python vs JavaScript Interest Over Time',
figsize=(12, 6))
# Analyze geographic distribution
geo_df = tr.interest_by_region('python')
# Get related queries
related = tr.related_queries('python')
# Find technology-related categories
categories = tr.categories(find='technology')
# Output: [{'name': 'Computers & Electronics', 'id': '13'}, ...]
# Search for locations
locations = tr.geo(find='york')
# Output: [{'name': 'New York', 'id': 'US-NY'}, ...]
# Use in queries
df = tr.interest_over_time(
'python',
geo='US-NY', # Found location ID
cat='13' # Found category ID
)
# Get current trending searches in the US
trends = tr.trending_now(geo='US')
# Get trending searches with news articles
trends_with_news = tr.trending_now_by_rss(geo='US')
print(trends_with_news[0]) # First trending topic
print(trends_with_news[0].news[0]) # Associated news article
# Get news articles for specific trending topics
news = tr.trending_now_news_by_ids(
trends[0].news_tokens, # News tokens from trending topic
max_news=3 # Number of articles to retrieve
)
for article in news:
print(f"Title: {article.title}")
print(f"Source: {article.source}")
print(f"URL: {article.url}\n")
from trendspy import BatchPeriod
# Unlike standard interest_over_time where data is normalized across all keywords,
# trending_now_showcase_timeline provides independent data for each keyword
# (up to 500+ keywords in a single request)
keywords = ['keyword1', 'keyword2', ..., 'keyword500']
# Get independent historical data
df_24h = tr.trending_now_showcase_timeline(
keywords,
timeframe=BatchPeriod.Past24H # 16-minute intervals
)
# Each keyword's data is normalized only to itself
df_24h.plot(
subplots=True,
layout=(5, 2),
figsize=(15, 20),
title="Independent Trend Lines"
)
# Available time windows:
# - Past4H: ~30 points (8-minute intervals)
# - Past24H: ~90 points (16-minute intervals)
# - Past48H: ~180 points (16-minute intervals)
# - Past7D: ~42 points (4-hour intervals)
# Country-level data
country_df = tr.interest_by_region('python')
# State-level data for the US
state_df = tr.interest_by_region(
'python',
geo='US',
resolution='REGION'
)
# City-level data for California
city_df = tr.interest_by_region(
'python',
geo='US-CA',
resolution='CITY'
)
'now 1-H'
, 'now 4-H'
, 'today 1-m'
, 'today 3-m'
, 'today 12-m'
'now 123-H'
, 'now 72-H'
'today 45-d'
, 'today 90-d'
, 'today 18-m'
'2024-02-01 10-d'
, '2024-03-15 3-m'
'2024-01-01 2024-12-31'
'2024-03-25T12 2024-03-25T15'
(for periods < 8 days)'all'
Compare search interest across different time periods and regions:
# Compare different time periods
timeframes = [
'2024-01-25 12-d', # 12-day period
'2024-06-20 23-d' # 23-day period
]
geo = ['US', 'GB'] # Compare US and UK
df = tr.interest_over_time(
'python',
timeframe=timeframes,
geo=geo
)
Note: When using multiple timeframes, they must maintain consistent resolution and the maximum timeframe cannot be more than twice the length of the minimum timeframe.
TrendsPy supports the same proxy configuration as the requests
library:
# Initialize with proxy
tr = Trends(proxy="http://user:pass@10.10.1.10:3128")
# or
tr = Trends(proxy={
"http": "http://10.10.1.10:3128",
"https": "http://10.10.1.10:1080"
})
# Configure proxy after initialization
tr.set_proxy("http://10.10.1.10:3128")
For more examples and detailed API documentation, check out the Jupyter notebook in the repository: basic_usage.ipynb
MIT License - see the LICENSE file for details.
This library is not affiliated with Google. Please ensure compliance with Google's terms of service when using this library.
FAQs
A Python library for accessing Google Trends data
We found that trendspy-lite demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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