Install pytrends via pip
masterInstall the pytrends package using pip.
pip install pytrendspytrends
repository·master·Indexed 25 days ago
https://github.com/generalmills/pytrendsAn unofficial Python API for Google Trends (version 4.9.2) that allows users to automate the downloading of interest reports, trending searches, and related queries. It provides tools to fetch interest over time, interest by region, related topics, and real-time search trends, returning data primarily as pandas DataFrames. The library includes the TrendReq client for connection management, payload building for queries, and specialized functions like get_daily_data() for scaled daily search volume.
What's inside pytrends
Get Realtime Search Trends
masterReturns realtime search trends for a specific country code (
pn). Returns apandas.DataFrame.pytrends.realtime_trending_searches(pn='US')Get Interest Over Time
masterReturns historical, indexed data for when the keyword was searched most, as shown in the Google Trends 'Interest Over Time' section. Returns a
pandas.DataFrame.pytrends.interest_over_time()Initialize TrendReq to connect to Google
masterUse
TrendReqfrompytrends.requestto establish a connection. You can specify the host language (hl) and the timezone offset (tz).from pytrends.request import TrendReq pytrends = TrendReq(hl='en-US', tz=360)Get Multirange Interest Over Time
masterReturns historical, indexed data across multiple time date ranges. The first row of the returned
pandas.DataFrameincludes the average. You must first callbuild_payloadwith a list of timeframes.pytrends.build_payload(kw_list=['pizza', 'bagel'], timeframe=['2022-09-04 2022-09-10', '2022-09-18 2022-09-24']) pytrends.multirange_interest_over_time()Build a payload for Google Trends
masterBefore calling most API methods, you must build a payload using
build_payload. This defines the keywords and filters for the subsequent request.kw_list = ["Blockchain"] pytrends.build_payload(kw_list, cat=0, timeframe='today 5-y', geo='', gprop='')Get Historical Hourly Interest
masterReturns historical, indexed, hourly data. This method sends multiple requests to Google (one per week). Use the
sleepparameter to avoid rate limits.pytrends.get_historical_interest(kw_list, year_start=2018, month_start=1, day_start=1, hour_start=0, year_end=2018, month_end=2, day_end=1, hour_end=0, cat=0, geo='', gprop='', sleep=0)Get Top Charts
masterReturns the data for a given year. Note that Google does not return data for the current year and does not support monthly queries. Returns a
pandas.DataFrame.pytrends.top_charts(date, hl='en-US', tz=300, geo='GLOBAL')Get Related Queries
masterReturns data for the related queries to a provided keyword. Returns a dictionary of
pandas.DataFrames.pytrends.related_queries()Get Related Topics
masterReturns data for the related topics to a provided keyword. Returns a dictionary of
pandas.DataFrames.pytrends.related_topics()Configure TrendReq with proxies and timeouts
masterIf you encounter Google rate limits, you can initialize
TrendReqwith proxies, timeouts, retries, and backoff factors. Note that onlyhttpsproxies are supported and must include the port number.from pytrends.request import TrendReq pytrends = TrendReq(hl='en-US', tz=360, timeout=(10,25), proxies=['https://34.203.233.13:80',], retries=2, backoff_factor=0.1, requests_args={'verify':False})Get Keyword Suggestions
masterReturns a list of additional suggested keywords that can be used to refine a trend search. Returns a dictionary.
pytrends.suggestions(keyword)