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pytrends

Introduction

Unofficial API for Google Trends

Allows simple interface for automating downloading of reports from Google Trends. Main feature is to allow the script to login to Google on your behalf to enable a higher rate limit. Only good until Google changes their backend again :-P. When that happens feel free to contribute!

Table of contens

Installation

pip install pytrends

Requirements

  • Written for both Python 2.7+ and Python 3.3+
  • Requires Requests, lxml, Pandas

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API

Connect to Google

from pytrends.request import TrendReq

pytrends = TrendReq(hl='en-US', tz=360)

or if you want to use proxies as you are blocked due to Google rate limit:

from pytrends.request import TrendReq

pytrends = TrendReq(hl='en-US', tz=360, proxies = {'https': 'https://34.203.233.13:80'})

Note: only https proxies will work, and you need to add the port number after the proxy ip address

Build Payload

kw_list = ["Blockchain"]
pytrends.build_payload(kw_list, cat=0, timeframe='today 5-y', geo='', gprop='')

Parameters

  • kw_list

    • Required
    • Keywords to get data for

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API Methods

The following API methods are available:

  • Interest Over Time: returns historical, indexed data for when the keyword was searched most as shown on Google Trends' Interest Over Time section.

  • Historical Hourly Interest: returns historical, indexed, hourly data for when the keyword was searched most as shown on Google Trends' Interest Over Time section. It sends multiple requests to Google, each retrieving one week of hourly data. It seems like this would be the only way to get historical, hourly data.

  • Interest by Region: returns data for where the keyword is most searched as shown on Google Trends' Interest by Region section.

  • Related Topics: returns data for the related keywords to a provided keyword shown on Google Trends' Related Topics section.

  • Related Queries: returns data for the related keywords to a provided keyword shown on Google Trends' Related Queries section.

  • Trending Searches: returns data for latest trending searches shown on Google Trends' Trending Searches section.

  • Top Charts: returns the data for a given topic shown in Google Trends' Top Charts section.

  • Suggestions: returns a list of additional suggested keywords that can be used to refine a trend search.

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Common API parameters

Many API methods use the following:

  • kw_list

    • keywords to get data for

    • Example ['Pizza']

    • Up to five terms in a list: ['Pizza', 'Italian', 'Spaghetti', 'Breadsticks', 'Sausage']

      • Advanced Keywords

        • When using Google Trends dashboard Google may provide suggested narrowed search terms.
        • For example "iron" will have a drop down of "Iron Chemical Element, Iron Cross, Iron Man, etc".
        • Find the encoded topic by using the get_suggestions() function and choose the most relevant one for you.
        • For example: https://www.google.com/trends/explore#q=%2Fm%2F025rw19&cmpt=q
        • "%2Fm%2F025rw19" is the topic "Iron Chemical Element" to use this with pytrends
        • You can also use pytrends.suggestions() to automate this.

  • cat

    • Category to narrow results
    • Find available cateogies by inspecting the url when manually using Google Trends. The category starts after cat= and ends before the next & or view this wiki page containing all available categories
    • For example: "https://www.google.com/trends/explore#q=pizza&cat=71"
    • '71' is the category
    • Defaults to no category

  • geo

    • Two letter country abbreviation
    • For example United States is 'US'
    • Defaults to World
    • More detail available for States/Provinces by specifying additonal abbreviations
    • For example: Alabama would be 'US-AL'
    • For example: England would be 'GB-ENG'

  • tz

    • Timezone Offset
    • For example US CST is '360'

  • timeframe

    • Date to start from

    • Defaults to last 5yrs, 'today 5-y'.

    • Everything 'all'

    • Specific dates, 'YYYY-MM-DD YYYY-MM-DD' example '2016-12-14 2017-01-25'

    • Specific datetimes, 'YYYY-MM-DDTHH YYYY-MM-DDTHH' example '2017-02-06T10 2017-02-12T07'

      • Note Time component is based off UTC

    • Current Time Minus Time Pattern:

      • By Month: 'today #-m' where # is the number of months from that date to pull data for

        • For example: 'today 3-m' would get data from today to 3months ago
        • NOTE Google uses UTC date as 'today'
        • Seems to only work for 1, 2, 3 months only

      • Daily: 'now #-d' where # is the number of days from that date to pull data for

        • For example: 'now 7-d' would get data from the last week
        • Seems to only work for 1, 7 days only

      • Hourly: 'now #-H' where # is the number of hours from that date to pull data for

        • For example: 'now 1-H' would get data from the last hour
        • Seems to only work for 1, 4 hours only

  • gprop

    • What Google property to filter to
    • Example 'images'
    • Defaults to web searches
    • Can be images, news, youtube or froogle (for Google Shopping results)

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Interest Over Time

pytrends.interest_over_time()

Returns pandas.Dataframe

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Historical Hourly Interest

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)

Parameters

  • kw_list

    • Required
    • list of keywords that you would like the historical data

  • year_start, month_start, day_start, hour_start, year_end, month_end, day_end, hour_end

    • the time period for which you would like the historical data

  • sleep

    • If you are rate-limited by Google, you should set this parameter to something (i.e. 60) to space off each API call.

Returns pandas.Dataframe

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Interest by Region

pytrends.interest_by_region(resolution='COUNTRY', inc_low_vol=True, inc_geo_code=False)

Parameters

  • resolution

    • 'CITY' returns city level data
    • 'COUNTRY' returns country level data
    • 'DMA' returns Metro level data
    • 'REGION' returns Region level data

  • inc_low_vol

    • True/False (includes google trends data for low volume countries/regions as well)

  • inc_geo_code

    • True/False (includes ISO codes of countries along with the names in the data)

Returns pandas.DataFrame

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Related Topics

pytrends.related_topics()

Returns dictionary of pandas.DataFrames

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Related Queries

pytrends.related_queries()

Returns dictionary of pandas.DataFrames

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Trending Searches

pytrends.trending_searches(pn='p1') # in English
pytrends.trending_searches(pn='p4') # in Japanese

Returns pandas.DataFrame

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Top Charts

pytrends.top_charts(date, cid, geo='US', cat='')

Parameters

  • date

    • Required
    • YYYYMM integer or string value
    • Example '201611' for November 2016 Top Chart data

  • cid

Returns pandas.DataFrame

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Suggestions

pytrends.suggestions(keyword)

Parameters

  • keyword

    • Required
    • keyword to get suggestions for

Returns dictionary

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Categories

pytrends.categories()

Returns dictionary

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Caveats

  • This is not an official or supported API
  • Google may change aggregation level for items with very large or very small search volume
  • Google will send you an email saying that you had a new login after running this.
  • Rate Limit is not publicly known, let me know if you have a consistent estimate
    • One user reports that 1,400 sequential requests of a 4 hours timeframe got them to the limit. (Replicated on 2 networks)
    • It has been tested, and 60 seconds of sleep between requests (successful or not) is the correct amount once you reach the limit.
  • For certain configurations the dependency lib certifi requires the environment variable REQUESTS_CA_BUNDLE to be explicitly set and exported. This variable must contain the path where the ca-certificates are saved or a SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] error is given at runtime.

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Pseudo API for Google Trends

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