pytrends

Introduction

Unofficial API for Google Trends

Allows simple interface for automating downloading of reports from Google Trends. Only good until Google changes their backend again :-P. When that happens feel free to contribute!

Looking for maintainers! Please open an issue with a method of contacting you if you're interested.

Installation
API
Credits

Installation

pip install pytrends

Requirements

Written for 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, timeout=(10,25), proxies=['https://34.203.233.13:80',], retries=2, backoff_factor=0.1, requests_args={'verify':False})

timeout(connect, read)
- See explantation on this on requests docs
tz
- Timezone Offset
- For example US CST is '360' (note NOT -360, Google uses timezone this way...)
proxies
- https proxies Google passed ONLY
- list ['https://34.203.233.13:80','https://35.201.123.31:880', ..., ...]
retries
- number of retries total/connect/read all represented by one scalar
backoff_factor
- A backoff factor to apply between attempts after the second try (most errors are resolved immediately by a second try without a delay). urllib3 will sleep for: {backoff factor} * (2 ^ ({number of total retries} - 1)) seconds. If the backoff_factor is 0.1, then sleep() will sleep for [0.0s, 0.2s, 0.4s, …] between retries. It will never be longer than Retry.BACKOFF_MAX. By default, backoff is disabled (set to 0).
requests_args
- A dict with additional parameters to pass along to the underlying requests library, for example verify=False to ignore SSL errors

Note: the parameter hl specifies host language for accessing Google Trends. 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.
Multirange Interest Over Time: returns historical, indexed data similar to interest over time, but across multiple time date ranges.
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
    - "/m/025rw19" 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 categories 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 additional abbreviations
- For example: Alabama would be 'US-AL'
- For example: England would be 'GB-ENG'
- Can also be a list of up to five regions
tz
- Timezone Offset (in minutes)
- For more information of Timezone Offset, view this wiki page containing about UCT 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'
    - Works for 1, 3, 12 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
    - Works 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
    - Works 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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Multirange Interest Over Time

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()

Returns pandas.Dataframe. It includes the average in the first row.

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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 Queries

pytrends.related_queries()

Returns dictionary of pandas.DataFrames

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Pytrends

Install / Use

README

pytrends

Introduction

Table of Contents

Installation

Requirements

API

Connect to Google

Build Payload

API Methods

Common API parameters

Interest Over Time

Multirange Interest Over Time

Historical Hourly Interest

Interest by Region

Related Topics

Related Queries

Trending Searches

Related Skills