Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

Pseudo API for Google Trends

Project description

# 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](#installation)

* [API](#api)

* [API Methods](#api-methods)

* [Common API parameters](#common-api-parameters)

* [Interest Over Time](#interest-over-time)
* [Interest by Region](#interest-by-region)
* [Related Queries](#related-queries)
* [Trending Searches](#trending-searches)
* [Top Charts](#top-charts)
* [Suggestions](#suggestions)

* [Caveats](#caveats)

* [Credits](#credits)

## Installation

pip install pytrends

## Requirements

* Written for both Python 2.7+ and Python 3.3+
* Requires a google account to use.
* Requires BeautifulSoup4, Requests, lxml, Pandas

<sub><sup>[back to top](#pytrends)</sub></sup>

## API

### Connect to Google

pytrends = TrendReq(google_username, google_password, hl='en-US', tz=360, custom_useragent=None)

Parameters

* `google_username`

- *Required*
- a valid gmail address

* `google_password`

- *Required*
- password for the gmail account

### Build Payload

pytrends = build_payload(kw_list, cat=0, timeframe='today 5-y', geo='', gprop='')

Parameters

* `kw_list`

- *Required*
- Keywords to get data for


<sub><sup>[back to top](#API)</sub></sup>

## API Methods

The following API methods are available:

* [Interest Over Time](#interest-over-time): returns historical, indexed data for when the keyword was searched most as shown on Google Trends' Interest Over Time section.

* [Interest by Region](#interest-by-region): returns data for where the keyword is most searched as shown on Google Trends' Interest by Region section.

* [Related Queries](#related-queries): returns data for the related keywords to a provided keyword shown on Google Trends' Related Queries section.

* [Trending Searches](#trending-searches): returns data for latest trending searches shown on Google Trends' Trending Searches section.

* [Top Charts](#top-charts): returns the data for a given topic shown in Google Trends' Top Charts section.

* [Suggestions](#suggestions): returns a list of additional suggested keywords that can be used to refine a trend search.

<sub><sup>[back to top](#api-methods)</sub></sup>

## 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 ```&```
- 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'`
- Single year, `'all_2008'`
- 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)

* `custom_useragent`

- name to identify requests coming from your script

<sub><sup>[back to top](#api-payload-keys)</sub></sup>

### Interest Over Time

pytrends.interest_over_time()

Returns pandas.Dataframe

<sub><sup>[back to top](#interest_over_time)</sub></sup>

### Interest by Region

pytrends.interest_by_region(resolution='COUNTRY')

Parameters

* `resolution`

- 'CITY' returns city level data
- 'COUNTRY' returns country level data

Returns pandas.DataFrame

<sub><sup>[back to top](#interest_by_region)</sub></sup>

### Related Queries

pytrends.related_queries()

Returns dictionary of pandas.DataFrames

<sub><sup>[back to top](#related_queries)</sub></sup>

### Trending Searches

pytrends.trending_searches()
Returns pandas.DataFrame

<sub><sup>[back to top](#trending_searches)</sub></sup>

### Top Charts

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

Parameters

* `date`

- *Required*
- YYYYMM integer or string value
- Example `'201611'` for November 2016 Top Chart data

* `cid`

- *Required*
- Topic to get data for
- Only able to choose from those listed on https://www.google.com/trends/topcharts
- Example the chart 'Baseketball players `cid` is `'basketball_players'`

Returns pandas.DataFrame

<sub><sup>[back to top](#top_charts)</sub></sup>

### Suggestions

pytrends.suggestions(keyword)

Parameters

* `keyword`

- *Required*
- keyword to get suggestions for

Returns dictionary

<sub><sup>[back to top](#suggestions)</sub></sup>

# 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 pubically known, let me know if you have a consistent estimate.

# Credits

* Major JSON revision ideas taken from pat310's JavaScript library

- https://github.com/pat310/google-trends-api

* Connecting to google code heavily based off Stack Overflow post

- http://stackoverflow.com/questions/6754709/logging-in-to-google-using-python

* With some ideas pulled from Matt Reid's Google Trends API

- https://bitbucket.org/mattreid9956/google-trend-api/overview


Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pytrends, version 4.1.1
Filename, size File type Python version Upload date Hashes
Filename, size pytrends-4.1.1-py2.py3-none-any.whl (11.1 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page