erood / pytrends

Pseudo API for Google Trends

pytrends

About

Pseudo API for Google Trends

• Allows simple interface for automating downloads of csv reports from Google Trends.

• Main feature is to help trick google into thinking the script is actually a browser.

• Only good until Google changes their backend again :-P

Installation

pip install pytrends

Requirements

• Written for both Python 2.7+ and Python 3.3+
• Requires a google account to use.
• Requires fake-useragent python library (installed automatically with pip)

Connect to Google

pyGTrends(google_username, google_password)

Parameters

• google_username
  • a valid gmail address
• google_password
  • password for the gmail account

Request a Report

request_report(keywords, hl='en-US', cat=None, geo=None, date=None, use_topic=False)

Parameters

• Keywords
  • the words to get data for
  • Example "Pizza"
  • Up to five terms with a comma and space: "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".
  • To automate future downloads run it once manually to find the encoded topic. Find the encoded topic by inspecting the url. The topic starts after q= and ends before the next &.
  • 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 set use_topics=True
• hl
  • Language to return result headers in
  • Two letter language abbreviation
  • For example English is "en"
  • Defaults to english
• 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=0-71"
  • "0-71" is the category
  • Defaults to no category
• geo
  • Two letter country abbreviation
  • For example United States is "US"
  • Defaults to World
• date
  • Date to start from
  • Defaults to all available data, 2004 - present.
  • Custom Timeframe Pattern:
    • By Month: "MM/YYYY #m" where # is the number of months from that date to pull data for
      • For example: "10/2009 61m" would get data from October 2009 to October 2014
      • Less than 4 months will return Daily level data
      • More than 36 months will return monthly level data
      • 4-36 months will return weekly level data
  • 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-61m" would get data from today to 61months ago
      • 1-3 months will return daily intervals of data
      • 4-36 months will return weekly intervals of data
      • 36+ months will return monthly intervals of data
    • Daily: "today-#d" where # is the number of days from that date to pull data for
      • For example: "today-7d" would get data from the last week
      • 1 day will return 8min intervals of data
      • 2-8 days will return Hourly intervals of data
      • 8-90 days will return Daily level data
    • Hourly: "now-#H" where # is the number of hours from that date to pull data for
      • For example: "now-1H" would get data from the last hour
      • 1-3 hours will return 1min intervals of data
      • 4-26 hours will return 8min intervals of data
      • 27-34 hours will return 16min intervals of data
• use_topic
  • Used for Advanced Keywords
  • Set to True to avoid URLencoding the keywords

Save a Report to file

save_csv(path, trend_name)

Parameters

• path
  • Output path
• trend_name
  • Human readable name for file

Credits

• Connecting to google code heavily based off Sal Uryasev's pyGTrends

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

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