Pseudo API for Google Trends
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Allows simple interface for automating downloads of csv reports from Google Trends.
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Main feature is to help trick google into thinking the script is actually a browser.
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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)
pyGTrends(google_username, google_password)
Parameters
- google_username
- a valid gmail address
- google_password
- password for the gmail account
request_report(keywords, hl='en-US', cat=None, geo=None, date=None)
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". - 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
- 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
- tz
- Timezone using Etc/GMT
- For example US CST is
"Etc/GMT+5"
- 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
- For example:
- By Month:
- 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 61-m"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
- For example:
- Daily:
"today #-d"where # is the number of days from that date to pull data for- For example:
"today 7-d"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
- For example:
- 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 - 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
- For example:
- By Month:
save_csv(path, trend_name)
Parameters
- path
- Output path
- trend_name
- Human readable name for file
get_suggestions(keyword)
Parameters
- keyword
- keyword to get suggestions for
Returns JSON
{"default": {"topics": [{"mid": "/m/0663v","title": "Pizza","type": "Dish"}]}}
- Use the
midvalue for the keyword in future searches for a more refined trend set
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Connecting to google code heavily based off Sal Uryasev's pyGTrends
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With some ideas pulled from Matt Reid's Google Trends API