googlewrapper

General Connector Classes for Google Products

Updated version to work with Python >= 3.7

Current Wrappers Available

• Google Analytics
• Google Search Console
• Google Calendar
• Google Big Query
• Google PageSpeed API
• Google Sheets
• Google Docs

Wrappers In the Pipeline

• Gmail
• Google Maps

STEPS

1. Acquire Google Credentials from API Console
2. Install this package
3. Create Connection in Python
4. Use product wrapper to make API calls (see links to individual docs above)

Acquire Google Credentials from API Console

First we will need to get our own Google Project set up so we can get our credentials. If you don't have experience, you can do so here Google API Console

After you have your project set up, oAuth configured, and the optional service account (only for Google Big Query connections), you are good to install this package.

Make sure to download your oAuth credentials and save them to your working directory as 'client_secret.json'.

Installation

pip install googlewrapper

OR

python -m pip install googlewrapper

Virtual Environment

For each project it is reccomended to create a virtualenv. Here is a simple guide on virtual environments.

Combining Products Examples

Example 1

Take a list of URLs from Sheets, grab Search Console Data, and import it into Big Query.

from googlewrapper import GoogleSearchConsole, GoogleSheets, GoogleBigQuery
import datetime as dt

# init our objects
sheets = GoogleSheets(YOUR_URL_HERE)
gsc = GoogleSearchConsole()
gbq = GoogleBigQuery()

# get our urls we want to pull
# remember that sheet1 is default
sites = sheets.get_column(1)

'''
this one is a bit more technical
we can pull our column Branded Words right 
from sheets then assign it to a dictionary to use
in our GSC object.

Make sure that your url column is the index for 
your df. This will happen by default if the urls
are in the first column in google sheets
'''
branded_list = sheets.df()['Branded Words'].to_dict()

# assign those sheets to GSC
gsc.set_sites(sites)
# assign other GSC variables
gsc.set_date(dt.date(2021,1,1))
gsc.set_dims(['page','date','query'])

# get our data
gsc_data = gsc.get_data()

# print the total clicks/impressions and avg position
# for all the sites we just pulled data for
# send them to Big Query
for site in gsc_data:
  print(f"{site}'s Data\n"\
      f"Clicks: {gsc_data[site]['Clicks'].sum()}\n"\
      f"Impressions: {gsc_data[site]['Impressions'].sum()}\n"\
      f"Avg Position: {gsc_data[site]['Position'].mean()}\n\n")
  # now we will send our data into our GBQ tables for storage
  # we will assign the dataset name to be our url
  # we will assign table to be gsc
  gbq.set_dataset(site)
  gbq.set_table('gsc')
  # send the data to GBQ
  gbq.send(gsc_data[site])

Pull Requests/Suggestions

I'd love to hear your feedback and suggestions. If you see something and you want to give it a shot and fix it, feel free to clone and make a pull request. OR you can submit and issue/feature request on GitHub.

Thanks for using my code

If you found this library useful, I'd appreciate a coffee. Thanks.