This post demonstrate basic use of python to read/edit Google sheets. For fast setup, you can visit this link. Below is the setup procedure copied from the link itself.
- Use this wizard to create or select a project in the Google Developers Console and automatically turn on the API. Click Continue, then Go to credentials.
- On the Add credentials to your project page, click the Cancel button.
- At the top of the page, select the OAuth consent screen tab. Select an Email address, enter a Product name if not already set, and click the Save button.
- Select the Credentials tab, click the Create credentials button and select OAuth client ID.
- Select the application type Other, enter the name “Google Sheets API Quickstart”, and click the Create button.
- Click OK to dismiss the resulting dialog.
- Click the file_download (Download JSON) button to the right of the client ID.
- Move this file to your working directory and rename it client_secret.json.
The next step will be to install the google client using pip.
pip install --upgrade google-api-python-client
The final step is to copy the sample from the same link. For the first time running the script, you would need to sign in with Google. Use the below command to link the sheets credentials to the targeted gmail account. Follow the instruction as from the prompt.
$ python name_of_script.py --noauth_local_webserver
You can easily access/modify the contents of the sheets especially if it is in the table format by linking it with Python Pandas.
# authorization: reference from link credentials = get_credentials() http = credentials.authorize(httplib2.Http()) discoveryUrl = ('https://sheets.googleapis.com/$discovery/rest?' 'version=v4') service = discovery.build('sheets', 'v4', http=http, discoveryServiceUrl=discoveryUrl) # Target spreadsheet spreadsheetId = 'your_spreadsheet_name' rangeName = 'Sheet1!A1:N' # read from spreadsheet result = service.spreadsheets().values().get( spreadsheetId=spreadsheetId, range=rangeName).execute() values = result.get('values', ) import pandas # Pandas Dataframe with values and header data_df = pd.DataFrame(values[1:], columns = values) print data_df
- Automating Ms Powerpoint with Python: https://simply-python.com/2014/07/04/rapid-generation-of-powerpoint-report-with-template-scanning
- Using Excel with Python: https://simply-python.com/2014/08/20/manage-and-extract-data-using-python-and-excel-tables