[Action]: GCP: Read from Google Sheet

[dougsillars]

Contact Details

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Action Name

GCP Read from Google Sheet

Action Inputs

sheetId
Range

Action Outputs

"google sheet imported"

Comments

Yes, you can also use the Google API google-api-python-client to read a Google Sheet as a dataframe in Python. Here are the steps:

Install the google-api-python-client library by running pip install google-api-python-client.

Go to the Google Developers Console and create a new project.

Enable the Google Sheets API by clicking on "Enable APIs and Services" and searching for "Google Sheets API".

Create a set of credentials by clicking on "Create credentials" and selecting "Service account key". Fill in the required information and download the JSON file containing the credentials.

Share the Google Sheet with the email address of the service account that you created in step 4.

In your Python code, import the necessary libraries:

python
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import pandas as pd
from google.oauth2 import service_account
from googleapiclient.discovery import build
Load the credentials from the JSON file:
python
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creds = service_account.Credentials.from_service_account_file('path/to/json/file.json')
Build the Google Sheets API service:
python
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service = build('sheets', 'v4', credentials=creds)
Define the spreadsheet ID and range that you want to read:
python
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spreadsheet_id = 'your-spreadsheet-id'
range_name = 'Sheet1!A1:C10'
Call the values().get() method of the Google Sheets API service to retrieve the data:
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result = service.spreadsheets().values().get(
spreadsheetId=spreadsheet_id,
range=range_name
).execute()
Convert the retrieved data into a Pandas dataframe:
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df = pd.DataFrame(result['values'][1:], columns=result['values'][0])
This will create a dataframe with the data from the specified range in the Google Sheet. Note that the first row of the range is assumed to be the column names of the dataframe. If your data doesn't have column names, you can modify the pd.DataFrame() call to specify the column names.

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