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Extract, Transform, and Load Google Sheets Data in Python

The CData Python Connector for Google Sheets enables you to create ETL applications and pipelines for Google Sheets data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Google Sheets and the petl framework, you can build Google Sheets-connected applications and pipelines for extracting, transforming, and loading Google Sheets data. This article shows how to connect to Google Sheets with the CData Python Connector and use petl and pandas to extract, transform, and load Google Sheets data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Google Sheets data in Python. When you issue complex SQL queries from Google Sheets, the driver pushes supported SQL operations, like filters and aggregations, directly to Google Sheets and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Google Sheets Data

Connecting to Google Sheets data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

You can connect to a spreadsheet by providing authentication to Google and then setting the Spreadsheet connection property to the name or feed link of the spreadsheet. If you want to view a list of information about the spreadsheets in your Google Drive, execute a query to the Spreadsheets view after you authenticate.

ClientLogin (username/password authentication) has been officially deprecated since April 20, 2012 and is now no longer available. Instead, use the OAuth 2.0 authentication standard. To access Google APIs on behalf on individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

See the Getting Started chapter in the help documentation to connect to Google Sheets from different types of accounts: Google accounts, Google Apps accounts, and accounts using two-step verification.

After installing the CData Google Sheets Connector, follow the procedure below to install the other required modules and start accessing Google Sheets through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Google Sheets Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.googlesheets as mod

You can now connect with a connection string. Use the connect function for the CData Google Sheets Connector to create a connection for working with Google Sheets data.

cnxn = mod.connect("Spreadsheet=MySheet;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Google Sheets

Use SQL to create a statement for querying Google Sheets. In this article, we read data from the Orders entity.

sql = "SELECT Shipcountry, OrderPrice FROM Orders WHERE ShipCity = 'Madrid'"

Extract, Transform, and Load the Google Sheets Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Google Sheets data. In this example, we extract Google Sheets data, sort the data by the OrderPrice column, and load the data into a CSV file.

Loading Google Sheets Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'OrderPrice')

etl.tocsv(table2,'orders_data.csv')

In the following example, we add new rows to the Orders table.

Adding New Rows to Google Sheets

table1 = [ ['Shipcountry','OrderPrice'], ['NewShipcountry1','NewOrderPrice1'], ['NewShipcountry2','NewOrderPrice2'], ['NewShipcountry3','NewOrderPrice3'] ]

etl.appenddb(table1, cnxn, 'Orders')

With the CData Python Connector for Google Sheets, you can work with Google Sheets data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the Google Sheets Python Connector to start building Python apps and scripts with connectivity to Google Sheets data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.googlesheets as mod

cnxn = mod.connect("Spreadsheet=MySheet;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Shipcountry, OrderPrice FROM Orders WHERE ShipCity = 'Madrid'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'OrderPrice')

etl.tocsv(table2,'orders_data.csv')

table3 = [ ['Shipcountry','OrderPrice'], ['NewShipcountry1','NewOrderPrice1'], ['NewShipcountry2','NewOrderPrice2'], ['NewShipcountry3','NewOrderPrice3'] ]

etl.appenddb(table3, cnxn, 'Orders')