Extract, Transform, and Load Smartsheet Data in Python

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Smartsheet Python Connector

Python Connector Libraries for Smartsheet Data Connectivity. Integrate Smartsheet with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Smartsheet enables you to create ETL applications and pipelines for Smartsheet 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 Smartsheet and the petl framework, you can build Smartsheet-connected applications and pipelines for extracting, transforming, and loading Smartsheet data. This article shows how to connect to Smartsheet with the CData Python Connector and use petl and pandas to extract, transform, and load Smartsheet data.

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

Connecting to Smartsheet Data

Connecting to Smartsheet 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.

Smartsheet uses the OAuth authentication standard. To authenticate using OAuth, you will need to register an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties.

However, for testing purposes you can instead use the Personal Access Token you get when you create an application; set this to the OAuthAccessToken connection property.

After installing the CData Smartsheet Connector, follow the procedure below to install the other required modules and start accessing Smartsheet 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 Smartsheet 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.smartsheet as mod

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

cnxn = mod.connect("OAuthClientId=MyOauthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Smartsheet

Use SQL to create a statement for querying Smartsheet. In this article, we read data from the Sheet_Event_Plan_Budget entity.

sql = "SELECT TaskName, Progress FROM Sheet_Event_Plan_Budget WHERE Assigned = 'Ana Trujilo'"

Extract, Transform, and Load the Smartsheet Data

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

Loading Smartsheet Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Smartsheet

table1 = [ ['TaskName','Progress'], ['NewTaskName1','NewProgress1'], ['NewTaskName2','NewProgress2'], ['NewTaskName3','NewProgress3'] ]

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

With the CData Python Connector for Smartsheet, you can work with Smartsheet 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 Smartsheet Python Connector to start building Python apps and scripts with connectivity to Smartsheet 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.smartsheet as mod

cnxn = mod.connect("OAuthClientId=MyOauthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT TaskName, Progress FROM Sheet_Event_Plan_Budget WHERE Assigned = 'Ana Trujilo'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['TaskName','Progress'], ['NewTaskName1','NewProgress1'], ['NewTaskName2','NewProgress2'], ['NewTaskName3','NewProgress3'] ]

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