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

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How to use SQLAlchemy ORM to access Smartsheet Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Smartsheet data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Smartsheet and the SQLAlchemy toolkit, you can build Smartsheet-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Smartsheet data to query, update, delete, and insert 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 CData Connector 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.

Follow the procedure below to install SQLAlchemy and start accessing Smartsheet through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model Smartsheet Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Smartsheet data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("smartsheet:///?OAuthClientId=MyOauthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Smartsheet Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Sheet_Event_Plan_Budget table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base() class Sheet_Event_Plan_Budget(base): __tablename__ = "Sheet_Event_Plan_Budget" TaskName = Column(String,primary_key=True) Progress = Column(String) ...

Query Smartsheet Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("smartsheet:///?OAuthClientId=MyOauthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Sheet_Event_Plan_Budget).filter_by(Assigned="Ana Trujilo"): print("TaskName: ", instance.TaskName) print("Progress: ", instance.Progress) print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Sheet_Event_Plan_Budget_table = Sheet_Event_Plan_Budget.metadata.tables["Sheet_Event_Plan_Budget"] for instance in session.execute(Sheet_Event_Plan_Budget_table.select().where(Sheet_Event_Plan_Budget_table.c.Assigned == "Ana Trujilo")): print("TaskName: ", instance.TaskName) print("Progress: ", instance.Progress) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Smartsheet Data

To insert Smartsheet data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Smartsheet.

new_rec = Sheet_Event_Plan_Budget(TaskName="placeholder", Assigned="Ana Trujilo") session.add(new_rec) session.commit()

Update Smartsheet Data

To update Smartsheet data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Smartsheet.

updated_rec = session.query(Sheet_Event_Plan_Budget).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Assigned = "Ana Trujilo" session.commit()

Delete Smartsheet Data

To delete Smartsheet data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Sheet_Event_Plan_Budget).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

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