Use SQLAlchemy ORMs to Access Excel Online Data in Python

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Excel Online Python Connector

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



The CData Python Connector for Excel Online enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Excel Online data.

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

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

Connecting to Excel Online Data

Connecting to Excel Online 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 workbook by providing authentication to Excel Online and then setting the following properties:

  • Workbook: Set this to the name or Id of the workbook.

    If you want to view a list of information about the available workbooks, execute a query to the Workbooks view after you authenticate.

  • UseSandbox: Set this to true if you are connecting to a workbook in a sandbox account. Otherwise, leave this blank to connect to a production account.

You use the OAuth authentication standard to authenticate to Excel Online. See the Getting Started section in the help documentation for a guide. Getting Started also guides you through executing SQL to worksheets and ranges.

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

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Excel Online Data in Python

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

engine = create_engine("excelonline:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Excel Online 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 Test_xlsx_Sheet1 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 Test_xlsx_Sheet1(base):
	__tablename__ = "Test_xlsx_Sheet1"
	Id = Column(String,primary_key=True)
	Column1 = Column(String)
	...

Query Excel Online 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("excelonline:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Test_xlsx_Sheet1).filter_by(Column2="Bob"):
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	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

Test_xlsx_Sheet1_table = Test_xlsx_Sheet1.metadata.tables["Test_xlsx_Sheet1"]
for instance in session.execute(Test_xlsx_Sheet1_table.select().where(Test_xlsx_Sheet1_table.c.Column2 == "Bob")):
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	print("---------")

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

Insert Excel Online Data

To insert Excel Online 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 Excel Online.

new_rec = Test_xlsx_Sheet1(Id="placeholder", Column2="Bob")
session.add(new_rec)
session.commit()

Update Excel Online Data

To update Excel Online 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 Excel Online.

updated_rec = session.query(Test_xlsx_Sheet1).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Column2 = "Bob"
session.commit()

Delete Excel Online Data

To delete Excel Online 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(Test_xlsx_Sheet1).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 Excel Online Python Connector to start building Python apps and scripts with connectivity to Excel Online data. Reach out to our Support Team if you have any questions.