Use SQLAlchemy ORMs to Access Excel Data in Python

Ready to get started?

Download for a free trial:

Download Now

Learn more:

Microsoft Excel Python Connector

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



The CData Python Connector for Excel enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Excel 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 and the SQLAlchemy toolkit, you can build Excel-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Excel data to query, update, delete, and insert Excel data.

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

Connecting to Excel Data

Connecting to Excel 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.

The ExcelFile, under the Authentication section, must be set to a valid Excel File.

Follow the procedure below to install SQLAlchemy and start accessing Excel 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 Data in Python

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

engine = create_engine("excel:///?Excel File='C:/MyExcelWorkbooks/SampleWorkbook.xlsx'")

Declare a Mapping Class for Excel 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 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(base):
	__tablename__ = "Sheet"
	Name = Column(String,primary_key=True)
	Revenue = Column(String)
	...

Query Excel 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("excel:///?Excel File='C:/MyExcelWorkbooks/SampleWorkbook.xlsx'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Sheet).filter_by(Name="Bob"):
	print("Name: ", instance.Name)
	print("Revenue: ", instance.Revenue)
	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_table = Sheet.metadata.tables["Sheet"]
for instance in session.execute(Sheet_table.select().where(Sheet_table.c.Name == "Bob")):
	print("Name: ", instance.Name)
	print("Revenue: ", instance.Revenue)
	print("---------")

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

Insert Excel Data

To insert Excel 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.

new_rec = Sheet(Name="placeholder", Name="Bob")
session.add(new_rec)
session.commit()

Update Excel Data

To update Excel 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.

updated_rec = session.query(Sheet).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Name = "Bob"
session.commit()

Delete Excel Data

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