How to use SQLAlchemy ORM to access Excel Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Microsoft 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.

Connecting to Local or Cloud-Stored (Box, Google Drive, Amazon S3, SharePoint) Excel Files

CData Drivers let you work with Excel files stored locally and stored in cloud storage services like Box, Amazon S3, Google Drive, or SharePoint, right where they are.

Setting connection properties for local files

Set the URI property to local folder path.

Setting connection properties for files stored in Amazon S3

To connect to Excel file(s) within Amazon S3, set the URI property to the URI of the Bucket and Folder where the intended Excel files exist. In addition, at least set these properties:

• AWSAccessKey: AWS Access Key (username)
• AWSSecretKey: AWS Secret Key

Setting connection properties for files stored in Box

To connect to Excel file(s) within Box, set the URI property to the URI of the folder that includes the intended Excel file(s). Use the OAuth authentication method to connect to Box.

Dropbox

To connect to Excel file(s) within Dropbox, set the URI proprerty to the URI of the folder that includes the intended Excel file(s). Use the OAuth authentication method to connect to Dropbox. Either User Account or Service Account can be used to authenticate.

SharePoint Online (SOAP)

To connect to Excel file(s) within SharePoint with SOAP Schema, set the URI proprerty to the URI of the document library that includes the intended Excel file. Set User, Password, and StorageBaseURL.

SharePoint Online REST

To connect to Excel file(s) within SharePoint with REST Schema, set the URI proprerty to the URI of the document library that includes the intended Excel file. StorageBaseURL is optional. If not set, the driver will use the root drive. OAuth is used to authenticate.

Google Drive

To connect to Excel file(s) within Google Drive, set the URI property to the URI of the folder that includes the intended Excel file(s). Use the OAuth authentication method to connect and set InitiateOAuth to GETANDREFRESH.

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 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 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.

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("excel:///?URI='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:///?URI='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 CData Python Connector for Microsoft Excel to start building Python apps and scripts with connectivity to Excel data. Reach out to our Support Team if you have any questions.