Use SQLAlchemy ORMs to Access Excel Services Data in Python

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Excel Services Python Connector

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



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

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

Connecting to Excel Services Data

Connecting to Excel Services 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 URL, User, and Password properties, under the Authentication section, must be set to valid credentials for SharePoint Online, SharePoint 2010, or SharePoint 2013. Additionally, the Library property must be set to a valid SharePoint Document Library and the File property must be set to a valid .xlsx file in the indicated Library.

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

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

engine = create_engine("excelservices:///?URL=https://myorg.sharepoint.com&User=admin@myorg.onmicrosoft.com&Password=password&File=Book1.xlsx")

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

Query Excel Services 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("excelservices:///?URL=https://myorg.sharepoint.com&User=admin@myorg.onmicrosoft.com&Password=password&File=Book1.xlsx")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Account).filter_by(Industry="Floppy Disks"):
	print("Name: ", instance.Name)
	print("AnnualRevenue: ", instance.AnnualRevenue)
	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

Account_table = Account.metadata.tables["Account"]
for instance in session.execute(Account_table.select().where(Account_table.c.Industry == "Floppy Disks")):
	print("Name: ", instance.Name)
	print("AnnualRevenue: ", instance.AnnualRevenue)
	print("---------")

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

Insert Excel Services Data

To insert Excel Services 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 Services.

new_rec = Account(Name="placeholder", Industry="Floppy Disks")
session.add(new_rec)
session.commit()

Update Excel Services Data

To update Excel Services 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 Services.

updated_rec = session.query(Account).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Industry = "Floppy Disks"
session.commit()

Delete Excel Services Data

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