Use SQLAlchemy ORMs to Access Box Data in Python

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Box Python Connector

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

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

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

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

Connecting to Box Data

Connecting to Box 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.

Box uses the OAuth standard to authenticate. To authenticate to Box, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

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

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

Declare a Mapping Class for Box 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 Files 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 Files(base):
	__tablename__ = "Files"
	Name = Column(String,primary_key=True)
	Size = Column(String)

Query Box 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("box:///?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(Files).filter_by(Id="123"):
	print("Name: ", instance.Name)
	print("Size: ", instance.Size)

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

Using the execute Method

Files_table = Files.metadata.tables["Files"]
for instance in session.execute( == "123")):
	print("Name: ", instance.Name)
	print("Size: ", instance.Size)

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

Insert Box Data

To insert Box 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 Box.

new_rec = Files(Name="placeholder", Id="123")

Update Box Data

To update Box 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 Box.

updated_rec = session.query(Files).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Id = "123"

Delete Box Data

To delete Box 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(Files).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

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