How to use SQLAlchemy ORM to access Wave Financial Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Wave Financial data.

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

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

Connecting to Wave Financial Data

Connecting to Wave Financial 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.

Connect using the API Token

You can connect to Wave Financial by specifying the APIToken You can obtain an API Token using the following steps:

Log in to your Wave account and navigate to "Manage Applications" in the left pane.
Select the application that you would like to create a token for. You may need to create an application first.
Click the "Create token" button to generate an APIToken.

Connect using OAuth

If you wish, you can connect using the embedded OAuth credentials. See the Help documentation for more information.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Wave Financial 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("wavefinancial:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Wave Financial 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 Invoices 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 Invoices(base):
	__tablename__ = "Invoices"
	Id = Column(String,primary_key=True)
	DueDate = Column(String)
	...

Query Wave Financial 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("wavefinancial:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Invoices).filter_by(Status="SENT"):
	print("Id: ", instance.Id)
	print("DueDate: ", instance.DueDate)
	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


Invoices_table = Invoices.metadata.tables["Invoices"]
for instance in session.execute(Invoices_table.select().where(Invoices_table.c.Status == "SENT")):
	print("Id: ", instance.Id)
	print("DueDate: ", instance.DueDate)
	print("---------")

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

Insert Wave Financial Data

To insert Wave Financial 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 Wave Financial.


new_rec = Invoices(Id="placeholder", Status="SENT")
session.add(new_rec)
session.commit()

Update Wave Financial Data

To update Wave Financial 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 Wave Financial.


updated_rec = session.query(Invoices).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Status = "SENT"
session.commit()

Delete Wave Financial Data

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

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Wave Financial Python Connector

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