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

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How to use SQLAlchemy ORM to access QuickBooks Time Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of QuickBooks Time data.

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

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

Connecting to QuickBooks Time Data

Connecting to QuickBooks Time 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.

TSheets uses the OAuth2 standard for authentication and authorization. To construct your own OAuth app and connect to data, refer to OAuth section in the Help.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with QuickBooks Time 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("tsheets:///?OAuthClientId=myclientid&OAuthClientSecret=myclientsecret&CallbackUrl=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for QuickBooks Time 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 Timesheets 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 Timesheets(base): __tablename__ = "Timesheets" Id = Column(String,primary_key=True) JobcodeId = Column(String) ...

Query QuickBooks Time 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("tsheets:///?OAuthClientId=myclientid&OAuthClientSecret=myclientsecret&CallbackUrl=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Timesheets).filter_by(JobCodeType="regular"): print("Id: ", instance.Id) print("JobcodeId: ", instance.JobcodeId) 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

Timesheets_table = Timesheets.metadata.tables["Timesheets"] for instance in session.execute(Timesheets_table.select().where(Timesheets_table.c.JobCodeType == "regular")): print("Id: ", instance.Id) print("JobcodeId: ", instance.JobcodeId) print("---------")

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for QuickBooks Time to start building Python apps and scripts with connectivity to QuickBooks Time data. Reach out to our Support Team if you have any questions.