How to Build an ETL App for QuickBooks Time Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for QuickBooks Time and the petl framework, you can build QuickBooks Time-connected applications and pipelines for extracting, transforming, and loading QuickBooks Time data. This article shows how to connect to QuickBooks Time with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData QuickBooks Time Connector, follow the procedure below to install the other required modules and start accessing QuickBooks Time through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for QuickBooks Time Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.tsheets as mod

You can now connect with a connection string. Use the connect function for the CData QuickBooks Time Connector to create a connection for working with QuickBooks Time data.

cnxn = mod.connect("OAuthClientId=myclientid;OAuthClientSecret=myclientsecret;CallbackUrl=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query QuickBooks Time

Use SQL to create a statement for querying QuickBooks Time. In this article, we read data from the Timesheets entity.

sql = "SELECT Id, JobcodeId FROM Timesheets WHERE JobCodeType = 'regular'"

Extract, Transform, and Load the QuickBooks Time Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the QuickBooks Time data. In this example, we extract QuickBooks Time data, sort the data by the JobcodeId column, and load the data into a CSV file.

Loading QuickBooks Time Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'JobcodeId')

etl.tocsv(table2,'timesheets_data.csv')

With the CData Python Connector for QuickBooks Time, you can work with QuickBooks Time data just like you would with any database, including direct access to data in ETL packages like petl.

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.

Full Source Code

import petl as etl
import pandas as pd
import cdata.tsheets as mod

cnxn = mod.connect("OAuthClientId=myclientid;OAuthClientSecret=myclientsecret;CallbackUrl=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, JobcodeId FROM Timesheets WHERE JobCodeType = 'regular'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'JobcodeId')

etl.tocsv(table2,'timesheets_data.csv')