Extract, Transform, and Load TaxJar Data in Python

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TaxJar Python Connector

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



The CData Python Connector for TaxJar enables you to create ETL applications and pipelines for TaxJar data in Python with petl.

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

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

Connecting to TaxJar Data

Connecting to TaxJar 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.

To authenticate to the TaxJar API, you will need to first obtain the API Key from the TaxJar UI.

NOTE: the API is available only for Professional and Premium TaxJar plans.

If you already have a Professional or Premium plan you can find the API Key by logging in the TaxJar UI and navigating to Account -> TaxJar API. After obtaining the API Key, you can set it in the APIKey connection property.

Additional Notes

  • By default, the CData connector will retrieve data of the last 3 months in cases where the entity support date range filtering. You can set StartDate to specify the minimum creation date of the data retrieved.
  • If the API Key has been created for a sandbox API account please set UseSandbox to true, but not all endpoints will work as expected. For more information, refer to the TaxJar developer documentation.

After installing the CData TaxJar Connector, follow the procedure below to install the other required modules and start accessing TaxJar 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 TaxJar 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.taxjar as mod

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

cnxn = mod.connect("APIKey=3bb04218ef8t80efdf1739abf7257144;")

Create a SQL Statement to Query TaxJar

Use SQL to create a statement for querying TaxJar. In this article, we read data from the Orders entity.

sql = "SELECT TransactionID, UserID FROM Orders WHERE TransactionID = '123'"

Extract, Transform, and Load the TaxJar Data

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

Loading TaxJar Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Orders table.

Adding New Rows to TaxJar

table1 = [ ['TransactionID','UserID'], ['NewTransactionID1','NewUserID1'], ['NewTransactionID2','NewUserID2'], ['NewTransactionID3','NewUserID3'] ]

etl.appenddb(table1, cnxn, 'Orders')

With the CData Python Connector for TaxJar, you can work with TaxJar 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 TaxJar Python Connector to start building Python apps and scripts with connectivity to TaxJar 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.taxjar as mod

cnxn = mod.connect("APIKey=3bb04218ef8t80efdf1739abf7257144;")

sql = "SELECT TransactionID, UserID FROM Orders WHERE TransactionID = '123'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['TransactionID','UserID'], ['NewTransactionID1','NewUserID1'], ['NewTransactionID2','NewUserID2'], ['NewTransactionID3','NewUserID3'] ]

etl.appenddb(table3, cnxn, 'Orders')