How to Build an ETL App for Printify 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 API Driver for Python and the petl framework, you can build Printify-connected applications and pipelines for extracting, transforming, and loading Printify data. This article shows how to connect to Printify with the CData Python Connector and use petl and pandas to extract, transform, and load Printify data.

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

Connecting to Printify Data

Connecting to Printify 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.

Start by setting the Profile connection property to the location of the Printify Profile on disk (e.g. C:\profiles\Profile.apip). Next, set the ProfileSettings connection property to the connection string for Printify (see below).

Printify API Profile Settings

In order to authenticate to Printify, you'll need to provide your API Key. To get your API Key navigate to My Profile, then Connections. In the Connections section you will be able to generate your Personal Access Token (API Key) and set your Token Access Scopes. Personal Access Tokens are valid for one year. An expired Personal Access Token can be re-generated using the same steps after it expires. Set the API Key to your Personal Access Token in the ProfileSettings property to connect.

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

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

cnxn = mod.connect("Profile=C:\profiles\Printify.apip;ProfileSettings='APIKey=your_personal_token';")

Create a SQL Statement to Query Printify

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

sql = "SELECT Id, ShippingMethod FROM Tags WHERE Status = 'pending'"

Extract, Transform, and Load the Printify Data

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

Loading Printify Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Printify 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 API Driver for Python to start building Python apps and scripts with connectivity to Printify 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Printify.apip;ProfileSettings='APIKey=your_personal_token';")

sql = "SELECT Id, ShippingMethod FROM Tags WHERE Status = 'pending'"

table1 = etl.fromdb(cnxn,sql)

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

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