How to Visualize Postmark Data in Python with pandas

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Postmark-connected Python applications and scripts for visualizing Postmark data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Postmark data, execute queries, and visualize the results.

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

Connecting to Postmark Data

Connecting to Postmark 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.

Using API Key Authentication

Postmark uses server API tokens to authenticate requests. Each Postmark server has its own API token, which controls access to messages, bounces, templates, and statistics associated with that server.

To obtain your Server API Token, log in to your Postmark account and navigate to the server you want to connect to. Go to API Tokens under the server settings and copy the token labeled Server API token.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Postmark Server API Token. This value is sent as the X-Postmark-Server-Token header on every request.

Example connection string:

Profile=C:\profiles\Postmark.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your-server-api-token"

Connecting to Postmark

Once the authentication is configured, you can connect to Postmark and query data from any of the available tables such as OutboundMessages, Bounces, and Templates.

Follow the procedure below to install the required modules and start accessing Postmark through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Postmark Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Postmark data.

engine = create_engine("api:///?Profile=C:\profiles\Postmark.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your-server-api-token"")

Execute SQL to Postmark

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT , FROM Bounces WHERE = ''", engine)

Visualize Postmark Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Postmark data. The show method displays the chart in a new window.

df.plot(kind="bar", x="", y="")
plt.show()

👁 Postmark data in a Python plot (Salesforce is shown).

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 Postmark data. Reach out to our Support Team if you have any questions.

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Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("api:///?Profile=C:\profiles\Postmark.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your-server-api-token"")
df = pandas.read_sql("SELECT , FROM Bounces WHERE = ''", engine)

df.plot(kind="bar", x="", y="")
plt.show()