How to Visualize MailerSend 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 MailerSend-connected Python applications and scripts for visualizing MailerSend data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to MailerSend data, execute queries, and visualize the results.

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

Connecting to MailerSend Data

Connecting to MailerSend 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.

The MailerSend API uses API Key authentication via a Bearer token in the Authorization request header.

Using API Key Authentication

Your MailerSend API token is required to create a connection. To obtain your API token:

1. Log into your MailerSend account at app.mailersend.com.
2. Navigate to Settings > API Tokens in your account dashboard.
3. Click Generate new token, provide a name and select the appropriate permissions.
4. Copy the generated API token.

After obtaining your API token, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your MailerSend API token.

Example connection string:

Profile=C:\profiles\Mailersend.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_token';

Follow the procedure below to install the required modules and start accessing MailerSend 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 MailerSend Data in Python

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

engine = create_engine("api:///?Profile=C:\profiles\Mailersend.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_token'")

Execute SQL to MailerSend

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 Activity WHERE DomainId = 'domain123'", engine)

Visualize MailerSend Data

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

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

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

Full Source Code

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

engine = create_engine("api:///?Profile=C:\profiles\Mailersend.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_token'")
df = pandas.read_sql("SELECT ,  FROM Activity WHERE DomainId = 'domain123'", engine)

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