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Use pandas to Visualize MailChimp Data in Python

The CData Python Connector for MailChimp enables you use pandas and other modules to analyze and visualize live MailChimp data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for MailChimp, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build MailChimp-connected Python applications and scripts for visualizing MailChimp data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to MailChimp data, execute queries, and visualize the results.

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

Connecting to MailChimp Data

Connecting to MailChimp 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.

You can set the APIKey to the key you generate in your account settings, or, instead of providing your APIKey, you can use the OAuth standard to authenticate the application. OAuth can be used to enable other users to access their own data. To authenticate using OAuth, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app with MailChimp.

See the "Getting Started" chapter in the help documentation for a guide to using OAuth.

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

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

engine = create_engine("mailchimp:///?APIKey=myAPIKey")

Execute SQL to MailChimp

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

df = pandas.read_sql("SELECT Name, Stats_AvgSubRate FROM Lists WHERE Contact_Country = 'US'", engine)

Visualize MailChimp Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the MailChimp Python Connector to start building Python apps and scripts with connectivity to MailChimp 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("mailchimp:///?APIKey=myAPIKey")
df = pandas.read_sql("SELECT Name, Stats_AvgSubRate FROM Lists WHERE Contact_Country = 'US'", engine)

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