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

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

Connecting to ActiveCampaign Data

Connecting to ActiveCampaign 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.

ActiveCampaign supports authenticating with the API Key. To connect to ActiveCampaign, set the following:

• URL: This can be found in your account on the My Settings page under the Developer tab. For example: https://{yourAccountName}.api-us1.com
• APIKey: This can be found in your account on the Settings page under the Developer tab. Each user in your ActiveCampaign account has their own unique API key.

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

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

engine = create_engine("activecampaign:///?URL=yourUrl&APIKey=yourApiKey")

Execute SQL to ActiveCampaign

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

df = pandas.read_sql("SELECT LastName, Email FROM Contacts WHERE LastName = 'Smith'", engine)

Visualize ActiveCampaign Data

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

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for ActiveCampaign to start building Python apps and scripts with connectivity to ActiveCampaign 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("activecampaign:///?URL=yourUrl&APIKey=yourApiKey")
df = pandas.read_sql("SELECT LastName, Email FROM Contacts WHERE LastName = 'Smith'", engine)

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