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Use pandas to Visualize Marketo Data in Python

The CData Python Connector for Marketo enables you use pandas and other modules to analyze and visualize live Marketo 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 Marketo, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Marketo-connected Python applications and scripts for visualizing Marketo data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Marketo data, execute queries, and visualize the results.

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

Connecting to Marketo Data

Connecting to Marketo 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.

Both the REST and SOAP APIs are supported and can be chosen by using the Schema property.

For the REST API: The OAuthClientId, OAuthClientSecret, and RESTEndpoint properties, under the OAuth and REST Connection sections, must be set to valid Marketo user credentials.

For the SOAP API: The UserId, EncryptionKey, and SOAPEndpoint properties, under the SOAP Connection section, must be set to valid Marketo user credentials.

See the "Getting Started" chapter of the help documentation for a guide to obtaining these values.

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

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

engine = create_engine("marketo:///?Schema=REST&RESTEndpoint=https://311-IFS-929.mktorest.com/rest&OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret")

Execute SQL to Marketo

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

df = pandas.read_sql("SELECT Email, AnnualRevenue FROM Leads WHERE Country = 'U.S.A.'", engine)

Visualize Marketo Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Marketo Python Connector to start building Python apps and scripts with connectivity to Marketo 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("marketo:///?Schema=REST&RESTEndpoint=https://311-IFS-929.mktorest.com/rest&OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret")
df = pandas.read_sql("SELECT Email, AnnualRevenue FROM Leads WHERE Country = 'U.S.A.'", engine)

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