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

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

Connecting to Anaplan Data

Connecting to Anaplan 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.

Authenticating to Anaplan

The driver supports authenticating with Basic, Certificate, or OAuth. In every case, set Region to the region where your Anaplan account data is hosted (e.g., , which is the default).

Using Basic Authentication

Set AuthScheme to , then supply your Anaplan User and Password. If your workspace uses single sign-on (SSO), you must be assigned as an Exception User to use Basic authentication.

Using Certificate Authentication

Set AuthScheme to , then supply the Certificate, CertificateType, and PrivateKey properties (and the matching CertificatePassword / PrivateKeyPassword if either is encrypted). The certificate must be a CA-issued X.509 certificate registered with your Anaplan tenant administrator.

Using OAuth Authentication

Register a custom OAuth application in Anaplan, then set the following properties:

• OAuthClientId: The client Id assigned when you registered your custom OAuth application.
• OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.
• CallbackURL: The redirect URI defined when you registered your application.
• InitiateOAuth: Set to to have the driver manage the OAuth token exchange and refresh automatically.

See the Getting Started chapter of the help documentation for a guide to creating a custom OAuth app and using OAuth.

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

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

engine = create_engine("anaplan:///?OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackURL=your_callback_url&Region=US1&InitiateOAuth=GETANDREFRESH")

Execute SQL to Anaplan

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

df = pandas.read_sql("SELECT Region, Product FROM Sales WHERE Value = '100'", engine)

Visualize Anaplan Data

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

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Anaplan to start building Python apps and scripts with connectivity to Anaplan 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("anaplan:///?OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackURL=your_callback_url&Region=US1&InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT Region, Product FROM Sales WHERE Value = '100'", engine)

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