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

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

Connecting to SAP Business Warehouse Data

Connecting to SAP Business Warehouse 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.

To connect to SAP Business Warehouse, set the URL property to a valid SAP Business Warehouse server base URL. The driver must connect to SAP Business Warehouse instances hosted over HTTP with XMLA access.

The driver supports the following authentication schemes via the AuthScheme property:

• None: Anonymous authentication, if available on the server.
• Basic: Set User and Password and set AuthScheme to Basic.
• Kerberos: See the Using Kerberos section of the help documentation for the required Kerberos properties.

By default, the driver attempts to negotiate SSL/TLS by checking the server's certificate against the system's trusted certificate store. To specify another certificate, see the SSLServerCert property for the available formats.

Follow the procedure below to install the required modules and start accessing SAP Business Warehouse 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 SAP Business Warehouse Data in Python

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

engine = create_engine("sapbusinesswarehouse:///?URL=https://mysapserver:8000&AuthScheme=Basic&User=username&Password=password")

Execute SQL to SAP Business Warehouse

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

df = pandas.read_sql("SELECT CustomerCount, City FROM Sales WHERE Country = 'US'", engine)

Visualize SAP Business Warehouse Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for SAP Business Warehouse to start building Python apps and scripts with connectivity to SAP Business Warehouse 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("sapbusinesswarehouse:///?URL=https://mysapserver:8000&AuthScheme=Basic&User=username&Password=password")
df = pandas.read_sql("SELECT CustomerCount, City FROM Sales WHERE Country = 'US'", engine)

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