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Use pandas to Visualize HPCC Systems Data in Python

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

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

Connecting to HPCC Systems Data

Connecting to HPCC Systems 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, set the following connection properties: Set URL to the machine name or IP address of the server and the port the server is running on, for example, https://server:port. The User and Password are required to authenticate to the HPCC Systems cluster specified in the URL. Note that LDAP authentication is not currently supported by our ODBC driver.

Set Version to the WsSQL Web server version. Note that if you have not already done so, you will need to install the WsSQL service on the HPCC Systems server. The WsSQL Web service is used to interact with the underlying HPCC Systems platform.

Set Cluster to the target cluster.

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

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

engine = create_engine("hpcc:///?URL=http://127.0.0.1:8510&User=test&password=xA123456&Version=1&Cluster=hthor")

Execute SQL to HPCC Systems

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

df = pandas.read_sql("SELECT CustomerName, Price FROM hpcc::test::orders WHERE ShipCity = 'New York'", engine)

Visualize HPCC Systems Data

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

df.plot(kind="bar", x="CustomerName", y="Price")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the HPCC Systems Python Connector to start building Python apps and scripts with connectivity to HPCC Systems 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("hpcc:///?URL=http://127.0.0.1:8510&User=test&password=xA123456&Version=1&Cluster=hthor")
df = pandas.read_sql("SELECT CustomerName, Price FROM hpcc::test::orders WHERE ShipCity = 'New York'", engine)

df.plot(kind="bar", x="CustomerName", y="Price")
plt.show()