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

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

Connecting to Sybase Data

Connecting to Sybase 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 Sybase, specify the following connection properties:

• Server: Set this to the name or network address of the Sybase database instance.
• Database: Set this to the name of the Sybase database running on the specified Server.

Optionally, you can also secure your connections with TLS/SSL by setting UseSSL to true.

Sybase supports several methods for authentication including Password and Kerberos.

Connect Using Password Authentication

Set the AuthScheme to Password and set the following connection properties to use Sybase authentication.

• User: Set this to the username of the authenticating Sybase user.
• Password: Set this to the username of the authenticating Sybase user.

Connect using LDAP Authentication

To connect with LDAP authentication, you will need to configure Sybase server-side to use the LDAP authentication mechanism.

After configuring Sybase for LDAP, you can connect using the same credentials as Password authentication.

Connect Using Kerberos Authentication

To leverage Kerberos authentication, begin by enabling it setting AuthScheme to Kerberos. See the Using Kerberos section in the Help documentation for more information on using Kerberos authentication.

You can find an example connection string below: Server=MyServer;Port=MyPort;User=SampleUser;Password=SamplePassword;Database=MyDB;Kerberos=true;KerberosKDC=MyKDC;KerberosRealm=MYREALM.COM;KerberosSPN=server-name

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

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

engine = create_engine("sybase:///?User=myuser&Password=mypassword&Server=localhost&Database=mydatabase&Charset=iso_1")

Execute SQL to Sybase

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

df = pandas.read_sql("SELECT Id, ProductName FROM Products WHERE ProductName = 'Konbu'", engine)

Visualize Sybase Data

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

df.plot(kind="bar", x="Id", y="ProductName")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Sybase to start building Python apps and scripts with connectivity to Sybase 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("sybase:///?User=myuser&Password=mypassword&Server=localhost&Database=mydatabase&Charset=iso_1")
df = pandas.read_sql("SELECT Id, ProductName FROM Products WHERE ProductName = 'Konbu'", engine)

df.plot(kind="bar", x="Id", y="ProductName")
plt.show()