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

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

Connecting to Lakebase Data

Connecting to Lakebase 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.

• DatabricksInstance: The Databricks instance or server hostname, provided in the format instance-abcdef12-3456-7890-abcd-abcdef123456.database.cloud.databricks.com.
• Server: The host name or IP address of the server hosting the Lakebase database.
• Port (optional): The port of the server hosting the Lakebase database, set to 5432 by default.
• Database (optional): The database to connect to after authenticating to the Lakebase Server, set to the authenticating user's default database by default.

OAuth Client Authentication

To authenicate using OAuth client credentials, you need to configure an OAuth client in your service principal. In short, you need to do the following:

1. Create and configure a new service principal
2. Assign permissions to the service principal
3. Create an OAuth secret for the service principal

For more information, refer to the Setting Up OAuthClient Authentication section in the Help documentation.

OAuth PKCE Authentication

To authenticate using the OAuth code type with PKCE (Proof Key for Code Exchange), set the following properties:

• AuthScheme: OAuthPKCE.
• User: The authenticating user's user ID.

For more information, refer to the Help documentation.

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

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

engine = create_engine("lakebase:///?DatabricksInstance=lakebase&Server=127.0.0.1&Port=5432&Database=my_database&InitiateOAuth=GETANDREFRESH")

Execute SQL to Lakebase

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

df = pandas.read_sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", engine)

Visualize Lakebase Data

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

df.plot(kind="bar", x="ShipName", y="ShipCity")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Lakebase to start building Python apps and scripts with connectivity to Lakebase 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("lakebase:///?DatabricksInstance=lakebase&Server=127.0.0.1&Port=5432&Database=my_database&InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", engine)

df.plot(kind="bar", x="ShipName", y="ShipCity")
plt.show()