Use pandas to Visualize Wasabi Data in Python

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Wasabi Python Connector

Python Connector Libraries for Wasabi Data Connectivity. Integrate Wasabi with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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

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

Connecting to Wasabi Data

Connecting to Wasabi 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 authorize Wasabi requests, provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id. Set SecretKey to the secret access key.

Note: You can connect as the AWS account administrator, but it is recommended to use IAM user credentials to access AWS services.

For information on obtaining the credentials and other authentication methods, refer to the Getting Started section of the Help documentation.

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

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

engine = create_engine("wasabi:///?AccessKey=a123&SecretKey=s123")

Execute SQL to Wasabi

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

df = pandas.read_sql("SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'", engine)

Visualize Wasabi Data

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

df.plot(kind="bar", x="Name", y="OwnerId")

Free Trial & More Information

Download a free, 30-day trial of the Wasabi Python Connector to start building Python apps and scripts with connectivity to Wasabi 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("wasabi:///?AccessKey=a123&SecretKey=s123")
df = pandas.read_sql("SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'", engine)

df.plot(kind="bar", x="Name", y="OwnerId")