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

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

Connecting to Databox Data

Connecting to Databox 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.

Start by setting the Profile connection property to the location of the Databox Profile on disk (e.g. C:\profiles\Databox.apip). Next, set the ProfileSettings connection property to the connection string for Databox (see below).

Databox API Profile Settings

To use the Databox API, you need to generate a personal access key from your Databox account. Navigate to the Databox account settings under Data Manager and generate an API key (prefixed with pak_).

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Databox personal access key.

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

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

engine = create_engine("api:///?Profile=C:\profiles\Databox.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key'")

Execute SQL to Databox

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, Title FROM Datasets WHERE DataSourceId = '4976164'", engine)

Visualize Databox Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Databox 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("api:///?Profile=C:\profiles\Databox.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key'")
df = pandas.read_sql("SELECT Id, Title FROM Datasets WHERE DataSourceId = '4976164'", engine)

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