How to Visualize Carbone 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 Carbone-connected Python applications and scripts for visualizing Carbone data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Carbone data, execute queries, and visualize the results.

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

Connecting to Carbone Data

Connecting to Carbone 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 Carbone Profile on disk (e.g. C:\profiles\Carbone.apip). Next, set the ProfileSettings connection property to the connection string for Carbone (see below).

Carbone API Profile Settings

Carbone uses API key authentication via a Bearer token in the Authorization header. To obtain an API key:

1. Log in to your Carbone account at https://account.carbone.io
2. Copy the API key shown on the account home page

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Carbone API key (Bearer token).
• CarboneVersion: (Optional) The major version of the Carbone API to target via the carbone-version request header. Defaults to 5. Accepted values: 5, 4, 3, staging.

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

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

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

Execute SQL to Carbone

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, Name FROM Templates WHERE Category = 'Invoices'", engine)

Visualize Carbone Data

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

df.plot(kind="bar", x="Id", y="Name")
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 Carbone 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\Carbone.apip&AuthScheme=APIKey&APIKey=your_api_key")
df = pandas.read_sql("SELECT Id, Name FROM Templates WHERE Category = 'Invoices'", engine)

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