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Use pandas to Visualize DigitalOcean Data in Python

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

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

Connecting to DigitalOcean Data

Connecting to DigitalOcean 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.

DigitalOcean uses OAuth 2.0 authentication. To authenticate using OAuth, you can use the embedded credentials or register an app with DigitalOcean.

See the Getting Started guide in the CData driver documentation for more information.

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

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

engine = create_engine("digitalocean:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to DigitalOcean

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 Droplets WHERE Id = '1'", engine)

Visualize DigitalOcean Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the DigitalOcean 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 DigitalOcean Python Connector to start building Python apps and scripts with connectivity to DigitalOcean 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("digitalocean:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, Name FROM Droplets WHERE Id = '1'", engine)

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