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

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

Connecting to Pingdom Data

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

Pingdom API Profile Settings

In your Pingdom account, navigate to Integrations > The Pingdom API and create a new API Token to use as your API key.

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

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

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

Execute SQL to Pingdom

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

df = pandas.read_sql("SELECT UserId, Username FROM Actions WHERE Status = 'delivered'", engine)

Visualize Pingdom Data

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

df.plot(kind="bar", x="UserId", y="Username")
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 Pingdom 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\Pingdom.apip&ProfileSettings='APIKey=your_api_token'")
df = pandas.read_sql("SELECT UserId, Username FROM Actions WHERE Status = 'delivered'", engine)

df.plot(kind="bar", x="UserId", y="Username")
plt.show()