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

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

Connecting to Pushover Data

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

Pushover API Profile Settings

Pushover uses API Key authentication. Your Pushover Application API Token is used to authenticate all requests. You can create and manage API tokens in the Pushover dashboard at pushover.net by registering an application under Settings > Your Applications.

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your 30-character Pushover Application API Token.

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

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

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

Execute SQL to Pushover

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

df = pandas.read_sql("SELECT User, Memo FROM GroupMembers WHERE GroupKey = 'your_group_key'", engine)

Visualize Pushover Data

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

df.plot(kind="bar", x="User", y="Memo")
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 Pushover 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\Pushover.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_application_token'")
df = pandas.read_sql("SELECT User, Memo FROM GroupMembers WHERE GroupKey = 'your_group_key'", engine)

df.plot(kind="bar", x="User", y="Memo")
plt.show()