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

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

Connecting to Pushbullet Data

Connecting to Pushbullet 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.

Using API Key Authentication

Pushbullet uses token-based authentication (Access Token). To obtain an Access Token:

1. Log in to your Pushbullet account at https://www.pushbullet.com
2. Navigate to Settings > Account
3. Click "Create Access Token"
4. Copy the generated token

After obtaining your Access Token, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your Pushbullet Access Token.

Example Connection String

Profile=C:\profiles\Pushbullet.apip;ProfileSettings='APIKey=your_access_token;';AuthScheme=APIKey;

Connecting to Pushbullet

Once the authentication is configured, you can connect to Pushbullet and query data from any of the available tables such as Users, Pushes, Devices, Chats, Subscriptions, and Channels.

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

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

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

Execute SQL to Pushbullet

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

df = pandas.read_sql("SELECT ,  FROM Users WHERE  = ''", engine)

Visualize Pushbullet Data

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

df.plot(kind="bar", x="", y="")
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 Pushbullet data. Reach out to our Support Team if you have any questions.

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Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("api:///?Profile=C:\profiles\Pushbullet.apip&ProfileSettings='APIKey=your_access_token&'&AuthScheme=APIKey")
df = pandas.read_sql("SELECT ,  FROM Users WHERE  = ''", engine)

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