Use Dash to Build to Web Apps on Pushbullet Data

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 module, and the Dash framework, you can build Pushbullet-connected web applications for Pushbullet data. This article shows how to connect to Pushbullet with the CData Connector and use pandas and Dash to build a simple web app for visualizing Pushbullet data.

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.

After installing the CData Pushbullet Connector, follow the procedure below to install the other required modules and start accessing Pushbullet through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize Pushbullet Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Pushbullet Connector to create a connection for working with Pushbullet data.

cnxn = mod.connect("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 result set in a DataFrame.

df = pd.read_sql("SELECT , FROM Users WHERE = ''", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-apiedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our Pushbullet data and configure the app layout.

trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Pushbullet Users Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
 app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the Pushbullet data.

python api-dash.py

👁 Pushbullet data in a Dash web app (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Pushbullet data. Reach out to our Support Team if you have any questions.

---

Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\Pushbullet.apip;ProfileSettings='APIKey=your_access_token;';AuthScheme=APIKey;")

df = pd.read_sql("SELECT , FROM Users WHERE = ''", cnxn)
app_name = 'dash-apidataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Pushbullet Users Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
 app.run_server(debug=True)