Use Dash to Build to Web Apps on PhantomBuster 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 PhantomBuster-connected web applications for PhantomBuster data. This article shows how to connect to PhantomBuster with the CData Connector and use pandas and Dash to build a simple web app for visualizing PhantomBuster data.

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

Connecting to PhantomBuster Data

Connecting to PhantomBuster 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

To use the Phantombuster API, you need to obtain an API key from your Phantombuster account settings. Navigate to phantombuster.com, click your profile icon, select Settings, and copy the API key from the API section.

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Phantombuster API key from the account settings page.

Multi-Organization Accounts

If your API key is associated with multiple organizations, you can target a specific organization by setting the OrganizationId connection property to the desired organization identifier. When set, it is sent as the X-Phantombuster-Org request header.

Example connection string:

Profile=C:\profiles\Phantombuster.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key_here"

After installing the CData PhantomBuster Connector, follow the procedure below to install the other required modules and start accessing PhantomBuster 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 PhantomBuster 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 PhantomBuster Connector to create a connection for working with PhantomBuster data.

cnxn = mod.connect("Profile=C:\profiles\Phantombuster.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key_here"")

Execute SQL to PhantomBuster

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 Agents 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 PhantomBuster 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='PhantomBuster Agents 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 PhantomBuster data.

python api-dash.py

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 PhantomBuster 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\Phantombuster.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key_here"")

df = pd.read_sql("SELECT ,  FROM Agents 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='PhantomBuster Agents Data', barmode='stack')
		})
], className="container")

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