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Use Dash to Build to Web Apps on LinkedIn Data

The CData Python Connector for LinkedIn enables you to create Python applications that use pandas and Dash to build LinkedIn-connected web apps.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for LinkedIn, the pandas module, and the Dash framework, you can build LinkedIn-connected web applications for LinkedIn data. This article shows how to connect to LinkedIn with the CData Connector and use pandas and Dash to build a simple web app for visualizing LinkedIn data.

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

Connecting to LinkedIn Data

Connecting to LinkedIn 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.

LinkedIn uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with LinkedIn. For more information refer to our authentication guide.

After installing the CData LinkedIn Connector, follow the procedure below to install the other required modules and start accessing LinkedIn 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 LinkedIn 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.linkedin as mod
import plotly.graph_objs as go

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;CompanyId=XXXXXXXInitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to LinkedIn

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 VisibilityCode, Comment FROM CompanyStatusUpdates WHERE EntityId = '238'", 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-linkedinedataplot'

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 LinkedIn data and configure the app layout.

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

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='LinkedIn CompanyStatusUpdates 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 LinkedIn data.

python linkedin-dash.py

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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.linkedin as mod
import plotly.graph_objs as go

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;CompanyId=XXXXXXXInitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT VisibilityCode, Comment FROM CompanyStatusUpdates WHERE EntityId = '238'", cnxn)
app_name = 'dash-linkedindataplot'

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.VisibilityCode, y=df.Comment, name='VisibilityCode')

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='LinkedIn CompanyStatusUpdates Data', barmode='stack')
		})
], className="container")

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