Use Dash to Build to Web Apps on LinkedIn Ads Data

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LinkedIn Ads Python Connector

Python Connector Libraries for LinkedIn Ads Data Connectivity. Integrate LinkedIn Ads with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for LinkedIn Ads enables you to create Python applications that use pandas and Dash to build LinkedIn Ads-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 Ads, the pandas module, and the Dash framework, you can build LinkedIn Ads-connected web applications for LinkedIn Ads data. This article shows how to connect to LinkedIn Ads with the CData Connector and use pandas and Dash to build a simple web app for visualizing LinkedIn Ads data.

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

Connecting to LinkedIn Ads Data

Connecting to LinkedIn Ads 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 Ads uses the OAuth authentication standard. OAuth requires the authenticating user to interact with LinkedIn using the browser. See the OAuth section in the Help documentation for a guide.

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

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

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

Execute SQL to LinkedIn Ads

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 Analytics 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-linkedinadsedataplot'

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 Ads 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 Ads Analytics 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 Ads data.

python linkedinads-dash.py

Free Trial & More Information

Download a free, 30-day trial of the LinkedIn Ads Python Connector to start building Python apps with connectivity to LinkedIn Ads 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.linkedinads as mod
import plotly.graph_objs as go

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

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

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 Ads Analytics Data', barmode='stack')
		})
], className="container")

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