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Use Dash to Build to Web Apps on Google Analytics Data

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

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

Connecting to Google Analytics Data

Connecting to Google Analytics 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.

Google uses the OAuth authentication standard. To access Google APIs on behalf on individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

In addition to the OAuth values, set Profile to the profile you want to connect to. This can be set to either the Id or website URL for the Profile. If not specified, the first Profile returned will be used.

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

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

cnxn = mod.connect("Profile=MyProfile;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Google Analytics

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 Browser, Sessions FROM Traffic WHERE Transactions = '0'", 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-googleanalyticsedataplot'

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 Google Analytics data and configure the app layout.

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

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='Google Analytics Traffic 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 Google Analytics data.

python googleanalytics-dash.py

Free Trial & More Information

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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.googleanalytics as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=MyProfile;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Browser, Sessions FROM Traffic WHERE Transactions = '0'", cnxn)
app_name = 'dash-googleanalyticsdataplot'

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.Browser, y=df.Sessions, name='Browser')

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

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