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Use Dash to Build to Web Apps on Gmail Data

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

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

Connecting to Gmail Data

Connecting to Gmail 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.

There are two ways to authenticate to Gmail. Before selecting one, first ensure that you have enabled IMAP access in your Gmail account settings. See the "Connecting to Gmail" section under "Getting Started" in the installed documentation for a guide.

The User and Password properties, under the Authentication section, can be set to valid Gmail user credentials.

Alternatively, instead of providing the Password, you can use 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, you will need to provide the User. See the "Getting Started" chapter in the help documentation for a guide to using OAuth.

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

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

cnxn = mod.connect("User=username;Password=password;")

Execute SQL to Gmail

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 Subject, Size FROM Inbox WHERE From = 'test@test.com'", 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-gmailedataplot'

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 Gmail data and configure the app layout.

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

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='Gmail Inbox 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 Gmail data.

python gmail-dash.py

Free Trial & More Information

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

cnxn = mod.connect("User=username;Password=password;")

df = pd.read_sql("SELECT Subject, Size FROM Inbox WHERE From = 'test@test.com'", cnxn)
app_name = 'dash-gmaildataplot'

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.Subject, y=df.Size, name='Subject')

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='Gmail Inbox Data', barmode='stack')
		})
], className="container")

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