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Use Dash to Build to Web Apps on Google Sheets Data

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

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

Connecting to Google Sheets Data

Connecting to Google Sheets 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.

You can connect to a spreadsheet by providing authentication to Google and then setting the Spreadsheet connection property to the name or feed link of the spreadsheet. If you want to view a list of information about the spreadsheets in your Google Drive, execute a query to the Spreadsheets view after you authenticate.

ClientLogin (username/password authentication) has been officially deprecated since April 20, 2012 and is now no longer available. Instead, use the OAuth 2.0 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.

See the Getting Started chapter in the help documentation to connect to Google Sheets from different types of accounts: Google accounts, Google Apps accounts, and accounts using two-step verification.

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

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

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

Execute SQL to Google Sheets

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 Shipcountry, OrderPrice FROM Orders WHERE ShipCity = 'Madrid'", 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-googlesheetsedataplot'

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 Sheets data and configure the app layout.

trace = go.Bar(x=df.Shipcountry, y=df.OrderPrice, name='Shipcountry')

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 Sheets Orders 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 Sheets data.

python googlesheets-dash.py

Free Trial & More Information

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

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

df = pd.read_sql("SELECT Shipcountry, OrderPrice FROM Orders WHERE ShipCity = 'Madrid'", cnxn)
app_name = 'dash-googlesheetsdataplot'

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.Shipcountry, y=df.OrderPrice, name='Shipcountry')

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 Sheets Orders Data', barmode='stack')
		})
], className="container")

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