Use Dash to Build to Web Apps on Airtable Data

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

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

Connecting to Airtable Data

Connecting to Airtable 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.

APIKey, BaseId and TableNames parameters are required to connect to Airtable. ViewNames is an optional parameter where views of the tables may be specified.

• APIKey : API Key of your account. To obtain this value, after logging in go to Account. In API section click Generate API key.
• BaseId : Id of your base. To obtain this value, it is in the same section as the APIKey. Click on Airtable API, or navigate to https://airtable.com/api and select a base. In the introduction section you can find "The ID of this base is appxxN2ftedc0nEG7."
• TableNames : A comma separated list of table names for the selected base. These are the same names of tables as found in the UI.
• ViewNames : A comma separated list of views in the format of (table.view) names. These are the same names of the views as found in the UI.

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

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

cnxn = mod.connect("APIKey=keymz3adb53RqsU;BaseId=appxxN2fe34r3rjdG7;TableNames=Table1,...;ViewNames=Table1.View1,...;")

Execute SQL to Airtable

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 Id, Column1 FROM SampleTable_1 WHERE Column2 = 'SomeValue'", 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-airtableedataplot'

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 Airtable data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.Column1, name='Id')

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='Airtable SampleTable_1 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 Airtable data.

python airtable-dash.py

👁 Airtable data in a Dash web app (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Airtable to start building Python apps with connectivity to Airtable data. Reach out to our Support Team if you have any questions.

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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.airtable as mod
import plotly.graph_objs as go

cnxn = mod.connect("APIKey=keymz3adb53RqsU;BaseId=appxxN2fe34r3rjdG7;TableNames=Table1,...;ViewNames=Table1.View1,...;")

df = pd.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'SomeValue'", cnxn)
app_name = 'dash-airtabledataplot'

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.Id, y=df.Column1, name='Id')

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='Airtable SampleTable_1 Data', barmode='stack')
		})
], className="container")

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