Use Dash to Build to Web Apps on Gumroad Data

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

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

Connecting to Gumroad Data

Connecting to Gumroad 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.

Using OAuth Authentication

To authenticate to Gumroad and connect to your own data or to allow other users to connect to their data, you can use the OAuth 2.0 standard. This is the recommended authentication method.

First you need to register an OAuth application with Gumroad. You can create an OAuth application by visiting your Gumroad account settings at https://app.gumroad.com/settings/advanced and navigating to the Applications section.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. The CData API Profile for Gumroad will automatically walk through the OAuth process in order to obtain the access token.
• OAuthClientID: Set this to the client_id that is specified in your app settings.
• OAuthClientSecret: Set this to the client_secret that is specified in your app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.

Example connection string

Profile=C:\profiles\Gumroad.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;

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

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

cnxn = mod.connect("Profile=C:\profiles\Gumroad.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Execute SQL to Gumroad

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 ,  FROM CustomFields WHERE ProductId = 'prod_abc123xyz'", 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-apiedataplot'

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 Gumroad data and configure the app layout.

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

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='Gumroad CustomFields 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 Gumroad data.

python api-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Gumroad 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.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\Gumroad.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

df = pd.read_sql("SELECT ,  FROM CustomFields WHERE ProductId = 'prod_abc123xyz'", cnxn)
app_name = 'dash-apidataplot'

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

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='Gumroad CustomFields Data', barmode='stack')
		})
], className="container")

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