Use Dash to Build to Web Apps on BigCommerce 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 BigCommerce, the pandas module, and the Dash framework, you can build BigCommerce-connected web applications for BigCommerce data. This article shows how to connect to BigCommerce with the CData Connector and use pandas and Dash to build a simple web app for visualizing BigCommerce data.

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

Connecting to BigCommerce Data

Connecting to BigCommerce 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.

BigCommerce authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Big Commerce developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL. These three parameters will be set as connection properties to your driver.

Additionally, in order to connect to your BigCommerce Store, you will need your StoreId. To find your Store Id please follow these steps:

1. Log in to your BigCommerce account.
2. From the Home Page, select Advanced Settings > API Accounts.
3. Click Create API Account.
4. A text box named API Path will appear on your screen.
5. Inside you can see a URL of the following structure: https://api.bigcommerce.com/stores/{Store Id}/v3.
6. As demonstrated above, your Store Id will be between the 'stores/' and '/v3' path paramters.
7. Once you have retrieved your Store Id you can either click Cancel or proceed in creating an API Account in case you do not have one already.

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

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

cnxn = mod.connect("OAuthClientId=YourClientId; OAuthClientSecret=YourClientSecret; StoreId='YourStoreID'; CallbackURL='http://localhost:33333'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to BigCommerce

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 FirstName, LastName FROM Customers WHERE FirstName = 'Bob'", 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-bigcommerceedataplot'

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 BigCommerce data and configure the app layout.

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

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='BigCommerce Customers 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 BigCommerce data.

python bigcommerce-dash.py

Free Trial & More Information

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

cnxn = mod.connect("OAuthClientId=YourClientId; OAuthClientSecret=YourClientSecret; StoreId='YourStoreID'; CallbackURL='http://localhost:33333'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT FirstName, LastName FROM Customers WHERE FirstName = 'Bob'", cnxn)
app_name = 'dash-bigcommercedataplot'

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

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='BigCommerce Customers Data', barmode='stack')
		})
], className="container")

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