Ready to get started?

Learn more about the CData Python Connector for QuickBooks POS or download a free trial:

Download Now

Use Dash to Build to Web Apps on QuickBooks POS Data

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

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

Connecting to QuickBooks POS Data

Connecting to QuickBooks POS 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.

When you are connecting to a local QuickBooks instance, you do not need to set any connection properties.

Requests are made to QuickBooks POS through the Remote Connector. The Remote Connector runs on the same machine as QuickBooks POS and accepts connections through a lightweight, embedded Web server. The server supports SSL/TLS, enabling users to connect securely from remote machines.

The first time you connect, you will need to authorize the Remote Connector with QuickBooks POS. See the "Getting Started" chapter of the help documentation for a guide.

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

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

cnxn = mod.connect("")

Execute SQL to QuickBooks POS

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 ListId, AccountLimit FROM Customers WHERE LastName = 'Cook'", 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-quickbooksposedataplot'

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 QuickBooks POS data and configure the app layout.

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

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='QuickBooks POS 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 QuickBooks POS data.

python quickbookspos-dash.py

Free Trial & More Information

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

cnxn = mod.connect("")

df = pd.read_sql("SELECT ListId, AccountLimit FROM Customers WHERE LastName = 'Cook'", cnxn)
app_name = 'dash-quickbooksposdataplot'

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

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

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