Discover how a bimodal integration strategy can address the major data management challenges facing your organization today.
Get the Report →Use Dash to Build to Web Apps on QuickBooks POS Data
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 CData Python Connector for QuickBooks POS 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)