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Create Python applications that use pandas and Dash to build QuickBooks-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, the pandas module, and the Dash framework, you can build QuickBooks-connected web applications for QuickBooks data. This article shows how to connect to QuickBooks with the CData Connector and use pandas and Dash to build a simple web app for visualizing QuickBooks data.
With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live QuickBooks data in Python. When you issue complex SQL queries from QuickBooks, the driver pushes supported SQL operations, like filters and aggregations, directly to QuickBooks and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
About QuickBooks Data Integration
CData simplifies access and integration of live QuickBooks data. Our customers leverage CData connectivity to:
- Access both local and remote company files.
- Connect across editions and regions: QuickBooks Premier, Professional, Enterprise, and Simple Start edition 2002+, as well as Canada, New Zealand, Australia, and UK editions from 2003+.
- Use SQL stored procedures to perform actions like voiding or clearing transactions, merging lists, searching entities, and more.
Customers regularly integrate their QuickBooks data with preferred tools, like Power BI, Tableau, or Excel, and integrate QuickBooks data into their database or data warehouse.
Getting Started
Connecting to QuickBooks Data
Connecting to QuickBooks 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 through the Remote Connector. The Remote Connector runs on the same machine as QuickBooks 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. See the "Getting Started" chapter of the help documentation for a guide.
After installing the CData QuickBooks Connector, follow the procedure below to install the other required modules and start accessing QuickBooks 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 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.quickbooks as mod import plotly.graph_objs as go
You can now connect with a connection string. Use the connect function for the CData QuickBooks Connector to create a connection for working with QuickBooks data.
cnxn = mod.connect("URL=http://remotehost:8166;User=admin;Password=admin123;")
Execute SQL to QuickBooks
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 Name, CustomerBalance FROM Customers WHERE Type = 'Commercial'", 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-quickbooksedataplot' 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 data and configure the app layout.
trace = go.Bar(x=df.Name, y=df.CustomerBalance, name='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='QuickBooks 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 data.
python quickbooks-dash.py
Free Trial & More Information
Download a free, 30-day trial of the CData Python Connector for QuickBooks to start building Python apps with connectivity to QuickBooks 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.quickbooks as mod import plotly.graph_objs as go cnxn = mod.connect("URL=http://remotehost:8166;User=admin;Password=admin123;") df = pd.read_sql("SELECT Name, CustomerBalance FROM Customers WHERE Type = 'Commercial'", cnxn) app_name = 'dash-quickbooksdataplot' 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.Name, y=df.CustomerBalance, name='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='QuickBooks Customers Data', barmode='stack') }) ], className="container") if __name__ == '__main__': app.run_server(debug=True)