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

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

Connecting to QuickBooks Time Data

Connecting to QuickBooks Time 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.

TSheets uses the OAuth2 standard for authentication and authorization. To construct your own OAuth app and connect to data, refer to OAuth section in the Help.

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

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

cnxn = mod.connect("OAuthClientId=myclientid;OAuthClientSecret=myclientsecret;CallbackUrl=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to QuickBooks Time

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 Id, JobcodeId FROM Timesheets WHERE JobCodeType = 'regular'", 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-tsheetsedataplot'

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 Time data and configure the app layout.

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

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 Time Timesheets 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 Time data.

python tsheets-dash.py

Free Trial & More Information

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

cnxn = mod.connect("OAuthClientId=myclientid;OAuthClientSecret=myclientsecret;CallbackUrl=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Id, JobcodeId FROM Timesheets WHERE JobCodeType = 'regular'", cnxn)
app_name = 'dash-tsheetsdataplot'

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

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 Time Timesheets Data', barmode='stack')
		})
], className="container")

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