Use Dash to Build to Web Apps on Harvest Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas module, and the Dash framework, you can build Harvest-connected web applications for Harvest data. This article shows how to connect to Harvest with the CData Connector and use pandas and Dash to build a simple web app for visualizing Harvest data.

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

Connecting to Harvest Data

Connecting to Harvest 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.

Start by setting the Profile connection property to the location of the Harvest Profile on disk (e.g. C:\profiles\Harvest.apip). Next, set the ProfileSettings connection property to the connection string for Harvest (see below).

Harvest API Profile Settings

To authenticate to Harvest, you can use either Token authentication or the OAuth standard. Use Basic authentication to connect to your own data. Use OAuth to allow other users to connect to their data.

Using Token Authentication

To use Token Authentication, set the APIKey to your Harvest Personal Access Token in the ProfileSettings connection property. In addition to APIKey, set your AccountId in ProfileSettings to connect.

Using OAuth Authentication

First, register an OAuth2 application with Harvest. The application can be created from the "Developers" section of Harvest ID.

After setting the following connection properties, you are ready to connect:

• ProfileSettings: Set your AccountId in ProfileSettings.
• AuthScheme: Set this to OAuth.
• OAuthClientId: Set this to the client ID that you specified in your app settings.
• OAuthClientSecret: Set this to the client secret that you specified in your app settings.
• CallbackURL: Set this to the Redirect URI that you specified in your app settings.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage how the driver obtains and refreshes the OAuthAccessToken.

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

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

cnxn = mod.connect("Profile=C:\profiles\Harvest.apip;ProfileSettings='APIKey=my_personal_key;AccountId=_your_account_id';")

Execute SQL to Harvest

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, ClientName FROM Invoices WHERE State = 'open'", 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-apiedataplot'

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 Harvest data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.ClientName, 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='Harvest Invoices 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 Harvest data.

python api-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Harvest 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.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\Harvest.apip;ProfileSettings='APIKey=my_personal_key;AccountId=_your_account_id';")

df = pd.read_sql("SELECT Id, ClientName FROM Invoices WHERE State = 'open'", cnxn)
app_name = 'dash-apidataplot'

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.ClientName, 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='Harvest Invoices Data', barmode='stack')
		})
], className="container")

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