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

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

Connecting to Greenhouse Data

Connecting to Greenhouse 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.

You need an API key to connect to Greenhouse. To create an API key, follow the steps below:

1. Click the Configure icon in the navigation bar and locate Dev Center on the left.
2. Select API Credential Management.
3. Click Create New API Key.
• Set "API Type" to Harvest.
• Set "Partner" to custom.
• Optionally, provide a description.
4. Proceed to Manage permissions and select the appropriate permissions based on the resources you want to access through the driver.
5. Copy the created key and set APIKey to that value.

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

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

cnxn = mod.connect("APIKey=YourAPIKey;")

Execute SQL to Greenhouse

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, CandidateId FROM Applications WHERE Status = 'Active'", 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-greenhouseedataplot'

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 Greenhouse data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.CandidateId, 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='Greenhouse Applications 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 Greenhouse data.

python greenhouse-dash.py

Free Trial & More Information

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

cnxn = mod.connect("APIKey=YourAPIKey;")

df = pd.read_sql("SELECT Id, CandidateId FROM Applications WHERE Status = 'Active'", cnxn)
app_name = 'dash-greenhousedataplot'

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.CandidateId, 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='Greenhouse Applications Data', barmode='stack')
		})
], className="container")

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