How to Visualize Greenhouse Data in Python with pandas

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 & Matplotlib modules, and the SQLAlchemy toolkit, you can build Greenhouse-connected Python applications and scripts for visualizing Greenhouse data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Greenhouse data, execute queries, and visualize the results.

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.

Follow the procedure below to install the required modules and start accessing Greenhouse through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Greenhouse Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Greenhouse data.

engine = create_engine("greenhouse:///?APIKey=YourAPIKey")

Execute SQL to Greenhouse

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT Id, CandidateId FROM Applications WHERE Status = 'Active'", engine)

Visualize Greenhouse Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Greenhouse data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Id", y="CandidateId")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Greenhouse to start building Python apps and scripts with connectivity to Greenhouse data. Reach out to our Support Team if you have any questions.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("greenhouse:///?APIKey=YourAPIKey")
df = pandas.read_sql("SELECT Id, CandidateId FROM Applications WHERE Status = 'Active'", engine)

df.plot(kind="bar", x="Id", y="CandidateId")
plt.show()