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

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

Connecting to BugHerd Data

Connecting to BugHerd 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 BugHerd Profile on disk (e.g. C:\profiles\BugHerd.apip). Next, set the ProfileSettings connection property to the connection string for BugHerd (see below).

BugHerd API Profile Settings

BugHerd uses HTTP Basic authentication with an API key as the username. To obtain an API key:

1. Sign in to your BugHerd account at https://www.bugherd.com
2. Navigate to Settings > General Settings
3. Locate the API Key section and copy the generated key

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your BugHerd API key. The driver uses the key as the Basic auth username and sets the password internally to the literal value 'x' as required by BugHerd.

Follow the procedure below to install the required modules and start accessing BugHerd 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 BugHerd Data in Python

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

engine = create_engine("api:///?Profile=C:\profiles\BugHerd.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key'")

Execute SQL to BugHerd

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, Title FROM Tasks WHERE ProjectId = '519666'", engine)

Visualize BugHerd Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to BugHerd 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("api:///?Profile=C:\profiles\BugHerd.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key'")
df = pandas.read_sql("SELECT Id, Title FROM Tasks WHERE ProjectId = '519666'", engine)

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