How to Visualize Aha! 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 Aha!-connected Python applications and scripts for visualizing Aha! data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Aha! data, execute queries, and visualize the results.

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

Connecting to Aha! Data

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

Aha! API Profile Settings

The Aha! API uses OAuth-based authentication.

You will first need to register an OAuth app with Aha!. This can be done from your Aha! account under 'Settings' > 'Personal' > 'Developer' > 'OAuth Applications'. Additionally, set the Domain, found in the domain name of your Aha account. For example if your Aha account is acmeinc.aha.io, then the Domain should be 'acmeinc'.

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

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• OAuthClientId: Set this to the client_id that is specified in you app settings.
• OAuthClientSecret: Set this to the client_secret that is specified in you app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.
• Domain: Set this in the ProfileSettings to your Aha domain.

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

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

engine = create_engine("api:///?Profile=C:\profiles\aha.apip&ProfileSettings='Domain=acmeinc'&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")

Execute SQL to Aha!

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, Name FROM Ideas WHERE AssignedToUserId = 'my_user_id'", engine)

Visualize Aha! Data

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

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

👁 Aha! data in a Python plot (Salesforce is shown).

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 Aha! data. Reach out to our Support Team if you have any questions.

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Full Source Code

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

engine = create_engine("api:///?Profile=C:\profiles\aha.apip&ProfileSettings='Domain=acmeinc'&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
df = pandas.read_sql("SELECT Id, Name FROM Ideas WHERE AssignedToUserId = 'my_user_id'", engine)

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