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

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

Connecting to Accelo Data

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

Accelo API Profile Settings

Register an OAuth application in Accelo via Configuration > API > Register Application. Your client ID, client secret, and redirect URI are provided in your app settings.

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

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

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

Execute SQL to Accelo

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, ActivityClass FROM Activities WHERE Subject = 'Project Meeting'", engine)

Visualize Accelo Data

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

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

👁 Accelo 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 Accelo 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\Accelo.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
df = pandas.read_sql("SELECT Id, ActivityClass FROM Activities WHERE Subject = 'Project Meeting'", engine)

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