Use pandas to Visualize Act-On Data in Python

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Python Connector Libraries for Act-On Data Connectivity. Integrate Act-On with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Act-On enables you use pandas and other modules to analyze and visualize live Act-On data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Act-On, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Act-On-connected Python applications and scripts for visualizing Act-On data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Act-On data, execute queries, and visualize the results.

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

Connecting to Act-On Data

Connecting to Act-On 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.

ActOn uses the OAuth authentication standard. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties.

See the Getting Started guide in the CData driver documentation for more information.

Follow the procedure below to install the required modules and start accessing Act-On 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 Act-On Data in Python

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

engine = create_engine("acton:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Act-On

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 Images WHERE FolderName = 'New Folder'", engine)

Visualize Act-On Data

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

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

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Full Source Code

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

engine = create_engine("acton:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, Name FROM Images WHERE FolderName = 'New Folder'", engine)

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