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

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

Connecting to CleverPush Data

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

Cleverpush API Profile Settings

CleverPush uses private API keys to authenticate requests. Your API key is passed as the Authorization request header value on every API call.

You can find your private API key in the CleverPush dashboard under Settings > API. Use the private key (not the public key) for server-side access.

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your CleverPush private API key.

Optional Connection Properties

• ChannelId: Set this to your default CleverPush channel identifier. Most tables require a channel filter. Setting this property allows queries without specifying ChannelId in every WHERE clause.

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

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

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

Execute SQL to CleverPush

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 Segments WHERE ChannelId = 'your_channel_id'", engine)

Visualize CleverPush Data

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

df.plot(kind="bar", x="Id", y="Name")
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 CleverPush 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\Cleverpush.apip&ProfileSettings='APIKey=my_api_key'")
df = pandas.read_sql("SELECT Id, Name FROM Segments WHERE ChannelId = 'your_channel_id'", engine)

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