Ready to get started?

Learn more about the CData Python Connector for IBM Cloud SQL Query or download a free trial:

Download Now

Use pandas to Visualize IBM Cloud SQL Query Data in Python

The CData Python Connector for IBM Cloud SQL Query enables you use pandas and other modules to analyze and visualize live IBM Cloud SQL Query 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 IBM Cloud SQL Query, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build IBM Cloud SQL Query-connected Python applications and scripts for visualizing IBM Cloud SQL Query data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to IBM Cloud SQL Query data, execute queries, and visualize the results.

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

Connecting to IBM Cloud SQL Query Data

Connecting to IBM Cloud SQL Query 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.

IBM Cloud SQL uses the OAuth and HMAC authentication standards. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

Follow the procedure below to install the required modules and start accessing IBM Cloud SQL Query 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 IBM Cloud SQL Query Data in Python

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

engine = create_engine("ibmcloudsqlquery:///?Api Key=MyAPIKey&Instance CRN=myInstanceCRN&Region=myRegion&Schema=mySchema&OAuth Client Id=myOAuthClientId&OAuth Client Secret=myOAuthClientSecret&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to IBM Cloud SQL Query

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, Status FROM Jobs WHERE UserId = 'user@domain.com'", engine)

Visualize IBM Cloud SQL Query Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the IBM Cloud SQL Query Python Connector to start building Python apps and scripts with connectivity to IBM Cloud SQL Query 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("ibmcloudsqlquery:///?Api Key=MyAPIKey&Instance CRN=myInstanceCRN&Region=myRegion&Schema=mySchema&OAuth Client Id=myOAuthClientId&OAuth Client Secret=myOAuthClientSecret&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, Status FROM Jobs WHERE UserId = 'user@domain.com'", engine)

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