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Use pandas to Visualize Redshift Data in Python

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

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

Connecting to Redshift Data

Connecting to Redshift 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.

To connect to Redshift, set the following:

  • Server: Set this to the host name or IP address of the cluster hosting the Database you want to connect to.
  • Port: Set this to the port of the cluster.
  • Database: Set this to the name of the database. Or, leave this blank to use the default database of the authenticated user.
  • User: Set this to the username you want to use to authenticate to the Server.
  • Password: Set this to the password you want to use to authenticate to the Server.

You can obtain the Server and Port values in the AWS Management Console:

  1. Open the Amazon Redshift console (http://console.aws.amazon.com/redshift).
  2. On the Clusters page, click the name of the cluster.
  3. On the Configuration tab for the cluster, copy the cluster URL from the connection strings displayed.

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

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

engine = create_engine("redshift:///?User=admin&Password=admin&Database=dev&Server=examplecluster.my.us-west-2.redshift.amazonaws.com&Port=5439")

Execute SQL to Redshift

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", engine)

Visualize Redshift Data

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

df.plot(kind="bar", x="ShipName", y="ShipCity")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Redshift Python Connector to start building Python apps and scripts with connectivity to Redshift 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("redshift:///?User=admin&Password=admin&Database=dev&Server=examplecluster.my.us-west-2.redshift.amazonaws.com&Port=5439")
df = pandas.read_sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", engine)

df.plot(kind="bar", x="ShipName", y="ShipCity")
plt.show()