Use pandas to Visualize Teradata Data in Python

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Teradata Python Connector

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

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

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

Connecting to Teradata Data

Connecting to Teradata 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 Teradata, provide authentication information and specify the database server name.

  • User: Set this to the username of a Teradata user.
  • Password: Set this to the password of the Teradata user.
  • DataSource: Specify the Teradata server name, DBC Name, or TDPID.
  • Port: Specify the port the server is running on.
  • Database: Specify the database name. If not specified, the default database is used.

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

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

engine = create_engine("teradata:///?User=myuser&Password=mypassword&Server=localhost&Database=mydatabase")

Execute SQL to Teradata

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

df = pandas.read_sql("SELECT ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = '5'", engine)

Visualize Teradata Data

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

df.plot(kind="bar", x="ProductId", y="ProductName")

Free Trial & More Information

Download a free, 30-day trial of the Teradata Python Connector to start building Python apps and scripts with connectivity to Teradata 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("teradata:///?User=myuser&Password=mypassword&Server=localhost&Database=mydatabase")
df = pandas.read_sql("SELECT ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = '5'", engine)

df.plot(kind="bar", x="ProductId", y="ProductName")