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Use pandas to Visualize AWS Management Data in Python

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

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

Connecting to AWS Management Data

Connecting to AWS Management 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 authorize AWSDataManagement requests, provide the credentials for an administrator account or for an IAM user with custom permissions:

  1. Set AccessKey to the access key Id.
  2. Set SecretKey to the secret access key.
  3. Set Region to the region where your AWSDataManagement data is hosted.

Note: Though you can connect as the AWS account administrator, it is recommended to use IAM user credentials to access AWS services.

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

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

engine = create_engine("awsdatamanagement:///?AccessKey=myAccessKey&Account=myAccountName&Region=us-east-1")

Execute SQL to AWS Management

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

df = pandas.read_sql("SELECT PartitionKey, Name FROM NorthwingProducts WHERE Id = '1'", engine)

Visualize AWS Management Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the AWS Management Python Connector to start building Python apps and scripts with connectivity to AWS Management 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("awsdatamanagement:///?AccessKey=myAccessKey&Account=myAccountName&Region=us-east-1")
df = pandas.read_sql("SELECT PartitionKey, Name FROM NorthwingProducts WHERE Id = '1'", engine)

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