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Python Connector Libraries for Amazon S3 Data Connectivity. Integrate Amazon S3 with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to Visualize Amazon S3 Data in Python with pandas

Use pandas and other modules to analyze and visualize live Amazon S3 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 Amazon S3, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Amazon S3-connected Python applications and scripts for visualizing Amazon S3 data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Amazon S3 data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Amazon S3 data in Python. When you issue complex SQL queries from Amazon S3, the driver pushes supported SQL operations, like filters and aggregations, directly to Amazon S3 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Amazon S3 Data

Connecting to Amazon S3 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 Amazon S3 requests, provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id. Set SecretKey to the secret access key.

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

For information on obtaining the credentials and other authentication methods, refer to the Getting Started section of the Help documentation.

Follow the procedure below to install the required modules and start accessing Amazon S3 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 Amazon S3 Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Amazon S3 data.

engine = create_engine("amazons3:///?AccessKey=a123&SecretKey=s123")

Execute SQL to Amazon S3

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

df = pandas.read_sql("SELECT Name, OwnerId FROM ObjectsACL WHERE Name = 'TestBucket'", engine)

Visualize Amazon S3 Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Amazon S3 data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Name", y="OwnerId")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Amazon S3 to start building Python apps and scripts with connectivity to Amazon S3 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("amazons3:///?AccessKey=a123&SecretKey=s123")
df = pandas.read_sql("SELECT Name, OwnerId FROM ObjectsACL WHERE Name = 'TestBucket'", engine)

df.plot(kind="bar", x="Name", y="OwnerId")