How to Visualize IBM Cloud Object Storage Data in Python with pandas

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 Object Storage, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build IBM Cloud Object Storage-connected Python applications and scripts for visualizing IBM Cloud Object Storage data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to IBM Cloud Object Storage 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 Object Storage data in Python. When you issue complex SQL queries from IBM Cloud Object Storage, the driver pushes supported SQL operations, like filters and aggregations, directly to IBM Cloud Object Storage and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to IBM Cloud Object Storage Data

Connecting to IBM Cloud Object Storage 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.

Register a New Instance of Cloud Object Storage

If you do not already have Cloud Object Storage in your IBM Cloud account, follow the procedure below to install an instance of SQL Query in your account:

1. Log in to your IBM Cloud account.
2. Navigate to the

Connecting using OAuth Authentication

There are certain connection properties you need to set before you can connect. You can obtain these as follows:

API Key

To connect with IBM Cloud Object Storage, you need an API Key. You can obtain this as follows:

1. Log in to your IBM Cloud account.
2. Navigate to the Platform API Keys page.
3. On the middle-right corner click "Create an IBM Cloud API Key" to create a new API Key.
4. In the pop-up window, specify the API Key name and click "Create". Note the API Key as you can never access it again from the dashboard.

Cloud Object Storage CRN

If you have multiple accounts, specify the CloudObjectStorageCRN explicitly. To find the appropriate value, you can:

• Query the Services view. This will list your IBM Cloud Object Storage instances along with the CRN for each.
• Locate the CRN directly in IBM Cloud. To do so, navigate to your IBM Cloud Dashboard. In the Resource List, Under Storage, select your Cloud Object Storage resource to get its CRN.

Connecting to Data

You can now set the following to connect to data:

• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
• ApiKey: Set this to your API key which was noted during setup.
• CloudObjectStorageCRN (Optional): Set this to the cloud object storage CRN you want to work with. While the connector attempts to retrieve this automatically, specifying this explicitly is recommended if you have more than Cloud Object Storage account.

When you connect, the connector completes the OAuth process.

1. Extracts the access token and authenticates requests.
2. Saves OAuth values in OAuthSettingsLocation to be persisted across connections.

Follow the procedure below to install the required modules and start accessing IBM Cloud Object Storage 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 Object Storage 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 Object Storage data.

engine = create_engine("ibmcloudobjectstorage:///?ApiKey=myApiKey&CloudObjectStorageCRN=MyInstanceCRN&Region=myRegion&OAuthClientId=MyOAuthClientId&OAuthClientSecret=myOAuthClientSecret")

Execute SQL to IBM Cloud Object Storage

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

df = pandas.read_sql("SELECT Key, Etag FROM Objects WHERE Bucket = 'someBucket'", engine)

Visualize IBM Cloud Object Storage Data

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

df.plot(kind="bar", x="Key", y="Etag")
plt.show()

👁 IBM Cloud Object Storage data in a Python plot (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for IBM Cloud Object Storage to start building Python apps and scripts with connectivity to IBM Cloud Object Storage data. Reach out to our Support Team if you have any questions.

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Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("ibmcloudobjectstorage:///?ApiKey=myApiKey&CloudObjectStorageCRN=MyInstanceCRN&Region=myRegion&OAuthClientId=MyOAuthClientId&OAuthClientSecret=myOAuthClientSecret")
df = pandas.read_sql("SELECT Key, Etag FROM Objects WHERE Bucket = 'someBucket'", engine)

df.plot(kind="bar", x="Key", y="Etag")
plt.show()