Use pandas to Visualize Google Cloud Storage Data in Python

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Google Cloud Storage Python Connector

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



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

Connecting to Google Cloud Storage Data

Connecting to Google Cloud 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.

Authenticate with a User Account

You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

Authenticate with a Service Account

Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.

You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:

  • InitiateOAuth: Set this to GETANDREFRESH.
  • OAuthJWTCertType: Set this to "PFXFILE".
  • OAuthJWTCert: Set this to the path to the .p12 file you generated.
  • OAuthJWTCertPassword: Set this to the password of the .p12 file.
  • OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
  • OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
  • OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
  • ProjectId: Set this to the Id of the project you want to connect to.

The OAuth flow for a service account then completes.

Follow the procedure below to install the required modules and start accessing Google Cloud 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 Google Cloud Storage Data in Python

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

engine = create_engine("googlecloudstorage:///?ProjectId='project1'&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Google Cloud 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 Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'", engine)

Visualize Google Cloud Storage Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Google Cloud Storage Python Connector to start building Python apps and scripts with connectivity to Google Cloud Storage 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("googlecloudstorage:///?ProjectId='project1'&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'", engine)

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