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

Connecting to Google Spanner Data

Connecting to Google Spanner 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.

Google Spanner uses the OAuth authentication standard. To authenticate using OAuth, you can use the embedded credentials or register an app with Google.

See the Getting Started guide in the CData driver documentation for more information.

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

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

engine = create_engine("googlespanner:///?ProjectId='project1'&InstanceId='instance1'&Database='db1'&InitiateOAuth=GETANDREFRESH")

Execute SQL to Google Spanner

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, TotalDue FROM Customer WHERE Id = '1'", engine)

Visualize Google Spanner Data

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

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

👁 Google Spanner data in a Python plot (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Google Spanner to start building Python apps and scripts with connectivity to Google Spanner 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("googlespanner:///?ProjectId='project1'&InstanceId='instance1'&Database='db1'&InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT Name, TotalDue FROM Customer WHERE Id = '1'", engine)

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