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

Connecting to Google Sheets Data

Connecting to Google Sheets 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.

You can connect to a spreadsheet by providing authentication to Google and then setting the Spreadsheet connection property to the name or feed link of the spreadsheet. If you want to view a list of information about the spreadsheets in your Google Drive, execute a query to the Spreadsheets view after you authenticate.

ClientLogin (username/password authentication) has been officially deprecated since April 20, 2012 and is now no longer available. Instead, use the OAuth 2.0 authentication standard. To access Google APIs on behalf on individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

See the Getting Started chapter in the help documentation to connect to Google Sheets from different types of accounts: Google accounts, Google Apps accounts, and accounts using two-step verification.

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

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

engine = create_engine("googlesheets:///?Spreadsheet=MySheet&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Google Sheets

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

df = pandas.read_sql("SELECT Shipcountry, OrderPrice FROM Orders WHERE ShipCity = 'Madrid'", engine)

Visualize Google Sheets Data

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

df.plot(kind="bar", x="Shipcountry", y="OrderPrice")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Google Spreadsheets to start building Python apps and scripts with connectivity to Google Sheets 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("googlesheets:///?Spreadsheet=MySheet&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Shipcountry, OrderPrice FROM Orders WHERE ShipCity = 'Madrid'", engine)

df.plot(kind="bar", x="Shipcountry", y="OrderPrice")
plt.show()