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

Connecting to Google Tasks Data

Connecting to Google Tasks 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.

Start by setting the Profile connection property to the location of the Google Tasks Profile on disk (e.g. C:\profiles\GoogleTasks.apip). Next, set the ProfileSettings connection property to the connection string for Google Tasks (see below).

Google Tasks API Profile Settings

In the Google Cloud Console, enable the Google Tasks API and create OAuth 2.0 credentials to obtain your Client ID and Client Secret.

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

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

engine = create_engine("api:///?Profile=C:\profiles\GoogleTasks.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")

Execute SQL to Google Tasks

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

df = pandas.read_sql("SELECT Id, Kind FROM TaskLists WHERE Title = 'My Tasks'", engine)

Visualize Google Tasks Data

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

df.plot(kind="bar", x="Id", y="Kind")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Google Tasks 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("api:///?Profile=C:\profiles\GoogleTasks.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
df = pandas.read_sql("SELECT Id, Kind FROM TaskLists WHERE Title = 'My Tasks'", engine)

df.plot(kind="bar", x="Id", y="Kind")
plt.show()