How to Visualize Todoist 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 Todoist-connected Python applications and scripts for visualizing Todoist data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Todoist data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Todoist data in Python. When you issue complex SQL queries from Todoist, the driver pushes supported SQL operations, like filters and aggregations, directly to Todoist and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Todoist Data

Connecting to Todoist 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 Todoist Profile on disk (e.g. C:\profiles\Todoist.apip). Next, set the ProfileSettings connection property to the connection string for Todoist (see below).

Todoist API Profile Settings

To authenticate to Todoist, and connect to your own data or to allow other users to connect to their data, you can use the OAuth standard.

First, register an OAuth application with Todoist. To do so, go to App Management Console, create a new application and configure a valid OAuth redirect URL. Your Oauth application will be assigned a client id and a client secret.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• OAuthClientId: Set this to the client_id that is specified in you app settings.
• OAuthClientSecret: Set this to the client_secret that is specified in you app settings.
• CallbackURL: Set this to the Redirect URI that is specified in your app settings

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

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

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

Execute SQL to Todoist

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, Priority FROM Tasks WHERE Completed = 'false'", engine)

Visualize Todoist Data

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

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

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

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 Todoist 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("api:///?Profile=C:\profiles\Todoist.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
df = pandas.read_sql("SELECT Id, Priority FROM Tasks WHERE Completed = 'false'", engine)

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