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

Use pandas to Visualize Asana Data in Python



The CData Python Connector for Asana enables you use pandas and other modules to analyze and visualize live Asana 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 Asana, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Asana-connected Python applications and scripts for visualizing Asana data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Asana data, execute queries, and visualize the results.

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

Connecting to Asana Data

Connecting to Asana 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 optionally set the following to refine the data returned from Asana.

  • WorkspaceId: Set this to the globally unique identifier (gid) associated with your Asana Workspace to only return projects from the specified workspace. To get your workspace id, navigate to https://app.asana.com/api/1.0/workspaces while logged into Asana. This displays a JSON object containing your workspace name and Id.
  • ProjectId: Set this to the globally unique identifier (gid) associated with your Asana Project to only return data mapped under the specified project. Project IDs can be found in the URL of your project's Overview page. This will be the numbers directly after /0/.

Connect Using OAuth Authentication

You must use OAuth to authenticate with Asana. OAuth requires the authenticating user to interact with Asana using the browser. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

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

engine = create_engine("asana:///?OAuthClientId=YourClientId&OAuthClientSecret=YourClientSecret&CallbackURL='http://localhost:33333'&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Asana

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, WorkspaceId FROM projects WHERE Archived = 'true'", engine)

Visualize Asana Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Asana to start building Python apps and scripts with connectivity to Asana 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("asana:///?OAuthClientId=YourClientId&OAuthClientSecret=YourClientSecret&CallbackURL='http://localhost:33333'&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, WorkspaceId FROM projects WHERE Archived = 'true'", engine)

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