Asana API Tutorial Notebook - UMD Classes -- Tutorials

What this Notebook Does:

Shows how to authenticate and interact with Asana via our custom API layer.
Demonstrates how to fetch tasks and comments for a specified project and time period.
Computes simple statistics (e.g., tasks created, tasks completed, number of comments).
Includes unit tests to ensure the code runs correctly.

Prerequisites:

A valid Asana Personal Access Token (PAT).

Instructions:

Recommended to your ASANA_ACCESS_TOKEN environment variable before running.

%load_ext autoreload
%autoreload 2
%matplotlib inline

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

import datetime
import logging

import helpers.hdbg as hdbg
import helpers.hpandas as hpandas
import helpers.hnotebook as hnotebo
import utils

hdbg.init_logger(verbosity=logging.INFO)

_LOG = logging.getLogger(__name__)

hnotebo.config_notebook()

WARNING: Logger already initialized: skipping

Define Config¶

Here we define all parameters in a single config dictionary. ou can easily modify:

The project_id to analyze a different project.
The start_date and end_date to change the timeframe.

today = datetime.datetime.now()
one_month_ago = today - datetime.timedelta(days=30)
config = {
    # Replace with a valid project ID from your Asana workspace.
    "project_id": ["1208279350109582"],
    "start_date": one_month_ago.isoformat(),
    "end_date": today.isoformat(),
    "access_token": "2/1208871906331279/1208966663406154:1c6f6b89083e73c22241670b11bf00ba",
}

Intialize asana client¶

client = utils.AsanaClient(access_token=config["access_token"])

Fetching Task Data¶

Using the parameters in config, we’ll fetch:

Tasks created within the start_date and end_date
Tasks completed within the same range

The fetch_tasks() function returns a DataFrame with columns like:

task_id
name
assignee
created_at
completed_at

# Fetch tasks created in the given period.
tasks_df = utils.fetch_tasks(
    client,
    project_ids=config["project_id"],
    start_date=config["start_date"],
    end_date=config["end_date"],
)
# Fetch tasks completed in the given period.
_LOG.info(
    "Created_taaks_df = \n%s",
    hpandas.df_to_str(tasks_df, log_level=logging.INFO),
)

INFO  Created_taaks_df = 
None

Fetching Comments (Stories)¶

We now fetch comments for the tasks that were created or completed in the time window. get_task_comments:

Takes a list of task_ids.
Returns a DataFrame with task_id, comment_text, comment_author, comment_created_at.

task_ids = tasks_df["task_id"].tolist() if not tasks_df.empty else []
tasks_comments_df = utils.fetch_comments(client, task_ids)
_LOG.info(
    "Comments df = \n %s",
    hpandas.df_to_str(tasks_comments_df, log_level=logging.INFO),
)

INFO  Comments df = 
 None

Computing Statistics¶

We’ll compute:

Number of tasks created in the period.
Number of comments on tasks created in the period.

num_created_tasks = len(tasks_df)
num_comments_on_created = len(tasks_comments_df)
_LOG.info("Number of tasks created in the period: %s", num_created_tasks)
_LOG.info("Number of comments on created tasks: %s", num_comments_on_created)

INFO  Number of tasks created in the period: 17
INFO  Number of comments on created tasks: 11

Statistics for All Users¶

We can aggregate by user (assignee) to see how many tasks each user created or completed.

Tasks Created per User: If created_tasks_df includes assignee, we can group by that column.

if not tasks_df.empty and "assignee" in tasks_df.columns:
    tasks_created_by_user = (
        tasks_df.groupby("assignee")["task_id"].count().reset_index()
    )
    tasks_created_by_user.columns = ["assignee", "tasks_created_count"]
    print("Tasks Created by User:")
    print(tasks_created_by_user)
else:
    print("No tasks created or 'assignee' information not available.")

Tasks Created by User:
                  assignee  tasks_created_count
0         Krishna P Taduri                    1
1  Prahar Kaushikbhai Modi                    1
2           Shaunak Dhande                    8

Tasks Completed per User:

Similarly, for completed tasks:

completed_tasks_df = tasks_df[tasks_df["task_status"] == "Completed"]
if not completed_tasks_df.empty and "assignee" in completed_tasks_df.columns:
    tasks_completed_by_user = (
        completed_tasks_df.groupby("assignee")["task_id"].count().reset_index()
    )
    tasks_completed_by_user.columns = ["assignee", "tasks_completed_count"]
    print("Tasks Completed by User:")
    print(tasks_completed_by_user)
else:
    print("No tasks completed or 'assignee' information not available.")

No tasks completed or 'assignee' information not available.

Comments per User:

For comments, we have author. We can see how many comments each user made during this period for both created and completed tasks.

if not tasks_comments_df.empty and "author" in tasks_comments_df.columns:
    comments_by_user = (
        tasks_comments_df.groupby("author")["task_id"].count().reset_index()
    )
    comments_by_user.columns = ["author", "comments_count"]
    print("Comments by User:")
    print(comments_by_user)
else:
    print("No comments found or 'comment_author' information not available.")

Comments by User:
           author  comments_count
0      GP Saggese              10
1  Shaunak Dhande               1