Use Dash to Build to Web Apps on Microsoft Planner Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Microsoft Planner, the pandas module, and the Dash framework, you can build Microsoft Planner-connected web applications for Microsoft Planner data. This article shows how to connect to Microsoft Planner with the CData Connector and use pandas and Dash to build a simple web app for visualizing Microsoft Planner data.

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

Connecting to Microsoft Planner Data

Connecting to Microsoft Planner 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 without setting any connection properties for your user credentials. Below are the minimum connection properties required to connect.

• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
• Tenant (optional): Set this if you wish to authenticate to a different tenant than your default. This is required to work with an organization not on your default Tenant.

When you connect the Driver opens the MS Planner OAuth endpoint in your default browser. Log in and grant permissions to the Driver. The Driver then completes the OAuth process.

1. Extracts the access token from the callback URL and authenticates requests.
2. Obtains a new access token when the old one expires.
3. Saves OAuth values in OAuthSettingsLocation to be persisted across connections.

After installing the CData Microsoft Planner Connector, follow the procedure below to install the other required modules and start accessing Microsoft Planner through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize Microsoft Planner Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.microsoftplanner as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Microsoft Planner Connector to create a connection for working with Microsoft Planner data.

cnxn = mod.connect("OAuthClientId=MyApplicationId;OAuthClientSecret=MySecretKey;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Microsoft Planner

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

df = pd.read_sql("SELECT TaskId, startDateTime FROM Tasks WHERE TaskId = 'BCrvyMoiLEafem-3RxIESmUAHbLK'", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-microsoftplanneredataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our Microsoft Planner data and configure the app layout.

trace = go.Bar(x=df.TaskId, y=df.startDateTime, name='TaskId')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Microsoft Planner Tasks Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the Microsoft Planner data.

python microsoftplanner-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Microsoft Planner to start building Python apps with connectivity to Microsoft Planner data. Reach out to our Support Team if you have any questions.

Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.microsoftplanner as mod
import plotly.graph_objs as go

cnxn = mod.connect("OAuthClientId=MyApplicationId;OAuthClientSecret=MySecretKey;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT TaskId, startDateTime FROM Tasks WHERE TaskId = 'BCrvyMoiLEafem-3RxIESmUAHbLK'", cnxn)
app_name = 'dash-microsoftplannerdataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.TaskId, y=df.startDateTime, name='TaskId')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Microsoft Planner Tasks Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)