Use Dash to Build to Web Apps on Zoom Data

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 module, and the Dash framework, you can build Zoom-connected web applications for Zoom data. This article shows how to connect to Zoom with the CData Connector and use pandas and Dash to build a simple web app for visualizing Zoom data.

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

Connecting to Zoom Data

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

Zoom API Profile Settings

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

First you will need to create an OAuth app. To do so, navigate to https://marketplace.zoom.us/develop/create and click Create under the OAuth section. Select whether or not the app will be for individual users or for the entire account, and uncheck the box to publish the app. Give the app a name and click Create. You will then be given your Client Secret and Client ID

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 OAuth Client ID that is specified in your app settings.
• OAuthClientSecret: Set this to the OAuth Client Secret that is specified in your app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.

After installing the CData Zoom Connector, follow the procedure below to install the other required modules and start accessing Zoom 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 Zoom 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.api as mod
import plotly.graph_objs as go

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

cnxn = mod.connect("Profile=C:\profiles\Zoom.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Zoom

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 Id, JobTitle FROM MeetingRegistrants WHERE State = 'NC'", 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-apiedataplot'

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 Zoom data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.JobTitle, name='Id')

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='Zoom MeetingRegistrants 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 Zoom data.

python api-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Zoom 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.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\Zoom.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Id, JobTitle FROM MeetingRegistrants WHERE State = 'NC'", cnxn)
app_name = 'dash-apidataplot'

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.Id, y=df.JobTitle, name='Id')

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='Zoom MeetingRegistrants Data', barmode='stack')
		})
], className="container")

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