Use Dash to Build to Web Apps on Vimeo 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 Vimeo-connected web applications for Vimeo data. This article shows how to connect to Vimeo with the CData Connector and use pandas and Dash to build a simple web app for visualizing Vimeo data.

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

Connecting to Vimeo Data

Connecting to Vimeo 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.

Vimeo is a professional video hosting platform. The Vimeo API uses personal access tokens (bearer tokens) to enable secure access to video metadata, user information, channels, groups, categories, and related resources.

Using API Key Authentication

To authenticate to the Vimeo API, you will need to provide a personal access token. To obtain your access token:

1. Log in to your Vimeo account at https://vimeo.com
2. Navigate to https://developer.vimeo.com/apps
3. Create a new app or select an existing app
4. Under "Personal Access Tokens", click "Generate" to create a new token
5. Select the required scopes: public and private for read access
6. Copy the generated token

After obtaining your access token, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Vimeo personal access token.

Example connection string

Profile=C:\profiles\Vimeo.apip;ProfileSettings='APIKey=your_personal_access_token';

After installing the CData Vimeo Connector, follow the procedure below to install the other required modules and start accessing Vimeo 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 Vimeo 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 Vimeo Connector to create a connection for working with Vimeo data.

cnxn = mod.connect("Profile=C:\profiles\Vimeo.apip;ProfileSettings='APIKey=your_personal_access_token';")

Execute SQL to Vimeo

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 ,  FROM Videos WHERE UserUri = '/users/12345678'", 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 Vimeo data and configure the app layout.

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

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='Vimeo Videos 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 Vimeo 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 Vimeo 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\Vimeo.apip;ProfileSettings='APIKey=your_personal_access_token';")

df = pd.read_sql("SELECT ,  FROM Videos WHERE UserUri = '/users/12345678'", 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., y=df., name='')

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='Vimeo Videos Data', barmode='stack')
		})
], className="container")

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