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Use Dash to Build to Web Apps on YouTube Analytics Data

The CData Python Connector for YouTube Analytics enables you to create Python applications that use pandas and Dash to build YouTube Analytics-connected web apps.

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

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

Connecting to YouTube Analytics Data

Connecting to YouTube Analytics 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.

YouTube Analytics uses the OAuth authentication standard. You can use the embedded CData OAuth credentials or you can register an application with Google to obtain your own.

In addition to the OAuth values, to access YouTube Analytics data set ChannelId to the Id of a YouTube channel. You can obtain the channel Id in the advanced account settings for your channel. If not specified, the channel of the currently authenticated user will be used.

If you want to generate content owner reports, specify the ContentOwnerId property. This is the Id of the copyright holder for content in YouTube's rights management system. The content owner is the person or organization that claims videos and sets their monetization policy.

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

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

cnxn = mod.connect("ContentOwnerId=MyContentOwnerId;ChannelId=MyChannelId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to YouTube Analytics

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 Snippet_Title, ContentDetails_ItemCount FROM Groups WHERE Mine = 'True'", 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-youtubeanalyticsedataplot'

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 YouTube Analytics data and configure the app layout.

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

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='YouTube Analytics Groups 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 YouTube Analytics data.

python youtubeanalytics-dash.py

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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.youtubeanalytics as mod
import plotly.graph_objs as go

cnxn = mod.connect("ContentOwnerId=MyContentOwnerId;ChannelId=MyChannelId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Snippet_Title, ContentDetails_ItemCount FROM Groups WHERE Mine = 'True'", cnxn)
app_name = 'dash-youtubeanalyticsdataplot'

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.Snippet_Title, y=df.ContentDetails_ItemCount, name='Snippet_Title')

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='YouTube Analytics Groups Data', barmode='stack')
		})
], className="container")

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