Use Dash to Build to Web Apps on Facebook Data

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Facebook Python Connector

Python Connector Libraries for Facebook Data Connectivity. Integrate Facebook with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Facebook Data

Connecting to Facebook 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.

Most tables require user authentication as well as application authentication. Facebook uses the OAuth authentication standard. To authenticate to Facebook, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can obtain your own by registering an app with Facebook.

See the Getting Started chapter of the help documentation for a guide to using OAuth.

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

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

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

Execute SQL to Facebook

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 FromName, LikesCount FROM Posts WHERE Target = 'thesimpsons'", 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-facebookedataplot'

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 Facebook data and configure the app layout.

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

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='Facebook Posts 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 Facebook data.

python facebook-dash.py

Free Trial & More Information

Download a free, 30-day trial of the Facebook Python Connector to start building Python apps with connectivity to Facebook 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.facebook as mod
import plotly.graph_objs as go

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

df = pd.read_sql("SELECT FromName, LikesCount FROM Posts WHERE Target = 'thesimpsons'", cnxn)
app_name = 'dash-facebookdataplot'

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.FromName, y=df.LikesCount, name='FromName')

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='Facebook Posts Data', barmode='stack')
		})
], className="container")

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