Use Dash to Build to Web Apps on Twitter Ads Data

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Twitter Ads Python Connector

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



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

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

Connecting to Twitter Ads Data

Connecting to Twitter Ads 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.

All tables require authentication. You must use OAuth to authenticate with Twitter. OAuth requires the authenticating user to interact with Twitter using the browser. For more information, refer to the OAuth section in the Help documentation.

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

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

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

Execute SQL to Twitter Ads

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 EntityId, Entity FROM AdStats WHERE Entity = 'ORGANIC_TWEET'", 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-twitteradsedataplot'

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 Twitter Ads data and configure the app layout.

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

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='Twitter Ads AdStats 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 Twitter Ads data.

python twitterads-dash.py

Free Trial & More Information

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

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

df = pd.read_sql("SELECT EntityId, Entity FROM AdStats WHERE Entity = 'ORGANIC_TWEET'", cnxn)
app_name = 'dash-twitteradsdataplot'

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.EntityId, y=df.Entity, name='EntityId')

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='Twitter Ads AdStats Data', barmode='stack')
		})
], className="container")

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