Use Dash to Build to Web Apps on Pinterest Data

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Pinterest Python Connector

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



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

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

Connecting to Pinterest Data

Connecting to Pinterest 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.

Pinterest authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Pinterest developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL.

Set InitiateOAuth to GETANDREFRESH and set OAuthClientId, OAuthClientSecret, and CallbackURL based on the property values for the app you created.

See the Help documentation for other OAuth authentication flows.

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

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

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;CallbackURL='https://localhost:33333'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Pinterest

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, Username FROM Users WHERE FirstName = 'Jane'", 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-pinterestedataplot'

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 Pinterest data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.Username, 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='Pinterest Users 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 Pinterest data.

python pinterest-dash.py

Free Trial & More Information

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

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;CallbackURL='https://localhost:33333'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Id, Username FROM Users WHERE FirstName = 'Jane'", cnxn)
app_name = 'dash-pinterestdataplot'

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.Username, 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='Pinterest Users Data', barmode='stack')
		})
], className="container")

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