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Use Dash to Build to Web Apps on Quandl Data

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

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

Connecting to Quandl Data

Connecting to Quandl 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.

Quandl uses an API key for authentication. See the help documentation for a guide to obtaining the APIKey property.

Additionally, set the DatabaseCode connection property to the code identifying the Database whose Datasets you want to query with SQL. You can search the available Databases by querying the Databases view.

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

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

cnxn = mod.connect("APIKey=abc123;DatabaseCode=WIKI;")

Execute SQL to Quandl

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 Date, Volume FROM AAPL WHERE Collapse = 'Daily'", 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-quandledataplot'

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 Quandl data and configure the app layout.

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

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='Quandl AAPL 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 Quandl data.

python quandl-dash.py

Free Trial & More Information

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

cnxn = mod.connect("APIKey=abc123;DatabaseCode=WIKI;")

df = pd.read_sql("SELECT Date, Volume FROM AAPL WHERE Collapse = 'Daily'", cnxn)
app_name = 'dash-quandldataplot'

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.Date, y=df.Volume, name='Date')

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='Quandl AAPL Data', barmode='stack')
		})
], className="container")

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