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Use Dash to Build to Web Apps on DataRobot Data

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

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

Connecting to DataRobot Data

Connecting to DataRobot 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.

To connect to DataRobot, the following connection properties are required: User, Password, and PredictionInstance. DataRobotKey may also be required depending on your type of DataRobot predictions instance. If using the Predictions API, DataFile is required. The CSV DataFile should include a header row as the first row of the datafile. APIKey is not required, but can be supplied. If not supplied, the driver will handle obtaining an APIKey.

User, DataRobotKey, and APIKey are the credentials for the DataRobot account.

ProjectID, DataFile, and ModelId are the parameters for the project, dataset, and model type.

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

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

cnxn = mod.connect("PredictionInstance=myinstance.orm.datarobot.com;DataFile=PATH\TO\input_file.csv;DataRobotKey=123-abc-456-def;User=username;Password=password;")

Execute SQL to DataRobot

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, Prediction1Value FROM Predictions WHERE Id = '1'", 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-datarobotedataplot'

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 DataRobot data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.Prediction1Value, 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='DataRobot Predictions 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 DataRobot data.

python datarobot-dash.py

Free Trial & More Information

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

cnxn = mod.connect("PredictionInstance=myinstance.orm.datarobot.com;DataFile=PATH\TO\input_file.csv;DataRobotKey=123-abc-456-def;User=username;Password=password;")

df = pd.read_sql("SELECT Id, Prediction1Value FROM Predictions WHERE Id = '1'", cnxn)
app_name = 'dash-datarobotdataplot'

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.Prediction1Value, 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='DataRobot Predictions Data', barmode='stack')
		})
], className="container")

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