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Use Dash to Build to Web Apps on JSON Services

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

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

Connecting to JSON Services

Connecting to JSON services 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.

See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models JSON APIs as bidirectional database tables and JSON files as read-only views (local files, files stored on popular cloud services, and FTP servers). The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation for authentication guides.

After setting the URI and providing any authentication values, set DataModel to more closely match the data representation to the structure of your data.

The DataModel property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

  • Document (default): Model a top-level, document view of your JSON data. The data provider returns nested elements as aggregates of data.
  • FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
  • Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

See the Modeling JSON Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

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

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

cnxn = mod.connect("URI=C:\people.json;DataModel=Relational;")

Execute SQL to JSON

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 [ personal.name.first ], [ personal.name.last ] FROM people WHERE [ personal.name.last ] = 'Roberts'", 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-jsonedataplot'

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 JSON services and configure the app layout.

trace = go.Bar(x=df.[ personal.name.first ], y=df.[ personal.name.last ], name='[ personal.name.first ]')

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='JSON people 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 JSON services.

python json-dash.py

Free Trial & More Information

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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.json as mod
import plotly.graph_objs as go

cnxn = mod.connect("URI=C:\people.json;DataModel=Relational;")

df = pd.read_sql("SELECT [ personal.name.first ], [ personal.name.last ] FROM people WHERE [ personal.name.last ] = 'Roberts'", cnxn)
app_name = 'dash-jsondataplot'

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.[ personal.name.first ], y=df.[ personal.name.last ], name='[ personal.name.first ]')

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='JSON people Data', barmode='stack')
		})
], className="container")

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