Use Dash to Build to Web Apps on ShipEngine Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas module, and the Dash framework, you can build ShipEngine-connected web applications for ShipEngine data. This article shows how to connect to ShipEngine with the CData Connector and use pandas and Dash to build a simple web app for visualizing ShipEngine data.

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

Connecting to ShipEngine Data

Connecting to ShipEngine 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.

Start by setting the Profile connection property to the location of the ShipEngine Profile on disk (e.g. C:\profiles\ShipEngine.apip). Next, set the ProfileSettings connection property to the connection string for ShipEngine (see below).

ShipEngine API Profile Settings

Retrieve your ShipEngine API Key from the Dashboard at app.shipengine.com under API Management.

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

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

cnxn = mod.connect("Profile=C:\profiles\ShipEngine.apip;ProfileSettings='APIKey=your_api_key';")

Execute SQL to ShipEngine

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 BatchId, Error FROM BatchErrors WHERE BatchId = 'batch_123'", 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-apiedataplot'

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 ShipEngine data and configure the app layout.

trace = go.Bar(x=df.BatchId, y=df.Error, name='BatchId')

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='ShipEngine BatchErrors 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 ShipEngine data.

python api-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to ShipEngine 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.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\ShipEngine.apip;ProfileSettings='APIKey=your_api_key';")

df = pd.read_sql("SELECT BatchId, Error FROM BatchErrors WHERE BatchId = 'batch_123'", cnxn)
app_name = 'dash-apidataplot'

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.BatchId, y=df.Error, name='BatchId')

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='ShipEngine BatchErrors Data', barmode='stack')
		})
], className="container")

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