Use Dash to Build to Web Apps on AfterShip 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 AfterShip-connected web applications for AfterShip data. This article shows how to connect to AfterShip with the CData Connector and use pandas and Dash to build a simple web app for visualizing AfterShip data.

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

Connecting to AfterShip Data

Connecting to AfterShip 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 AfterShip Profile on disk (e.g. C:\profiles\Aftership.apip). Next, set the ProfileSettings connection property to the connection string for AfterShip (see below).

AfterShip API Profile Settings

Obtain your API key from the AfterShip account management panel by navigating to My Account > Settings > API Keys.

After installing the CData AfterShip Connector, follow the procedure below to install the other required modules and start accessing AfterShip 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 AfterShip 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 AfterShip Connector to create a connection for working with AfterShip data.

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

Execute SQL to AfterShip

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 Slug, Name FROM AllCouriers WHERE Name = 'FedEx'", 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 AfterShip data and configure the app layout.

trace = go.Bar(x=df.Slug, y=df.Name, name='Slug')

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='AfterShip AllCouriers 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 AfterShip 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 AfterShip 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\Aftership.apip;ProfileSettings='APIKey=your_api_key';")

df = pd.read_sql("SELECT Slug, Name FROM AllCouriers WHERE Name = 'FedEx'", 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.Slug, y=df.Name, name='Slug')

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='AfterShip AllCouriers Data', barmode='stack')
		})
], className="container")

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