Use Dash to Build to Web Apps on Highrise Data

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Highrise Python Connector

Python Connector Libraries for Highrise Data Connectivity. Integrate Highrise with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Highrise Data

Connecting to Highrise 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.

Highrise uses the OAuth authentication standard. To authenticate to Highrise, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app with Highrise. You will also need to set the AccountId to connect to data.

See the "Getting Started" section in the help documentation for a guide to using OAuth.

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost;AccountId=MyAccountId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Highrise

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 Name, Price FROM Deals WHERE GroupId = 'MyGroupId'", 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-highriseedataplot'

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 Highrise data and configure the app layout.

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

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='Highrise Deals 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 Highrise data.

python highrise-dash.py

Free Trial & More Information

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost;AccountId=MyAccountId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Name, Price FROM Deals WHERE GroupId = 'MyGroupId'", cnxn)
app_name = 'dash-highrisedataplot'

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.Name, y=df.Price, name='Name')

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='Highrise Deals Data', barmode='stack')
		})
], className="container")

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