How to Visualize Highrise Data in Python with pandas

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 & Matplotlib modules, and the SQLAlchemy toolkit, you can build Highrise-connected Python applications and scripts for visualizing Highrise data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Highrise data, execute queries, and visualize the results.

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.

Follow the procedure below to install the required modules and start accessing Highrise through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Highrise Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Highrise data.

engine = create_engine("highrise:///?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 resultset in a DataFrame.

df = pandas.read_sql("SELECT Name, Price FROM Deals WHERE GroupId = 'MyGroupId'", engine)

Visualize Highrise Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Highrise data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Name", y="Price")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Highrise to start building Python apps and scripts with connectivity to Highrise data. Reach out to our Support Team if you have any questions.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("highrise:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost&AccountId=MyAccountId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Name, Price FROM Deals WHERE GroupId = 'MyGroupId'", engine)

df.plot(kind="bar", x="Name", y="Price")
plt.show()