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

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

Connecting to HubDB Data

Connecting to HubDB 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.

There are two authentication methods available for connecting to HubDB data source: OAuth Authentication with a public HubSpot application and authentication with a Private application token.

Using a Custom OAuth App

AuthScheme must be set to "OAuth" in all OAuth flows. Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).

Follow the steps below to register an application and obtain the OAuth client credentials:

1. Log into your HubSpot app developer account.
   • Note that it must be an app developer account. Standard HubSpot accounts cannot create public apps.
2. On the developer account home page, click the Apps tab.
3. Click Create app.
4. On the App info tab, enter and optionally modify values that are displayed to users when they connect. These values include the public application name, application logo, and a description of the application.
5. On the Auth tab, supply a callback URL in the "Redirect URLs" box.
   • If you're creating a desktop application, set this to a locally accessible URL like http://localhost:33333.
   • If you are creating a Web application, set this to a trusted URL where you want users to be redirected to when they authorize your application.
6. Click Create App. HubSpot then generates the application, along with its associated credentials.
7. On the Auth tab, note the Client ID and Client secret. You will use these later to configure the driver.

8. Under Scopes, select any scopes you need for your application's intended functionality.

   A minimum of the following scopes is required to access tables:

   • hubdb
   • oauth
   • crm.objects.owners.read
9. Click Save changes.
10. Install the application into a production portal with access to the features that are required by the integration.
    • Under "Install URL (OAuth)", click Copy full URL to copy the installation URL for your application.
    • Navigate to the copied link in your browser. Select a standard account in which to install the application.
    • Click Connect app. You can close the resulting tab.

Using a Private App

To connect using a HubSpot private application token, set the AuthScheme property to "PrivateApp."

You can generate a private application token by following the steps below:

1. In your HubDB account, click the settings icon (the gear) in the main navigation bar.
2. In the left sidebar menu, navigate to Integrations > Private Apps.
3. Click Create private app.
4. On the Basic Info tab, configure the details of your application (name, logo, and description).
5. On the Scopes tab, select Read or Write for each scope you want your private application to be able to access.
6. A minimum of hubdb and crm.objects.owners.read is required to access tables.
7. After you are done configuring your application, click Create app in the top right.
8. Review the info about your application's access token, click Continue creating, and then Show token.
9. Click Copy to copy the private application token.

To connect, set PrivateAppToken to the private application token you retrieved.

Follow the procedure below to install the required modules and start accessing HubDB 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 HubDB Data in Python

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

engine = create_engine("hubdb:///?AuthScheme=OAuth&OAuthClientID=MyOAuthClientID&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH")

Execute SQL to HubDB

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT PartitionKey, Name FROM NorthwindProducts WHERE Id = '1'", engine)

Visualize HubDB Data

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

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

👁 HubDB data in a Python plot (Salesforce is shown).

Free Trial & More Information

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

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Full Source Code

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

engine = create_engine("hubdb:///?AuthScheme=OAuth&OAuthClientID=MyOAuthClientID&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT PartitionKey, Name FROM NorthwindProducts WHERE Id = '1'", engine)

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