Use pandas to Visualize HubSpot Data in Python

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

The CData Python Connector for HubSpot enables you use pandas and other modules to analyze and visualize live HubSpot data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for HubSpot, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build HubSpot-connected Python applications and scripts for visualizing HubSpot data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to HubSpot data, execute queries, and visualize the results.

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

Connecting to HubSpot Data

Connecting to HubSpot 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.

HubSpot uses the OAuth authentication standard. You can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can obtain your own by registering an app.

See the Getting Started chapter of the help documentation for a guide to using OAuth.

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

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

engine = create_engine("hubspot:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to HubSpot

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

df = pandas.read_sql("SELECT Slug, PageViews FROM Prospects WHERE Region = 'ONTARIO'", engine)

Visualize HubSpot Data

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

df.plot(kind="bar", x="Slug", y="PageViews")

Free Trial & More Information

Download a free, 30-day trial of the HubSpot Python Connector to start building Python apps and scripts with connectivity to HubSpot 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("hubspot:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Slug, PageViews FROM Prospects WHERE Region = 'ONTARIO'", engine)

df.plot(kind="bar", x="Slug", y="PageViews")