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

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

Connecting to Pinecone Data

Connecting to Pinecone 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.

Authentication

To authenticate to Pinecone, and connect to your own data or to allow other users to connect to their data, you can use API Key authentication.

Using API Key Authentication

To authenticate using an API Key, you need to obtain your API Key from your Pinecone console at https://app.pinecone.io/.

You can then connect by setting the AuthScheme to APIKey and providing your API key:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your API key from Pinecone.

Example connection strings:

Standard API Key Configuration:

Profile=C:\profiles\Pinecone.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key;APIVersion=2025-10';

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

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

engine = create_engine("api:///?Profile=C:\profiles\Pinecone.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key&APIVersion=2025-10'")

Execute SQL to Pinecone

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

df = pandas.read_sql("SELECT ,  FROM Indexes WHERE Name = 'my-index'", engine)

Visualize Pinecone Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Pinecone 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("api:///?Profile=C:\profiles\Pinecone.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key&APIVersion=2025-10'")
df = pandas.read_sql("SELECT ,  FROM Indexes WHERE Name = 'my-index'", engine)

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