How to Visualize Hugging Face 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 Hugging Face-connected Python applications and scripts for visualizing Hugging Face data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Hugging Face data, execute queries, and visualize the results.

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

Connecting to Hugging Face Data

Connecting to Hugging Face 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.

HuggingFace Hub uses token-based authentication to enable access to its API. The API provides access to machine learning models, datasets, spaces, papers, and other resources on the HuggingFace Hub platform.

Using API Key Authentication

To authenticate to HuggingFace Hub, you will need to provide an API Key (Access Token). To obtain your access token:

1. Log in to your HuggingFace account at https://huggingface.co
2. Navigate to Settings > Access Tokens
3. Click "New token" to create a new access token
4. Select the appropriate permissions (read or write)
5. Copy the token value

After obtaining your access token, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your HuggingFace access token.

Example connection string

Profile=C:\profiles\HuggingFace.apip;ProfileSettings='APIKey=hf_xxxxxxxxxxxxxxxxxxxx';

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

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

engine = create_engine("api:///?Profile=C:\profiles\HuggingFace.apip&ProfileSettings='APIKey=hf_xxxxxxxxxxxxxxxxxxxx'")

Execute SQL to Hugging Face

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 Collections WHERE  = ''", engine)

Visualize Hugging Face Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Hugging Face 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 Hugging Face 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\HuggingFace.apip&ProfileSettings='APIKey=hf_xxxxxxxxxxxxxxxxxxxx'")
df = pandas.read_sql("SELECT ,  FROM Collections WHERE  = ''", engine)

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