Implementation:DistrictDataLabs Yellowbrick FrequencyVisualizer

Knowledge Sources	DistrictDataLabs_Yellowbrick
Domains	NLP, Visualization
Last Updated	2026-02-08 05:00 GMT

Overview

Concrete tool for visualizing the frequency distribution of terms in a text corpus as a bar chart, provided by the Yellowbrick text module.

Description

The FrequencyVisualizer displays the most frequently occurring terms from a pre-vectorized document-term matrix as a bar chart. It supports both horizontal and vertical orientations and allows limiting the number of displayed terms. The input must be a document-term matrix (e.g., from CountVectorizer or TfidfVectorizer) along with the feature names.

Usage

Import this visualizer when exploring the most common terms in a corpus after vectorization. It works with the output of scikit-learn text vectorizers.

Code Reference

Source Location

• Repository: DistrictDataLabs_Yellowbrick
• File: yellowbrick/text/freqdist.py
• Lines: 1-317

Signature

class FrequencyVisualizer(TextVisualizer):
    def __init__(
        self,
        features,
        ax=None,
        n=50,
        orient="h",
        color=None,
        **kwargs,
    ):
        """Frequency distribution bar chart for text terms."""

def freqdist(
    features, X, y=None, ax=None, n=50, orient="h", color=None, show=True, **kwargs,
):
    """Quick method for one-off frequency distribution visualization."""

FreqDistVisualizer = FrequencyVisualizer  # Backwards compatibility alias

Import

from yellowbrick.text import FreqDistVisualizer
from yellowbrick.text.freqdist import freqdist

I/O Contract

Inputs

Name	Type	Required	Description
features	list of str	Yes	Feature/term names from vectorizer
X	sparse matrix	Yes	Document-term matrix (fit)
n	int	No	Number of top terms to display (default: 50)
orient	str	No	Bar orientation: "h" or "v" (default: "h")

Outputs

Name	Type	Description
ax	matplotlib.Axes	Axes with frequency bar chart

Usage Examples

from sklearn.feature_extraction.text import CountVectorizer
from yellowbrick.text import FreqDistVisualizer
from yellowbrick.datasets import load_hobbies

corpus = load_hobbies()
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus.data)

viz = FreqDistVisualizer(vectorizer.get_feature_names_out(), n=20)
viz.fit(X)
viz.show()

Related Pages

• Environment:DistrictDataLabs_Yellowbrick_Python_Scikit_Learn_Environment