Implementation:DistrictDataLabs Yellowbrick MissingValuesBar

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

Overview

Concrete tool for visualizing missing values per feature as a horizontal stacked bar chart, provided by the Yellowbrick contrib module.

Description

The MissingValuesBar renders a horizontal bar chart showing the count of missing (NaN) values for each feature in a dataset. When target labels are provided, bars are stacked by class to reveal whether missingness is associated with specific target values. It inherits from MissingDataVisualizer which handles feature name extraction.

Usage

Import this visualizer when performing initial data quality assessment to identify features with missing values and whether missingness patterns differ across target classes.

Code Reference

Source Location

• Repository: DistrictDataLabs_Yellowbrick
• File: yellowbrick/contrib/missing/bar.py
• Lines: 1-269

Signature

class MissingValuesBar(MissingDataVisualizer):
    def __init__(self, width=0.5, color=None, colors=None, classes=None, **kwargs):
        """Missing values bar chart visualizer."""

def missing_bar(X, y=None, ax=None, classes=None, width=0.5, color="black", **kwargs):
    """Quick method for one-off missing values bar visualization."""

Import

from yellowbrick.contrib.missing import MissingValuesBar
from yellowbrick.contrib.missing.bar import missing_bar

I/O Contract

Inputs

Name	Type	Required	Description
X	array-like or DataFrame	Yes	Feature data with potential NaN values
y	array-like	No	Target labels for stacked bars
classes	list of str	No	Class names for legend
width	float	No	Bar width (default: 0.5)

Outputs

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

Usage Examples

import numpy as np
import pandas as pd
from yellowbrick.contrib.missing import MissingValuesBar

# Create data with missing values
df = pd.DataFrame({
    "A": [1, np.nan, 3, np.nan, 5],
    "B": [np.nan, 2, 3, 4, 5],
    "C": [1, 2, np.nan, 4, np.nan],
})

viz = MissingValuesBar()
viz.fit(df.values)
viz.show()

Related Pages

• Environment:DistrictDataLabs_Yellowbrick_Python_Scikit_Learn_Environment