MissingValues Dispersion

The MissingValues Dispersion visualizer creates a chart that maps the position of missing values by the order of the index.


import numpy as np
from sklearn.datasets import make_classification

X, y = make_classification(
        n_samples=400, n_features=10, n_informative=2, n_redundant=3,
        n_classes=2, n_clusters_per_class=2, random_state=854
# assign some NaN values
X[X > 1.5] = np.nan
features = ["Feature {}".format(str(n)) for n in range(10)]

Without Targets Supplied

from yellowbrick.contrib.missing import MissingValuesDispersion

viz = MissingValuesDispersion(features=features)

With Targets (y) Supplied

from yellowbrick.contrib.missing import MissingValuesDispersion

viz = MissingValuesDispersion(features=features)
viz.fit(X, y=y) # supply the targets via y

API Reference

Dispersion visualizer for locations of missing values by column against index position.

class yellowbrick.contrib.missing.dispersion.MissingValuesDispersion(alpha=0.5, marker='|', classes=None, **kwargs)[source]

Bases: yellowbrick.contrib.missing.base.MissingDataVisualizer

The Missing Values Dispersion visualizer shows the locations of missing (nan) values in the feature dataset by the order of the index.

When y targets are supplied to fit, the output dispersion plot is color coded according to the target y that the element refers to.

alpha : float, default: 0.5

A value for bending elments with the background.

marker : matplotlib marker, default: |

The marker used for each element coordinate in the plot

classes : list, default: None

A list of class names for the legend. If classes is None and a y value is passed to fit then the classes are selected from the target vector.

kwargs : dict

Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers.


>>> from yellowbrick.contrib.missing import MissingValuesDispersion
>>> visualizer = MissingValuesDispersion()
>>> visualizer.fit(X, y=y)
>>> visualizer.poof()
features_ : np.array

The feature labels ranked according to their importance

classes_ : np.array

The class labels for each of the target values

draw(X, y, **kwargs)[source]

Called from the fit method, this method creates a scatter plot that draws each instance as a class or target colored point, whose location is determined by the feature data set.

If y is not None, then it draws a scatter plot where each class is in a different color.


Draws a multi dimensional dispersion chart, each color corresponds to a different target variable.


Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize.

kwargs: generic keyword arguments.

Gets the locations of nans in feature data and returns the coordinates in the matrix