bar.rst

MissingValues Bar

The MissingValues Bar visualizer creates a bar graph that counts the number of missing values per feature column. If the target y is supplied to fit, a stacked bar chart is produced.

Without Targets Supplied

import numpy as np

from sklearn.datasets import make_classification from yellowbrick.contrib.missing import MissingValuesBar

# Make a classification dataset 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 NaN values X[X > 1.5] = np.nan features = ["Feature {}".format(str(n)) for n in range(10)]

# Instantiate the visualizer visualizer = MissingValuesBar(features=features)

visualizer.fit(X) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure

With Targets (y) Supplied

import numpy as np

from sklearn.datasets import make_classification from yellowbrick.contrib.missing import MissingValuesBar

# Make a classification dataset 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 NaN values X[X > 1.5] = np.nan features = ["Feature {}".format(str(n)) for n in range(10)]

# Instantiate the visualizer visualizer = MissingValuesBar(features=features)

visualizer.fit(X, y=y) # Supply the targets via y visualizer.show() # Finalize and render the figure

API Reference

yellowbrick.contrib.missing.bar