class_docstr: FeatureImportances visualizer
class_docstr_tok: ["FeatureImportances","visualizer"]
docstr: Integration test of visualizer with numpy array with mixed dtypes in a single column
docstr_tok: ["Integration","test","of","visualizer","with","numpy","array","with","mixed","dtypes","in","a","single","column"]
code: def test_missingvaluesbar_numpy_with_mixed_dtypes(self):
      """
      Integration test of visualizer with numpy array with mixed dtypes in a single column
      """
      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,
      )
  
      # add nan values to a range of values in the matrix
      X[X > 1.5] = np.nan
  
      rng = np.random.default_rng(2021)
      mixed_dtype_choices = np.array(
          [
              "apples",
              "pears",
              "peaches",
              "",
              np.nan,
              "bananas",
              1,
              2.4,
              5.6,
              False,
              True,
          ]
      )
      mixed_dtypes = rng.choice(mixed_dtype_choices, (400, 1))
  
      X_with_mixed_dtypes = np.append(X, mixed_dtypes, axis=1)
  
      features = [str(n) for n in range(11)]
      viz = MissingValuesBar(features=features)
      viz.fit(X_with_mixed_dtypes, y)
      viz.finalize()
  
      self.assert_images_similar(viz, tol=5)
  
file_path: yellowbrick/tests/test_contrib/test_missing/test_bar.py
imports: import os
  import pytest
  import numpy
  from tests.base import VisualTestCase
  from sklearn.datasets import make_classification
  from yellowbrick.contrib.missing.bar import *
label_desc: Use image node_id 9 for calling the TestMissingBarVisualizer obj's underlying member method code with example usage: obj.test_missingvaluesbar_numpy_with_mixed_dtypes() without return types