class_docstr: Test the PrecisionRecallCurve visualizer
class_docstr_tok: ["Test","the","PrecisionRecallCurve","visualizer"]
docstr: Test the precision_recall_curve with Pandas dataframes
docstr_tok: ["Test","the","precision_recall_curve","with","Pandas","dataframes"]
code: def test_pandas_integration(self):
      """
      Test the precision_recall_curve with Pandas dataframes
      """
      X, y = load_occupancy(return_dataset=True).to_pandas()
  
      model = DecisionTreeClassifier(random_state=14)
  
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=0.2, shuffle=True, random_state=555
      )
  
      oz = PrecisionRecallCurve(
          model,
          per_class=True,
          micro=False,
          fill_area=False,
          iso_f1_curves=True,
          ap_score=False,
          classes=["unoccupied", "occupied"],
      )
      oz.fit(X_train, y_train)
      oz.score(X_test, y_test)
  
      oz.finalize()
  
      # Miniconda & Appveyor: images not close (RMS 5.089)
      self.assert_images_similar(oz, tol=5.5)
  
imports: import sys
  import pytest
  import matplotlib
  from yellowbrick.exceptions import *
  from yellowbrick.classifier.prcurve import *
  from yellowbrick.datasets import load_occupancy
  from tests.base import IS_WINDOWS_OR_CONDA, VisualTestCase
  from .test_rocauc import FakeClassifier
  from sklearn.svm import LinearSVC, SVC
  from sklearn.naive_bayes import GaussianNB
  from sklearn.datasets import make_regression
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.linear_model import RidgeClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import MinMaxScaler
label_desc: Use image node_id 10 for calling the TestPrecisionRecallCurve obj's underlying member method code with example usage: obj.test_pandas_integration() without return types