add pandas-numpy equality test

kmodes/tests/test_kmodes.py

@@ -6,6 +6,7 @@
 import unittest
 
 import numpy as np
+import pandas as pd
 
 from kmodes.kmodes import KModes
 from kmodes.util.dissim import ng_dissim, jaccard_dissim_binary, jaccard_dissim_label
@@ -575,3 +576,9 @@ def test_kmodes_fit_predict(self):
         data1 = kmodes.fit_predict(TEST_DATA, sample_weight=sample_weight)
         data2 = kmodes.fit(TEST_DATA, sample_weight=sample_weight).predict(TEST_DATA)
         assert_cluster_splits_equal(data1, data2)
+
+    def test_pandas_numpy_equality(self):
+        kmodes = KModes(n_clusters=4, init='Cao', random_state=42)
+        result_np = kmodes.fit_predict(SOYBEAN)
+        result_pd = kmodes.fit_predict(pd.DataFrame(SOYBEAN))
+        np.testing.assert_array_equal(result_np, result_pd)

kmodes/tests/test_kprototypes.py

@@ -6,6 +6,7 @@
 import unittest
 
 import numpy as np
+import pandas as pd
 
 from kmodes import kprototypes
 from kmodes.tests.test_kmodes import assert_cluster_splits_equal
@@ -421,3 +422,9 @@ def test_kmodes_fit_predict_equality(self):
         data1 = model1.predict(STOCKS, categorical=[1, 2])
         data2 = kproto.fit_predict(STOCKS, categorical=[1, 2], sample_weight=sample_weight)
         assert_cluster_splits_equal(data1, data2)
+
+    def test_pandas_numpy_equality(self):
+        kproto = kprototypes.KPrototypes(n_clusters=4, init='Cao', random_state=42)
+        result_np = kproto.fit_predict(STOCKS, categorical=[1, 2])
+        result_pd = kproto.fit_predict(pd.DataFrame(STOCKS), categorical=[1, 2])
+        np.testing.assert_array_equal(result_np, result_pd)

setup.py

@@ -34,6 +34,7 @@
         'dev': [
             'pytest',
             'pytest-cov',
+            'pandas'
         ]
     },
     classifiers=['Development Status :: 3 - Alpha',