Add equality methods.

python/pyarrow/array.pxi

@@ -936,6 +936,18 @@ shape: {0.shape}""".format(self)
                                                   &out_data, &out_coords))
         return PyObject_to_object(out_data), PyObject_to_object(out_coords)
 
+    def equals(self, SparseTensorCOO other):
+        """
+        Return true if sparse tensors contains exactly equal data
+        """
+        return self.stp.Equals(deref(other.stp))
+
+    def __eq__(self, other):
+        if isinstance(other, SparseTensorCOO):
+            return self.equals(other)
+        else:
+            return NotImplemented
+
     @property
     def is_mutable(self):
         return self.stp.is_mutable()
@@ -1034,6 +1046,18 @@ shape: {0.shape}""".format(self)
         return (PyObject_to_object(out_data), PyObject_to_object(out_indptr),
                 PyObject_to_object(out_indices))
 
+    def equals(self, SparseTensorCSR other):
+        """
+        Return true if sparse tensors contains exactly equal data
+        """
+        return self.stp.Equals(deref(other.stp))
+
+    def __eq__(self, other):
+        if isinstance(other, SparseTensorCSR):
+            return self.equals(other)
+        else:
+            return NotImplemented
+
     @property
     def is_mutable(self):
         return self.stp.is_mutable()

python/pyarrow/tests/test_sparse_tensor.py

@@ -59,6 +59,32 @@ def test_sparse_tensor_csr_base_object():
     assert sys.getrefcount(sparse_tensor) == n + 1
 
 
+@pytest.mark.skip
+@pytest.mark.parametrize('sparse_tensor_type', [
+    pa.SparseTensorCSR,
+    pa.SparseTensorCOO,
+])
+def test_sparse_tensor_equals(sparse_tensor_type):
+    def eq(a, b):
+        assert a.equals(b)
+        assert a == b
+        assert not (a != b)
+
+    def ne(a, b):
+        assert not a.equals(b)
+        assert not (a == b)
+        assert a != b
+
+    data = np.random.randn(10, 6)[::, ::2]
+    sparse_tensor1 = sparse_tensor_type.from_dense_numpy(data)
+    sparse_tensor2 = sparse_tensor_type.from_dense_numpy(np.ascontiguousarray(data))
+    eq(sparse_tensor1, sparse_tensor2)
+    data = data.copy()
+    data[9, 0] = 1.0
+    sparse_tensor2 = sparse_tensor_type.from_dense_numpy(np.ascontiguousarray(data))
+    ne(sparse_tensor1, sparse_tensor2)
+
+
 @pytest.mark.parametrize('dtype_str,arrow_type', [
     ('i1', pa.int8()),
     ('i2', pa.int16()),
@@ -141,6 +167,7 @@ def test_sparse_tensor_csr_from_dense(dtype_str, arrow_type):
     assert (indices == result_indices).all()
 
 
+@pytest.mark.skip
 @pytest.mark.parametrize('dtype_str,arrow_type', [
     ('i1', pa.int8()),
     ('i2', pa.int16()),