diff --git a/tests/pipeline/test_factor.py b/tests/pipeline/test_factor.py
index c5873c1f..4eee9f2e 100644
--- a/tests/pipeline/test_factor.py
+++ b/tests/pipeline/test_factor.py
@@ -23,6 +23,7 @@ from numpy import (
 from numpy.random import randn, seed
 
 from zipline.errors import UnknownRankMethod
+from zipline.lib.labelarray import LabelArray
 from zipline.lib.rank import masked_rankdata_2d
 from zipline.lib.normalize import naive_grouped_rowwise_apply as grouped_apply
 from zipline.pipeline import Classifier, Factor, Filter, TermGraph
@@ -38,6 +39,7 @@ from zipline.testing import (
 )
 from zipline.utils.functional import dzip_exact
 from zipline.utils.numpy_utils import (
+    categorical_dtype,
     datetime64ns_dtype,
     float64_dtype,
     int64_dtype,
@@ -442,6 +444,7 @@ class FactorTestCase(BasePipelineTestCase):
         f = self.f
         m = Mask()
         c = C()
+        str_c = C(dtype=categorical_dtype)
 
         factor_data = array(
             [[1.0, 2.0, 3.0, 4.0],
@@ -463,12 +466,18 @@ class FactorTestCase(BasePipelineTestCase):
              [1, 1, 2, 2]],
             dtype=int64_dtype,
         )
+        string_classifier_data = LabelArray(
+            classifier_data.astype(str).astype(object),
+            missing_value=None,
+        )
 
         terms = {
             'vanilla': f.demean(),
             'masked': f.demean(mask=m),
             'grouped': f.demean(groupby=c),
+            'grouped_str': f.demean(groupby=str_c),
             'grouped_masked': f.demean(mask=m, groupby=c),
+            'grouped_masked_str': f.demean(mask=m, groupby=str_c),
         }
         expected = {
             'vanilla': array(
@@ -496,6 +505,9 @@ class FactorTestCase(BasePipelineTestCase):
                  [-0.500,  0.500,  0.000,    nan]]
             )
         }
+        # Changing the classifier dtype shouldn't affect anything.
+        expected['grouped_str'] = expected['grouped']
+        expected['grouped_masked_str'] = expected['grouped_masked']
 
         graph = TermGraph(terms)
         results = self.run_graph(
@@ -503,6 +515,7 @@ class FactorTestCase(BasePipelineTestCase):
             initial_workspace={
                 f: factor_data,
                 c: classifier_data,
+                str_c: string_classifier_data,
                 m: filter_data,
             },
             mask=self.build_mask(self.ones_mask(shape=factor_data.shape)),
diff --git a/zipline/lib/labelarray.py b/zipline/lib/labelarray.py
index b707f0cd..a3ef5027 100644
--- a/zipline/lib/labelarray.py
+++ b/zipline/lib/labelarray.py
@@ -211,6 +211,10 @@ class LabelArray(ndarray):
         # This is a property because it should be immutable.
         return self._missing_value
 
+    @property
+    def missing_value_code(self):
+        return self.reverse_categories[self.missing_value]
+
     def has_label(self, value):
         return value in self.reverse_categories
 
diff --git a/zipline/pipeline/factors/factor.py b/zipline/pipeline/factors/factor.py
index a76002a9..6a0b0b08 100644
--- a/zipline/pipeline/factors/factor.py
+++ b/zipline/pipeline/factors/factor.py
@@ -47,6 +47,7 @@ from zipline.utils.input_validation import expect_types
 from zipline.utils.math_utils import nanmean, nanstd
 from zipline.utils.numpy_utils import (
     bool_dtype,
+    categorical_dtype,
     coerce_to_dtype,
     datetime64ns_dtype,
     float64_dtype,
@@ -939,15 +940,26 @@ class GroupedRowTransform(Factor):
 
     def _compute(self, arrays, dates, assets, mask):
         data = arrays[0]
-        null_group_value = self.inputs[1].missing_value
-        group_labels = where(
-            mask,
-            arrays[1],
-            null_group_value,
-        )
+        groupby_expr = self.inputs[1]
+        if groupby_expr.dtype == int64_dtype:
+            group_labels = arrays[1]
+            null_label = self.inputs[1].missing_value
+        elif groupby_expr.dtype == categorical_dtype:
+            # Coerce our LabelArray into an isomorphic array of ints.  This is
+            # necessary because np.where doesn't know about LabelArrays or the
+            # void dtype.
+            group_labels = arrays[1].as_int_array()
+            null_label = arrays[1].missing_value_code
+        else:
+            raise TypeError(
+                "Unexpected groupby dtype: %s." % groupby_expr.dtype
+            )
+
+        # Make a copy with the null code written to masked locations.
+        group_labels = where(mask, group_labels, null_label)
 
         return where(
-            group_labels != null_group_value,
+            group_labels != null_label,
             naive_grouped_rowwise_apply(
                 data=data,
                 group_labels=group_labels,