Fix for supporting DF categoricals unboxing

sdc/hiframes/api.py

@@ -43,6 +43,7 @@
     SeriesType,
     if_series_to_array_type)
 from numba.core.errors import TypingError
+from sdc.datatypes.categorical.types import Categorical
 
 
 def isna(arr, i):
@@ -162,7 +163,7 @@ def fix_df_array_list_str_impl(column):  # pragma: no cover
     if isinstance(column, RangeIndexType):
         return lambda column: np.array(column)
 
-    if isinstance(column, (types.Array, StringArrayType)):
+    if isinstance(column, (types.Array, StringArrayType, Categorical)):
         return lambda column: column
 
 

sdc/hiframes/boxing.py

@@ -127,10 +127,11 @@ def unbox_dataframe(typ, val, c):
                     series_obj = c.pyapi.object_getattr_string(val, typ.columns[col_idx])
                     arr_obj = c.pyapi.object_getattr_string(series_obj, "values")
                     ty_series = typ.data[col_idx]
-                    if isinstance(ty_series, types.Array):
-                        native_val = unbox_array(typ.data[col_idx], arr_obj, c)
-                    elif ty_series == string_array_type:
-                        native_val = unbox_str_series(string_array_type, series_obj, c)
+
+                    # FIXME: CategoricalType has wrong dtype attribute value (i.e. dtype of codes)
+                    # current implementation offers pd_dtype for this purpose, so use it
+                    column_dtype = ty_series.pd_dtype if isinstance(ty_series, Categorical) else ty_series.dtype
+                    native_val = _unbox_series_data(column_dtype, ty_series, arr_obj, c)
 
                     inst.setitem(c.context.get_constant(types.intp, i), native_val.value, incref=False)
 

sdc/tests/categorical/__init__.py

@@ -27,3 +27,4 @@
 from . import test_categorical
 from . import test_categoricaldtype
 from . import test_series_category
+from . import test_df_category

sdc/tests/categorical/test_df_category.py

@@ -0,0 +1,124 @@
+# *****************************************************************************
+# Copyright (c) 2020, Intel Corporation All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+#     Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#     Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
+# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
+# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
+# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+# *****************************************************************************
+
+from sdc.tests.test_base import TestCase
+
+import numpy as np
+import pandas as pd
+import numba as nb
+from numba import types
+
+from sdc.types import (
+    CategoricalDtypeType,
+    Categorical,
+)
+
+from sdc.hiframes.pd_dataframe_type import DataFrameType
+from sdc.tests.test_utils import skip_numba_jit
+
+
+class DFCategoryTest(TestCase):
+    """
+    Test for pandas DataFrames with CategoricalDtype.
+    """
+
+    def _pd_value(self):
+        return pd.DataFrame({'A': pd.Categorical([1, 2, 3, 2, 1])})
+
+    def test_typeof(self):
+        pd_value = self._pd_value()
+        nb_type = nb.typeof(pd_value)
+
+        assert(isinstance(nb_type, DataFrameType))
+        assert(nb_type.columns == ('A',))
+        assert(nb_type.index == types.none)
+        assert(nb_type.data[0].pd_dtype == CategoricalDtypeType(categories=[1, 2, 3], ordered=False))
+        assert(nb_type.data[0] == Categorical(CategoricalDtypeType(categories=[1, 2, 3], ordered=False)))
+
+    def test_unboxing(self):
+        @nb.njit
+        def func(c):
+            pass
+
+        pd_value = self._pd_value()
+        func(pd_value)
+
+    def test_boxing(self):
+        @nb.njit
+        def func(c):
+            return c
+
+        pd_value = self._pd_value()
+        boxed = func(pd_value)
+        assert(boxed.equals(pd_value))
+
+    @skip_numba_jit("capturing DFs (not only categoricals) as freevar not working")
+    def test_lowering(self):
+        pd_value = self._pd_value()
+
+        @nb.njit
+        def func():
+            return pd_value
+
+        boxed = func()
+        assert(boxed.equals(pd_value))
+
+    def test_constructor(self):
+        @nb.njit
+        def func():
+            return pd.DataFrame({'A': pd.Categorical([1, 2, 3, 2, 1])})
+
+        boxed = func()
+        assert(boxed.equals(self._pd_value()))
+
+    @skip_numba_jit("compiles, but category dtype not supported by df ctor")
+    def test_constructor_list(self):
+        @nb.njit
+        def func():
+            return pd.DataFrame({'A': list("12321")}, dtype='category')
+
+        boxed = func()
+        assert(boxed.equals(self._pd_value()))
+
+    @skip_numba_jit
+    def test_constructor_CategoricalDtype(self):
+        @nb.njit
+        def func():
+            return pd.DataFrame(data={'A': np.array([1, 2, 3, 2, 1])},
+                                dtype=pd.CategoricalDtype(categories=[1, 2, 3]))
+
+        boxed = func()
+        assert(boxed.equals(self._pd_value()))
+
+    @skip_numba_jit
+    def test_constructor_CategoricalDtype_list(self):
+        @nb.njit
+        def func():
+            return pd.DataFrame(data={'A': [1, 2, 3, 2, 1]},
+                                dtype=pd.CategoricalDtype(categories=[1, 2, 3]))
+
+        boxed = func()
+        assert(boxed.equals(self._pd_value()))