move new constructor to dtypes/dtypes.py

Author: topper-123

pandas/core/arrays/categorical.py

@@ -200,71 +200,6 @@ def contains(cat, key, container):
         return any(loc_ in container for loc_ in loc)
 
 
-def create_categorical_dtype(values=None, categories=None, ordered=None,
-                             dtype=None):
-    """
-    Construct and return a :class:`~pandas.api.types.CategoricalDtype`.
-
-    This is a helper function, and specifically does not do the
-    factorization step, if that is needed.
-
-    Parameters
-    ----------
-    values : list-like, optional
-        The list-like must be 1-dimensional.
-    categories : list-like, optional
-        Categories for the CategoricalDtype.
-    ordered : bool, optional
-        Designating if the categories are ordered.
-    dtype : CategoricalDtype or the string "category", optional
-        If ``CategoricalDtype`` cannot be used together with
-        `categories` or `ordered`.
-
-    Returns
-    -------
-    CategoricalDtype
-
-    Examples
-    --------
-    >>> create_categorical_dtype()
-    CategoricalDtype(categories=None, ordered=None)
-    >>> create_categorical_dtype(categories=['a', 'b'], ordered=True)
-    CategoricalDtype(categories=['a', 'b'], ordered=True)
-    >>> dtype1 = CategoricalDtype(['a', 'b'], ordered=True)
-    >>> dtype2 = CategoricalDtype(['x', 'y'], ordered=False)
-    >>> c = Categorical([0, 1], dtype=dtype1, fastpath=True)
-    >>> create_categorical_dtype(c, ['x', 'y'], ordered=True, dtype=dtype2)
-    ValueError: Cannot specify `categories` or `ordered` together with `dtype`.
-
-    The supplied dtype takes precedence over values's dtype:
-
-    >>> create_categorical_dtype(c, dtype=dtype2)
-    CategoricalDtype(['x', 'y'], ordered=False)
-    """
-    if dtype is not None:
-        # The dtype argument takes precedence over values.dtype (if any)
-        if isinstance(dtype, compat.string_types):
-            if dtype == 'category':
-                dtype = CategoricalDtype(categories, ordered)
-            else:
-                msg = "Unknown dtype {dtype!r}"
-                raise ValueError(msg.format(dtype=dtype))
-        elif categories is not None or ordered is not None:
-            raise ValueError("Cannot specify `categories` or `ordered` "
-                             "together with `dtype`.")
-    elif is_categorical(values):
-        # If no "dtype" was passed, use the one from "values", but honor
-        # the "ordered" and "categories" arguments
-        dtype = values.dtype._from_categorical_dtype(values.dtype,
-                                                     categories, ordered)
-    else:
-        # If dtype=None and values is not categorical, create a new dtype.
-        # Note: This could potentially have categories=None and ordered=None.
-        dtype = CategoricalDtype(categories, ordered)
-
-    return dtype
-
-
 _codes_doc = """\
 The category codes of this categorical.
 
@@ -381,7 +316,8 @@ class Categorical(ExtensionArray, PandasObject):
     def __init__(self, values, categories=None, ordered=None, dtype=None,
                  fastpath=False):
 
-        dtype = create_categorical_dtype(values, categories, ordered, dtype)
+        dtype = CategoricalDtype._from_values_or_dtype(values, categories,
+                                                       ordered, dtype)
         # At this point, dtype is always a CategoricalDtype, but
         # we may have dtype.categories be None, and we need to
         # infer categories in a factorization step futher below
@@ -689,7 +625,8 @@ def from_codes(cls, codes, categories, ordered=False):
             categorical. If not given, the resulting categorical will be
             unordered.
         """
-        dtype = create_categorical_dtype(codes, categories, ordered)
+        dtype = CategoricalDtype._from_values_or_dtype(codes, categories,
+                                                       ordered)
 
         codes = np.asarray(codes)  # #21767
         if not is_integer_dtype(codes):

pandas/core/dtypes/dtypes.py

@@ -241,6 +241,77 @@ def _from_categorical_dtype(cls, dtype, categories=None, ordered=None):
             ordered = dtype.ordered
         return cls(categories, ordered)
 
+    @classmethod
+    def _from_values_or_dtype(cls, values=None, categories=None, ordered=None,
+                              dtype=None):
+        """
+        Construct from the inputs used in :class:`Categorical` construction.
+
+        This is an internal helper method, and specifically does not do the
+        factorization step, if that is needed. Additional steps may
+        therefore have to be taken to create the final dtype.
+
+        Parameters
+        ----------
+        values : list-like, optional
+            The list-like must be 1-dimensional.
+        categories : list-like, optional
+            Categories for the CategoricalDtype.
+        ordered : bool, optional
+            Designating if the categories are ordered.
+        dtype : CategoricalDtype or the string "category", optional
+            If ``CategoricalDtype`` cannot be used together with
+            `categories` or `ordered`.
+
+        Returns
+        -------
+        CategoricalDtype
+
+        Examples
+        --------
+        >>> CategoricalDtype._from_values_or_dtype()
+        CategoricalDtype(categories=None, ordered=None)
+        >>> CategoricalDtype._from_values_or_dtype(categories=['a', 'b'],
+        ...                                        ordered=True)
+        CategoricalDtype(categories=['a', 'b'], ordered=True)
+        >>> dtype1 = CategoricalDtype(['a', 'b'], ordered=True)
+        >>> dtype2 = CategoricalDtype(['x', 'y'], ordered=False)
+        >>> c = Categorical([0, 1], dtype=dtype1, fastpath=True)
+        >>> CategoricalDtype._from_values_or_dtype(c, ['x', 'y'], ordered=True,
+        ...                                        dtype=dtype2)
+        ValueError: Cannot specify `categories` or `ordered` together with
+        `dtype`.
+
+        The supplied dtype takes precedence over values's dtype:
+
+        >>> CategoricalDtype._from_values_or_dtype(c, dtype=dtype2)
+        CategoricalDtype(['x', 'y'], ordered=False)
+        """
+        from pandas.core.dtypes.common import is_categorical
+
+        if dtype is not None:
+            # The dtype argument takes precedence over values.dtype (if any)
+            if isinstance(dtype, compat.string_types):
+                if dtype == 'category':
+                    dtype = CategoricalDtype(categories, ordered)
+                else:
+                    msg = "Unknown dtype {dtype!r}"
+                    raise ValueError(msg.format(dtype=dtype))
+            elif categories is not None or ordered is not None:
+                raise ValueError("Cannot specify `categories` or `ordered` "
+                                 "together with `dtype`.")
+        elif is_categorical(values):
+            # If no "dtype" was passed, use the one from "values", but honor
+            # the "ordered" and "categories" arguments
+            dtype = values.dtype._from_categorical_dtype(values.dtype,
+                                                         categories, ordered)
+        else:
+            # If dtype=None and values is not categorical, create a new dtype.
+            # Note: This could potentially have categories=None and ordered=None.
+            dtype = CategoricalDtype(categories, ordered)
+
+        return dtype
+
     def _finalize(self, categories, ordered, fastpath=False):
 
         if ordered is not None:

pandas/core/indexes/category.py

@@ -17,8 +17,7 @@
 
 from pandas.core import accessor
 from pandas.core.algorithms import take_1d
-from pandas.core.arrays.categorical import (
-    Categorical, contains, create_categorical_dtype)
+from pandas.core.arrays.categorical import Categorical, contains
 import pandas.core.common as com
 from pandas.core.config import get_option
 import pandas.core.indexes.base as ibase
@@ -108,7 +107,8 @@ def __new__(cls, data=None, categories=None, ordered=None, dtype=None,
             if fastpath:
                 return cls._simple_new(data, name=name, dtype=dtype)
 
-        dtype = create_categorical_dtype(data, categories, ordered, dtype)
+        dtype = CategoricalDtype._from_values_or_dtype(data, categories,
+                                                       ordered, dtype)
 
         if name is None and hasattr(data, 'name'):
             name = data.name

pandas/tests/arrays/categorical/test_constructors.py

@@ -13,7 +13,6 @@
     Categorical, CategoricalIndex, DatetimeIndex, Index, Interval,
     IntervalIndex, NaT, Series, Timestamp, date_range, period_range,
     timedelta_range)
-from pandas.core.arrays.categorical import create_categorical_dtype
 import pandas.util.testing as tm
 
 
@@ -531,32 +530,3 @@ def test_constructor_imaginary(self):
         c1 = Categorical(values)
         tm.assert_index_equal(c1.categories, Index(values))
         tm.assert_numpy_array_equal(np.array(c1), np.array(values))
-
-
-class TestCreateCategoricalDtype(object):
-    dtype1 = CategoricalDtype(['a', 'b'], ordered=True)
-    dtype2 = CategoricalDtype(['x', 'y'], ordered=False)
-    c = Categorical([0, 1], dtype=dtype1, fastpath=True)
-
-    @pytest.mark.parametrize('values, categories, ordered, dtype, expected', [
-        [None, None, None, None, CategoricalDtype()],
-        [None, ['a', 'b'], True, None, dtype1],
-        [c, None, None, dtype2, dtype2],
-        [c, ['x', 'y'], False, None, dtype2],
-    ])
-    def test_create_categorical_dtype(
-            self, values, categories, ordered, dtype, expected):
-        result = create_categorical_dtype(values, categories, ordered, dtype)
-        assert result == expected
-
-    @pytest.mark.parametrize('values, categories, ordered, dtype', [
-        [None, ['a', 'b'], True, dtype2],
-        [None, ['a', 'b'], None, dtype2],
-        [None, None, True, dtype2],
-    ])
-    def test_create_categorical_dtype_raises(self, values, categories, ordered,
-                                             dtype):
-        msg = "Cannot specify `categories` or `ordered` together with `dtype`."
-
-        with pytest.raises(ValueError, match=msg):
-            create_categorical_dtype(values, categories, ordered, dtype)

pandas/tests/dtypes/test_dtypes.py

@@ -98,6 +98,38 @@ def test_constructor_invalid(self):
         with pytest.raises(TypeError, match=msg):
             CategoricalDtype("category")
 
+    dtype1 = CategoricalDtype(['a', 'b'], ordered=True)
+    dtype2 = CategoricalDtype(['x', 'y'], ordered=False)
+    c = Categorical([0, 1], dtype=dtype1, fastpath=True)
+
+    @pytest.mark.parametrize('values, categories, ordered, dtype, expected',
+                             [
+                                 [None, None, None, None,
+                                  CategoricalDtype()],
+                                 [None, ['a', 'b'], True, None, dtype1],
+                                 [c, None, None, dtype2, dtype2],
+                                 [c, ['x', 'y'], False, None, dtype2],
+                             ])
+    def test_create_categorical_dtype(
+            self, values, categories, ordered, dtype, expected):
+        result = CategoricalDtype._from_values_or_dtype(values, categories,
+                                                        ordered, dtype)
+        assert result == expected
+
+    @pytest.mark.parametrize('values, categories, ordered, dtype', [
+        [None, ['a', 'b'], True, dtype2],
+        [None, ['a', 'b'], None, dtype2],
+        [None, None, True, dtype2],
+    ])
+    def test_create_categorical_dtype_raises(self, values, categories,
+                                             ordered,
+                                             dtype):
+        msg = "Cannot specify `categories` or `ordered` together with `dtype`."
+
+        with pytest.raises(ValueError, match=msg):
+            CategoricalDtype._from_values_or_dtype(values, categories,
+                                                   ordered, dtype)
+
     def test_is_dtype(self):
         assert CategoricalDtype.is_dtype(self.dtype)
         assert CategoricalDtype.is_dtype('category')