Categorical type

doc/source/advanced.rst

@@ -638,9 +638,11 @@ and allows efficient indexing and storage of an index with a large number of dup
 
 .. ipython:: python
 
+   from pandas.api.types import CategoricalDtype
+
    df = pd.DataFrame({'A': np.arange(6),
                       'B': list('aabbca')})
-   df['B'] = df['B'].astype('category', categories=list('cab'))
+   df['B'] = df['B'].astype(CategoricalDtype(list('cab')))
    df
    df.dtypes
    df.B.cat.categories

doc/source/api.rst

@@ -646,7 +646,10 @@ strings and apply several methods to it. These can be accessed like
 Categorical
 ~~~~~~~~~~~
 
-If the Series is of dtype ``category``, ``Series.cat`` can be used to change the the categorical
+.. autoclass:: api.types.CategoricalDtype
+   :members: categories, ordered
+
+If the Series is of dtype ``CategoricalDtype``, ``Series.cat`` can be used to change the categorical
 data. This accessor is similar to the ``Series.dt`` or ``Series.str`` and has the
 following usable methods and properties:
 

doc/source/categorical.rst

@@ -89,12 +89,22 @@ By passing a :class:`pandas.Categorical` object to a `Series` or assigning it to
     df["B"] = raw_cat
     df
 
-You can also specify differently ordered categories or make the resulting data ordered, by passing these arguments to ``astype()``:
+Anywhere above we passed a keyword ``dtype='category'``, we used the default behavior of
+
+1. categories are inferred from the data
+2. categories are unordered.
+
+To control those behaviors, instead of passing ``'category'``, use an instance
+of :class:`~pandas.api.types.CategoricalDtype`.
 
 .. ipython:: python
 
-    s = pd.Series(["a","b","c","a"])
-    s_cat = s.astype("category", categories=["b","c","d"], ordered=False)
+    from pandas.api.types import CategoricalDtype
+
+    s = pd.Series(["a", "b", "c", "a"])
+    cat_type = CategoricalDtype(categories=["b", "c", "d"],
+                                ordered=True)
+    s_cat = s.astype(cat_type)
     s_cat
 
 Categorical data has a specific ``category`` :ref:`dtype <basics.dtypes>`:
@@ -133,6 +143,73 @@ constructor to save the factorize step during normal constructor mode:
     splitter = np.random.choice([0,1], 5, p=[0.5,0.5])
     s = pd.Series(pd.Categorical.from_codes(splitter, categories=["train", "test"]))
 
+.. _categorical.categoricaldtype:
+
+CategoricalDtype
+----------------
+
+.. versionchanged:: 0.21.0
+
+A categorical's type is fully described by
+
+1. ``categories``: a sequence of unique values and no missing values
+2. ``ordered``: a boolean
+
+This information can be stored in a :class:`~pandas.api.types.CategoricalDtype`.
+The ``categories`` argument is optional, which implies that the actual categories
+should be inferred from whatever is present in the data when the
+:class:`pandas.Categorical` is created. The categories are assumed to be unordered
+by default.      
+
+.. ipython:: python
+
+   from pandas.api.types import CategoricalDtype
+
+   CategoricalDtype(['a', 'b', 'c'])
+   CategoricalDtype(['a', 'b', 'c'], ordered=True)
+   CategoricalDtype()
+
+A :class:`~pandas.api.types.CategoricalDtype` can be used in any place pandas
+expects a `dtype`. For example :func:`pandas.read_csv`,
+:func:`pandas.DataFrame.astype`, or in the Series constructor.
+
+.. note::
+
+    As a convenience, you can use the string ``'category'`` in place of a
+    :class:`~pandas.api.types.CategoricalDtype` when you want the default behavior of
+    the categories being unordered, and equal to the set values present in the
+    array. In other words, ``dtype='category'`` is equivalent to
+    ``dtype=CategoricalDtype()``.
+
+Equality Semantics
+~~~~~~~~~~~~~~~~~~
+
+Two instances of :class:`~pandas.api.types.CategoricalDtype` compare equal
+whenever they have the same categories and orderedness. When comparing two
+unordered categoricals, the order of the ``categories`` is not considered
+
+.. ipython:: python
+
+   c1 = CategoricalDtype(['a', 'b', 'c'], ordered=False)
+
+   # Equal, since order is not considered when ordered=False
+   c1 == CategoricalDtype(['b', 'c', 'a'], ordered=False)
+
+   # Unequal, since the second CategoricalDtype is ordered
+   c1 == CategoricalDtype(['a',  'b', 'c'], ordered=True)
+
+All instances of ``CategoricalDtype`` compare equal to the string ``'category'``
+
+.. ipython:: python
+
+   c1 == 'category'
+
+.. warning::
+
+   Since ``dtype='category'`` is essentially ``CategoricalDtype(None, False)``,
+   and since all instances ``CategoricalDtype`` compare equal to ``'`category'``,
+   all instances of ``CategoricalDtype`` compare equal to a ``CategoricalDtype(None)``
+
 Description
 -----------
 
@@ -184,7 +261,7 @@ It's also possible to pass in the categories in a specific order:
 
     .. ipython:: python
 
-         s = pd.Series(list('babc')).astype('category', categories=list('abcd'))
+         s = pd.Series(list('babc')).astype(CategoricalDtype(list('abcd')))
          s
 
          # categories
@@ -297,7 +374,9 @@ meaning and certain operations are possible. If the categorical is unordered, ``
 
     s = pd.Series(pd.Categorical(["a","b","c","a"], ordered=False))
     s.sort_values(inplace=True)
-    s = pd.Series(["a","b","c","a"]).astype('category', ordered=True)
+    s = pd.Series(["a","b","c","a"]).astype(
+        CategoricalDtype(ordered=True)
+    )
     s.sort_values(inplace=True)
     s
     s.min(), s.max()
@@ -397,9 +476,15 @@ categories or a categorical with any list-like object, will raise a TypeError.
 
 .. ipython:: python
 
-    cat = pd.Series([1,2,3]).astype("category", categories=[3,2,1], ordered=True)
-    cat_base = pd.Series([2,2,2]).astype("category", categories=[3,2,1], ordered=True)
-    cat_base2 = pd.Series([2,2,2]).astype("category", ordered=True)
+    cat = pd.Series([1,2,3]).astype(
+        CategoricalDtype([3, 2, 1], ordered=True)
+    )
+    cat_base = pd.Series([2,2,2]).astype(
+        CategoricalDtype([3, 2, 1], ordered=True)
+    )
+    cat_base2 = pd.Series([2,2,2]).astype(
+        CategoricalDtype(ordered=True)
+    )
 
     cat
     cat_base

doc/source/merging.rst

@@ -830,8 +830,10 @@ The left frame.
 
 .. ipython:: python
 
+   from pandas.api.types import CategoricalDtype
+
    X = pd.Series(np.random.choice(['foo', 'bar'], size=(10,)))
-   X = X.astype('category', categories=['foo', 'bar'])
+   X = X.astype(CategoricalDtype(categories=['foo', 'bar']))
 
    left = pd.DataFrame({'X': X,
                         'Y': np.random.choice(['one', 'two', 'three'], size=(10,))})
@@ -842,8 +844,11 @@ The right frame.
 
 .. ipython:: python
 
-   right = pd.DataFrame({'X': pd.Series(['foo', 'bar']).astype('category', categories=['foo', 'bar']),
-                         'Z': [1, 2]})
+   right = pd.DataFrame({
+        'X': pd.Series(['foo', 'bar'],
+                       dtype=CategoricalDtype(['foo', 'bar'])),
+        'Z': [1, 2]
+   })
    right
    right.dtypes
 

doc/source/whatsnew/v0.21.0.txt

@@ -10,6 +10,8 @@ users upgrade to this version.
 Highlights include:
 
 - Integration with `Apache Parquet <https://parquet.apache.org/>`__, including a new top-level :func:`read_parquet` and :func:`DataFrame.to_parquet` method, see :ref:`here <io.parquet>`.
+- New user-facing :class:`pandas.api.types.CategoricalDtype` for specifying
+  categoricals independent of the data, see :ref:`here <whatsnew_0210.enhancements.categorical_dtype>`.
 
 Check the :ref:`API Changes <whatsnew_0210.api_breaking>` and :ref:`deprecations <whatsnew_0210.deprecations>` before updating.
 
@@ -89,6 +91,30 @@ This does not raise any obvious exceptions, but also does not create a new colum
 
 Setting a list-like data structure into a new attribute now raise a ``UserWarning`` about the potential for unexpected behavior. See :ref:`Attribute Access <indexing.attribute_access>`.
 
+.. _whatsnew_0210.enhancements.categorical_dtype:
+
+``CategoricalDtype`` for specifying categoricals
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:class:`pandas.api.types.CategoricalDtype` has been added to the public API and
+expanded to include the ``categories`` and ``ordered`` attributes. A
+``CategoricalDtype`` can be used to specify the set of categories and
+orderedness of an array, independent of the data themselves. This can be useful,
+e.g., when converting string data to a ``Categorical`` (:issue:`14711`, :issue:`15078`):
+
+.. ipython:: python
+
+   from pandas.api.types import CategoricalDtype
+
+   s = pd.Series(['a', 'b', 'c', 'a'])  # strings
+   dtype = CategoricalDtype(categories=['a', 'b', 'c', 'd'], ordered=True)
+   s.astype(dtype)
+
+The ``.dtype`` property of a ``Categorical``, ``CategoricalIndex`` or a
+``Series`` with categorical type will now return an instance of ``CategoricalDtype``.
+
+See the :ref:`CategoricalDtype docs <categorical.categoricaldtype>` for more.
+
 .. _whatsnew_0210.enhancements.other:
 
 Other Enhancements