Doc and test unexpected values

Author: TomAugspurger

doc/source/io.rst

@@ -482,14 +482,22 @@ that column's ``dtype``.
    dtype = CategoricalDtype(['d', 'c', 'b', 'a'], ordered=True)
    pd.read_csv(StringIO(data), dtype={'col1': dtype}).dtypes
 
+When using ``dtype=CategoricalDtype``, "unexpected" values outside of
+``dtype.categories`` are treated as missing values.
+
+   dtype = CategoricalDtype(['a', 'b', 'd'])  # No 'c'
+   pd.read_csv(StringIO(data), dtype={'col1': dtype}).col1
+
+This matches the behavior of :meth:`Categorical.set_categories`.
+
 .. note::
 
    With ``dtype='category'``, the resulting categories will always be parsed
    as strings (object dtype). If the categories are numeric they can be
    converted using the :func:`to_numeric` function, or as appropriate, another
    converter such as :func:`to_datetime`.
 
-   When ``dtype`` is a ``CategoricalDtype`` with homogenous ``categoriess`` (
+   When ``dtype`` is a ``CategoricalDtype`` with homogenous ``categories`` (
    all numeric, all datetimes, etc.), the conversion is done automatically.
 
    .. ipython:: python

doc/source/whatsnew/v0.21.0.txt

@@ -119,7 +119,7 @@ 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`, :issue:`16015`):
+:issue:`15078`, :issue:`16015`, :issue:`17643`):
 
 .. ipython:: python
 
@@ -129,8 +129,33 @@ e.g., when converting string data to a ``Categorical`` (:issue:`14711`,
    dtype = CategoricalDtype(categories=['a', 'b', 'c', 'd'], ordered=True)
    s.astype(dtype)
 
+One place that deserves special mention is in :meth:`read_csv`. Previously, with
+``dtype={'col': 'category'}``, the returned values and categories would always
+be strings.
+
+.. ipython:: python
+
+   from pandas.compat import StringIO
+
+   data = 'A,B\na,1\nb,2\nc,3'
+   pd.read_csv(StringIO(data), dtype={'B': 'category'}).B.cat.categories
+
+Notice the "object" dtype.
+
+With a ``CategoricalDtype`` of all numerics, datetimes, or
+timedeltas, we can automatically convert to the correct type
+
+    dtype = {'B': CategoricalDtype([1, 2, 3])}
+    pd.read_csv(StringIO(data), dtype=dtype).B.cat.categories
+
+The values have been correctly interpreted as integers.
+
 The ``.dtype`` property of a ``Categorical``, ``CategoricalIndex`` or a
 ``Series`` with categorical type will now return an instance of ``CategoricalDtype``.
+For the most part, this is backwards compatible, though the string repr has changed.
+If you were previously using ``str(s.dtype == 'category')`` to detect categorical data,
+switch to :func:`api.types.is_categorical_dtype`, which is compatible with the old and
+new ``CategoricalDtype``.
 
 See the :ref:`CategoricalDtype docs <categorical.categoricaldtype>` for more.
 
@@ -163,8 +188,6 @@ Other Enhancements
 - :func:`Categorical.rename_categories` now accepts a dict-like argument as `new_categories` and only updates the categories found in that dict. (:issue:`17336`)
 - :func:`read_excel` raises ``ImportError`` with a better message if ``xlrd`` is not installed. (:issue:`17613`)
 - :meth:`DataFrame.assign` will preserve the original order of ``**kwargs`` for Python 3.6+ users instead of sorting the column names
-- Pass a :class:`~pandas.api.types.CategoricalDtype` to :meth:`read_csv` to parse categorical
-  data as numeric, datetimes, or timedeltas, instead of strings. See :ref:`here <io.categorical>`. (:issue:`17643`)
 
 
 .. _whatsnew_0210.api_breaking:

pandas/tests/io/parser/dtypes.py

@@ -210,6 +210,14 @@ def test_categoricaldtype_coerces_timedelta(self):
         result = self.read_csv(StringIO(data), dtype=dtype)
         tm.assert_frame_equal(result, expected)
 
+    def test_categoricaldtype_unexpected_categories(self):
+        dtype = {'b': CategoricalDtype(['a', 'b', 'd', 'e'])}
+        data = "b\nd\na\nc\nd"  # Unexpected c
+        expected = pd.DataFrame({"b": Categorical(list('dacd'),
+                                                  dtype=dtype['b'])})
+        result = self.read_csv(StringIO(data), dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
     def test_categorical_categoricaldtype_chunksize(self):
         # GH 10153
         data = """a,b