DOC: make doc build run with string dtype enabled (#61864)

Author: jorisvandenbossche

.github/workflows/docbuild-and-upload.yml

@@ -57,8 +57,6 @@ jobs:
       run: python web/pandas_web.py web/pandas --target-path=web/build
 
     - name: Build documentation
-      # TEMP don't let errors fail the build until all string dtype changes are fixed
-      continue-on-error: true
       run: doc/make.py --warnings-are-errors
 
     - name: Build the interactive terminal

doc/source/user_guide/basics.rst

@@ -590,7 +590,7 @@ arguments. The special value ``all`` can also be used:
 
 .. ipython:: python
 
-    frame.describe(include=["object"])
+    frame.describe(include=["str"])
     frame.describe(include=["number"])
     frame.describe(include="all")
 

doc/source/whatsnew/v0.13.0.rst

@@ -184,7 +184,7 @@ API changes
   .. ipython:: python
      :okwarning:
 
-     dfc.loc[0]['A'] = 1111
+     dfc.loc[0]['B'] = 1111
 
   ::
 
@@ -198,7 +198,7 @@ API changes
 
   .. ipython:: python
 
-     dfc.loc[0, 'A'] = 11
+     dfc.loc[0, 'B'] = 1111
      dfc
 
 - ``Panel.reindex`` has the following call signature ``Panel.reindex(items=None, major_axis=None, minor_axis=None, **kwargs)``

doc/source/whatsnew/v0.15.0.rst

@@ -1025,20 +1025,49 @@ Other:
 - :func:`describe` on mixed-types DataFrames is more flexible. Type-based column filtering is now possible via the ``include``/``exclude`` arguments.
   See the :ref:`docs <basics.describe>` (:issue:`8164`).
 
-  .. ipython:: python
+  .. code-block:: python
 
-    df = pd.DataFrame({'catA': ['foo', 'foo', 'bar'] * 8,
-                       'catB': ['a', 'b', 'c', 'd'] * 6,
-                       'numC': np.arange(24),
-                       'numD': np.arange(24.) + .5})
-    df.describe(include=["object"])
-    df.describe(include=["number", "object"], exclude=["float"])
+    >>> df = pd.DataFrame({'catA': ['foo', 'foo', 'bar'] * 8,
+    ...                    'catB': ['a', 'b', 'c', 'd'] * 6,
+    ...                    'numC': np.arange(24),
+    ...                    'numD': np.arange(24.) + .5})
+    >>> df.describe(include=["object"])
+           catA catB
+    count    24   24
+    unique    2    4
+    top     foo    a
+    freq     16    6
+    >>> df.describe(include=["number", "object"], exclude=["float"])
+           catA catB       numC
+    count    24   24  24.000000
+    unique    2    4        NaN
+    top     foo    a        NaN
+    freq     16    6        NaN
+    mean    NaN  NaN  11.500000
+    std     NaN  NaN   7.071068
+    min     NaN  NaN   0.000000
+    25%     NaN  NaN   5.750000
+    50%     NaN  NaN  11.500000
+    75%     NaN  NaN  17.250000
+    max     NaN  NaN  23.000000
 
   Requesting all columns is possible with the shorthand 'all'
 
-  .. ipython:: python
+  .. code-block:: python
 
-    df.describe(include='all')
+    >>> df.describe(include='all')
+           catA catB       numC       numD
+    count    24   24  24.000000  24.000000
+    unique    2    4        NaN        NaN
+    top     foo    a        NaN        NaN
+    freq     16    6        NaN        NaN
+    mean    NaN  NaN  11.500000  12.000000
+    std     NaN  NaN   7.071068   7.071068
+    min     NaN  NaN   0.000000   0.500000
+    25%     NaN  NaN   5.750000   6.250000
+    50%     NaN  NaN  11.500000  12.000000
+    75%     NaN  NaN  17.250000  17.750000
+    max     NaN  NaN  23.000000  23.500000
 
   Without those arguments, ``describe`` will behave as before, including only numerical columns or, if none are, only categorical columns. See also the :ref:`docs <basics.describe>`