DOC: fix various warnings and errors in the docs (from deprecations/api changes) (#19763)

Author: jorisvandenbossche

doc/source/advanced.rst

@@ -274,7 +274,7 @@ Passing a list of labels or tuples works similar to reindexing:
 
    df.loc[[('bar', 'two'), ('qux', 'one')]]
 
-.. info::
+.. note::
 
    It is important to note that tuples and lists are not treated identically
    in pandas when it comes to indexing. Whereas a tuple is interpreted as one

doc/source/dsintro.rst

@@ -506,7 +506,7 @@ to be inserted (for example, a ``Series`` or NumPy array), or a function
 of one argument to be called on the ``DataFrame``. A *copy* of the original
 DataFrame is returned, with the new values inserted.
 
-.. versionmodified:: 0.23.0
+.. versionchanged:: 0.23.0
 
 Starting with Python 3.6 the order of ``**kwargs`` is preserved. This allows
 for *dependent* assignment, where an expression later in ``**kwargs`` can refer

doc/source/io.rst

@@ -2262,6 +2262,7 @@ is not round-trippable, nor are any names beginning with 'level_' within a
 indicate missing values and the subsequent read cannot distinguish the intent.
 
 .. ipython:: python
+   :okwarning:
 
    df.index.name = 'index'
    df.to_json('test.json', orient='table')

doc/source/whatsnew/v0.10.0.txt

@@ -411,15 +411,23 @@ N Dimensional Panels (Experimental)
 Adding experimental support for Panel4D and factory functions to create n-dimensional named panels.
 :ref:`Docs <dsintro.panel4d>` for NDim. Here is a taste of what to expect.
 
-     .. ipython:: python
-        :okwarning:
-
-        p4d = Panel4D(randn(2, 2, 5, 4),
-              labels=['Label1','Label2'],
-              items=['Item1', 'Item2'],
-              major_axis=date_range('1/1/2000', periods=5),
-              minor_axis=['A', 'B', 'C', 'D'])
-        p4d
+.. code-block:: ipython
+
+  In [58]: p4d = Panel4D(randn(2, 2, 5, 4),
+    ....:       labels=['Label1','Label2'],
+    ....:       items=['Item1', 'Item2'],
+    ....:       major_axis=date_range('1/1/2000', periods=5),
+    ....:       minor_axis=['A', 'B', 'C', 'D'])
+    ....:
+
+  In [59]: p4d
+  Out[59]:
+  <class 'pandas.core.panelnd.Panel4D'>
+  Dimensions: 2 (labels) x 2 (items) x 5 (major_axis) x 4 (minor_axis)
+  Labels axis: Label1 to Label2
+  Items axis: Item1 to Item2
+  Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
+  Minor_axis axis: A to D
 
 
 

doc/source/whatsnew/v0.13.1.txt

@@ -140,25 +140,44 @@ API changes
   applied would be called with an empty ``Series`` to guess whether a
   ``Series`` or ``DataFrame`` should be returned:
 
-  .. ipython:: python
+  .. code-block:: ipython
+
+    In [32]: def applied_func(col):
+      ....:    print("Apply function being called with: ", col)
+      ....:    return col.sum()
+      ....:
 
-     def applied_func(col):
-        print("Apply function being called with: ", col)
-        return col.sum()
+    In [33]: empty = DataFrame(columns=['a', 'b'])
 
-     empty = DataFrame(columns=['a', 'b'])
-     empty.apply(applied_func)
+    In [34]: empty.apply(applied_func)
+    Apply function being called with:  Series([], Length: 0, dtype: float64)
+    Out[34]:
+    a   NaN
+    b   NaN
+    Length: 2, dtype: float64
 
   Now, when ``apply`` is called on an empty ``DataFrame``: if the ``reduce``
   argument is ``True`` a ``Series`` will returned, if it is ``False`` a
   ``DataFrame`` will be returned, and if it is ``None`` (the default) the
   function being applied will be called with an empty series to try and guess
   the return type.
 
-  .. ipython:: python
+  .. code-block:: ipython
+
+    In [35]: empty.apply(applied_func, reduce=True)
+    Out[35]:
+    a   NaN
+    b   NaN
+    Length: 2, dtype: float64
+
+    In [36]: empty.apply(applied_func, reduce=False)
+    Out[36]:
+    Empty DataFrame
+    Columns: [a, b]
+    Index: []
+
+    [0 rows x 2 columns]
 
-     empty.apply(applied_func, reduce=True)
-     empty.apply(applied_func, reduce=False)
 
 Prior Version Deprecations/Changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

doc/source/whatsnew/v0.15.0.txt

@@ -1044,7 +1044,7 @@ Other:
 
       idx = MultiIndex.from_product([['a'], range(3), list("pqr")], names=['foo', 'bar', 'baz'])
       idx.set_names('qux', level=0)
-      idx.set_names(['qux','baz'], level=[0,1])
+      idx.set_names(['qux','corge'], level=[0,1])
       idx.set_levels(['a','b','c'], level='bar')
       idx.set_levels([['a','b','c'],[1,2,3]], level=[1,2])
 

doc/source/whatsnew/v0.21.0.txt

@@ -894,17 +894,14 @@ imported. Matplotlib plot methods (``plt.plot``, ``ax.plot``, ...), will not
 nicely format the x-axis for ``DatetimeIndex`` or ``PeriodIndex`` values. You
 must explicitly register these methods:
 
-.. ipython:: python
-
-   from pandas.tseries import converter
-   converter.register()
-
-   fig, ax = plt.subplots()
-   plt.plot(pd.date_range('2017', periods=6), range(6))
-
 Pandas built-in ``Series.plot`` and ``DataFrame.plot`` *will* register these
 converters on first-use (:issue:17710).
 
+.. note::
+
+  This change has been temporarily reverted in pandas 0.21.1,
+  for more details see :ref:`here <whatsnew_0211.converters>`.
+
 .. _whatsnew_0210.api:
 
 Other API Changes

doc/source/whatsnew/v0.23.0.txt

@@ -211,6 +211,7 @@ A ``DataFrame`` can now be written to and subsequently read back via JSON while
 Please note that the string `index` is not supported with the round trip format, as it is used by default in ``write_json`` to indicate a missing index name.
 
 .. ipython:: python
+   :okwarning:
 
    df.index.name = 'index'
    df.to_json('test.json', orient='table')

doc/source/whatsnew/v0.8.0.txt

@@ -217,12 +217,12 @@ nanosecond support (the ``nanosecond`` field store the nanosecond value between
 ``DatetimeIndex`` to regular NumPy arrays.
 
 If you have code that requires an array of ``datetime.datetime`` objects, you
-have a couple of options. First, the ``asobject`` property of ``DatetimeIndex``
+have a couple of options. First, the ``astype(object)`` method of ``DatetimeIndex``
 produces an array of ``Timestamp`` objects:
 
 .. ipython:: python
 
-   stamp_array = rng.asobject
+   stamp_array = rng.astype(object)
    stamp_array
    stamp_array[5]
 

pandas/core/frame.py

@@ -114,7 +114,7 @@
             - if `axis` is 1 or `'columns'` then `by` may contain column
               levels and/or index labels
 
-        .. versionmodified:: 0.23.0
+        .. versionchanged:: 0.23.0
            Allow specifying index or column level names.""",
     versionadded_to_excel='',
     optional_labels="""labels : array-like, optional
@@ -2696,7 +2696,7 @@ def assign(self, **kwargs):
         or modified columns. All items are computed first, and then assigned
         in alphabetical order.
 
-        .. versionmodified :: 0.23.0
+        .. versionchanged :: 0.23.0
 
             Keyword argument order is maintained for Python 3.6 and later.