DOC: Some doc cleanup

README.md

@@ -106,9 +106,8 @@ Here are just a few of the things that pandas does well:
   - Make it [**easy to convert**][conversion] ragged,
     differently-indexed data in other Python and NumPy data structures
     into DataFrame objects
-  - Intelligent label-based [**slicing**][slicing], [**fancy
-    indexing**][fancy-indexing], and [**subsetting**][subsetting] of
-    large data sets
+  - Intelligent label-based [**slicing**][slicing], [**selecting**][selecting],
+    and [**subsetting**][subsetting] of large data sets
   - Intuitive [**merging**][merging] and [**joining**][joining] data
     sets
   - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
@@ -129,7 +128,7 @@ Here are just a few of the things that pandas does well:
    [groupby]: https://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine
    [conversion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe
    [slicing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges
-   [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix
+   [selecting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#selection-by-label
    [subsetting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing
    [merging]: https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging
    [joining]: https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index

doc/source/indexing.rst

@@ -310,27 +310,6 @@ Selection By Label
    This is sometimes called ``chained assignment`` and should be avoided.
    See :ref:`Returning a View versus Copy <indexing.view_versus_copy>`.
 
-.. warning::
-
-   ``.loc`` is strict when you present slicers that are not compatible (or convertible) with the index type. For example
-   using integers in a ``DatetimeIndex``. These will raise a ``TypeError``.
-
-  .. ipython:: python
-
-     dfl = pd.DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=pd.date_range('20130101',periods=5))
-     dfl
-
-  .. code-block:: ipython
-
-     In [4]: dfl.loc[2:3]
-     TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'>
-
-  String likes in slicing *can* be convertible to the type of the index and lead to natural slicing.
-
-  .. ipython:: python
-
-     dfl.loc['20130102':'20130104']
-
 .. warning::
 
    Starting in 0.21.0, pandas will show a ``FutureWarning`` if indexing with a list with missing labels. In the future
@@ -430,6 +409,29 @@ error will be raised (since doing otherwise would be computationally expensive,
 as well as potentially ambiguous for mixed type indexes). For instance, in the
 above example, ``s.loc[1:6]`` would raise ``KeyError``.
 
+.. warning::
+
+   ``.loc`` is strict when you present slicers that are not compatible
+   (or convertible) with the index type. For example using integers in a
+   ``DatetimeIndex``. These will raise a ``TypeError``.
+
+  .. ipython:: python
+
+     dfl = pd.DataFrame(np.random.randn(5,4), columns=list('ABCD'),
+                        index=pd.date_range('20130101',periods=5))
+     dfl
+
+  .. code-block:: ipython
+
+     In [4]: dfl.loc[2:3]
+     TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'>
+
+  String likes in slicing *can* be convertible to the type of the index and lead to natural slicing.
+
+  .. ipython:: python
+
+     dfl.loc['20130102':'20130104']
+
 .. _indexing.integer:
 
 Selection By Position