add test to test_nanops.py + doc cleanup

Author: tp

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

@@ -304,38 +304,6 @@ largely as a convenience since it is such a common operation.
 Selection By Label
 ------------------
 
-.. warning::
-
-   Whether a copy or a reference is returned for a setting operation, may depend on the context.
-   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
-   this will raise a ``KeyError``. See :ref:`list-like Using loc with missing keys in a list is Deprecated <indexing.deprecate_loc_reindex_listlike>`.
-
 pandas provides a suite of methods in order to have **purely label based indexing**. This is a strict inclusion based protocol.
 Every label asked for must be in the index, or a ``KeyError`` will be raised.
 When slicing, both the start bound **AND** the stop bound are *included*, if present in the index.
@@ -352,6 +320,12 @@ The ``.loc`` attribute is the primary access method. The following are valid inp
 - A boolean array.
 - A ``callable``, see :ref:`Selection By Callable <indexing.callable>`.
 
+.. warning::
+
+   Whether a copy or a reference is returned for a setting operation, may depend on the context.
+   This is sometimes called ``chained assignment`` and should be avoided.
+   See :ref:`Returning a View versus Copy <indexing.view_versus_copy>`.
+
 .. ipython:: python
 
    s1 = pd.Series(np.random.randn(6),index=list('abcdef'))
@@ -402,6 +376,33 @@ For getting a value explicitly (equivalent to deprecated ``df.get_value('a','A')
    # this is also equivalent to ``df1.at['a','A']``
    df1.loc['a', 'A']
 
+.. 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
+   this will raise a ``KeyError``. See :ref:`list-like Using loc with missing keys in a list is Deprecated <indexing.deprecate_loc_reindex_listlike>`.
+
 .. _indexing.slicing_with_labels:
 
 Slicing with labels

pandas/tests/test_nanops.py

@@ -13,7 +13,7 @@
 import pandas.core.nanops as nanops
 import pandas.util.testing as tm
 import pandas.util._test_decorators as td
-from pandas.compat.numpy import _np_version_under1p13
+from pandas.compat.numpy import _np_version_under1p13, _np_version_under1p10
 
 use_bn = nanops._USE_BOTTLENECK
 
@@ -1023,6 +1023,10 @@ def test_use_bottleneck():
     (np.nansum, 10),
     (np.mean, 2.5),
     (np.nanmean, 2.5),
+    (np.prod, 24),
+    pytest.param(getattr(np, 'nanprod'), 24,
+                 marks=pytest.mark.skipif(_np_version_under1p10,
+                                          reason="numpy >=1.10 required")),
     (np.median, 2.5),
     (np.nanmedian, 2.5),
     (np.min, 1),