Added selection example to cookbook

I added a (for me) very helpful Q&A from Stackoverflow on groupby/minimization while retaining the original DataFrame columns. Additionally, I cleaned up some links in the same cookbok section.

(Disclaimer: this is my first pull request.)

Author: ojdo

doc/source/cookbook.rst

@@ -60,28 +60,27 @@ Selection
 
 The :ref:`indexing <indexing>` docs.
 
-Indexing using both row labels and conditionals, see
-`here
+`Indexing using both row labels and conditionals
 <http://stackoverflow.com/questions/14725068/pandas-using-row-labels-in-boolean-indexing>`__
 
-Use loc for label-oriented slicing and iloc positional slicing, see
-`here <https://github.com/pydata/pandas/issues/2904>`__
+`Use loc for label-oriented slicing and iloc positional slicing 
+<https://github.com/pydata/pandas/issues/2904>`__
 
-Extend a panel frame by transposing, adding a new dimension, and transposing back to the original dimensions, see
-`here
+`Extend a panel frame by transposing, adding a new dimension, and transposing back to the original dimensions 
 <http://stackoverflow.com/questions/15364050/extending-a-pandas-panel-frame-along-the-minor-axis>`__
 
-Mask a panel by using ``np.where`` and then reconstructing the panel with the new masked values
-`here
+`Mask a panel by using ``np.where`` and then reconstructing the panel with the new masked values
 <http://stackoverflow.com/questions/14650341/boolean-mask-in-pandas-panel>`__
 
-Using ``~`` to take the complement of a boolean array, see
-`here
+`Using ``~`` to take the complement of a boolean array, see
 <http://stackoverflow.com/questions/14986510/picking-out-elements-based-on-complement-of-indices-in-python-pandas>`__
 
 `Efficiently creating columns using applymap
 <http://stackoverflow.com/questions/16575868/efficiently-creating-additional-columns-in-a-pandas-dataframe-using-map>`__
 
+`Keep other columns when using min() with groupby
+<http://stackoverflow.com/questions/23394476/keep-other-columns-when-using-min-with-groupby>`__
+
 .. _cookbook.multi_index:
 
 MultiIndexing