From 4b2119a11a9a3b020fe83709e56aad1d38175eff Mon Sep 17 00:00:00 2001
From: Tulio Casagrande <tuliocasagrande@gmail.com>
Date: Thu, 11 Jan 2018 15:56:49 -0200
Subject: [PATCH] DOC: Improve DataFrame.select_dtypes examples

---
 pandas/core/frame.py | 39 ++++++++++++++++++++-------------------
 1 file changed, 20 insertions(+), 19 deletions(-)

diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index a8c4053850548..43df2c48fcf58 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2417,17 +2417,18 @@ def select_dtypes(self, include=None, exclude=None):
 
         Notes
         -----
-        * To select all *numeric* types use the numpy dtype ``numpy.number``
+        * To select all *numeric* types, use ``np.number`` or ``'number'``
         * To select strings you must use the ``object`` dtype, but note that
           this will return *all* object dtype columns
         * See the `numpy dtype hierarchy
           <http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__
-        * To select datetimes, use np.datetime64, 'datetime' or 'datetime64'
-        * To select timedeltas, use np.timedelta64, 'timedelta' or
-          'timedelta64'
-        * To select Pandas categorical dtypes, use 'category'
-        * To select Pandas datetimetz dtypes, use 'datetimetz' (new in 0.20.0),
-          or a 'datetime64[ns, tz]' string
+        * To select datetimes, use ``np.datetime64``, ``'datetime'`` or
+          ``'datetime64'``
+        * To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
+          ``'timedelta64'``
+        * To select Pandas categorical dtypes, use ``'category'``
+        * To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
+          0.20.0) or ``'datetime64[ns, tz]'``
 
         Examples
         --------
@@ -2436,12 +2437,12 @@ def select_dtypes(self, include=None, exclude=None):
         ...                    'c': [1.0, 2.0] * 3})
         >>> df
                 a      b  c
-        0  0.3962   True  1
-        1  0.1459  False  2
-        2  0.2623   True  1
-        3  0.0764  False  2
-        4 -0.9703   True  1
-        5 -1.2094  False  2
+        0  0.3962   True  1.0
+        1  0.1459  False  2.0
+        2  0.2623   True  1.0
+        3  0.0764  False  2.0
+        4 -0.9703   True  1.0
+        5 -1.2094  False  2.0
         >>> df.select_dtypes(include='bool')
            c
         0  True
@@ -2452,12 +2453,12 @@ def select_dtypes(self, include=None, exclude=None):
         5  False
         >>> df.select_dtypes(include=['float64'])
            c
-        0  1
-        1  2
-        2  1
-        3  2
-        4  1
-        5  2
+        0  1.0
+        1  2.0
+        2  1.0
+        3  2.0
+        4  1.0
+        5  2.0
         >>> df.select_dtypes(exclude=['floating'])
                b
         0   True