diff --git a/pandas/core/base.py b/pandas/core/base.py
index 6625a3bbe97d7..4ea8c43b640fb 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -1008,7 +1008,7 @@ def map_f(values, f):
     def value_counts(self, normalize=False, sort=True, ascending=False,
                      bins=None, dropna=True):
         """
-        Returns object containing counts of unique values.
+        Return a Series containing counts of unique values.
 
         The resulting object will be in descending order so that the
         first element is the most frequently-occurring element.
@@ -1020,18 +1020,69 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
             If True then the object returned will contain the relative
             frequencies of the unique values.
         sort : boolean, default True
-            Sort by values
+            Sort by values.
         ascending : boolean, default False
-            Sort in ascending order
+            Sort in ascending order.
         bins : integer, optional
             Rather than count values, group them into half-open bins,
-            a convenience for pd.cut, only works with numeric data
+            a convenience for ``pd.cut``, only works with numeric data.
         dropna : boolean, default True
             Don't include counts of NaN.
 
         Returns
         -------
         counts : Series
+
+        See Also
+        --------
+        Series.count: number of non-NA elements in a Series
+        DataFrame.count: number of non-NA elements in a DataFrame
+
+        Examples
+        --------
+        >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
+        >>> index.value_counts()
+        3.0    2
+        4.0    1
+        2.0    1
+        1.0    1
+        dtype: int64
+
+        With `normalize` set to `True`, returns the relative frequency by
+        dividing all values by the sum of values.
+
+        >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
+        >>> s.value_counts(normalize=True)
+        3.0    0.4
+        4.0    0.2
+        2.0    0.2
+        1.0    0.2
+        dtype: float64
+
+        **bins**
+
+        Bins can be useful for going from a continuous variable to a
+        categorical variable; instead of counting unique
+        apparitions of values, divide the index in the specified
+        number of half-open bins.
+
+        >>> s.value_counts(bins=3)
+        (2.0, 3.0]      2
+        (0.996, 2.0]    2
+        (3.0, 4.0]      1
+        dtype: int64
+
+        **dropna**
+
+        With `dropna` set to `False` we can also see NaN index values.
+
+        >>> s.value_counts(dropna=False)
+        3.0    2
+        NaN    1
+        4.0    1
+        2.0    1
+        1.0    1
+        dtype: int64
         """
         from pandas.core.algorithms import value_counts
         result = value_counts(self, sort=sort, ascending=ascending,