diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index d5bdeb7fe1a4d..a65c2cd8df350 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -4506,22 +4506,108 @@ def astype(self, dtype, copy=True, errors='raise', **kwargs):
 
     def copy(self, deep=True):
         """
-        Make a copy of this objects data.
+        Make a copy of this object's indices and data.
+
+        When ``deep=True`` (default), a new object will be created with a
+        copy of the calling object's data and indices. Modifications to
+        the data or indices of the copy will not be reflected in the
+        original object (see notes below).
+
+        When ``deep=False``, a new object will be created without copying
+        the calling object's data or index (only references to the data
+        and index are copied). Any changes to the data of the original
+        will be reflected in the shallow copy (and vice versa).
 
         Parameters
         ----------
-        deep : boolean or string, default True
+        deep : bool, default True
             Make a deep copy, including a copy of the data and the indices.
-            With ``deep=False`` neither the indices or the data are copied.
-
-            Note that when ``deep=True`` data is copied, actual python objects
-            will not be copied recursively, only the reference to the object.
-            This is in contrast to ``copy.deepcopy`` in the Standard Library,
-            which recursively copies object data.
+            With ``deep=False`` neither the indices nor the data are copied.
 
         Returns
         -------
-        copy : type of caller
+        copy : Series, DataFrame or Panel
+            Object type matches caller.
+
+        Notes
+        -----
+        When ``deep=True``, data is copied but actual Python objects
+        will not be copied recursively, only the reference to the object.
+        This is in contrast to `copy.deepcopy` in the Standard Library,
+        which recursively copies object data (see examples below).
+
+        While ``Index`` objects are copied when ``deep=True``, the underlying
+        numpy array is not copied for performance reasons. Since ``Index`` is
+        immutable, the underlying data can be safely shared and a copy
+        is not needed.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2], index=["a", "b"])
+        >>> s
+        a    1
+        b    2
+        dtype: int64
+
+        >>> s_copy = s.copy()
+        >>> s_copy
+        a    1
+        b    2
+        dtype: int64
+
+        **Shallow copy versus default (deep) copy:**
+
+        >>> s = pd.Series([1, 2], index=["a", "b"])
+        >>> deep = s.copy()
+        >>> shallow = s.copy(deep=False)
+
+        Shallow copy shares data and index with original.
+
+        >>> s is shallow
+        False
+        >>> s.values is shallow.values and s.index is shallow.index
+        True
+
+        Deep copy has own copy of data and index.
+
+        >>> s is deep
+        False
+        >>> s.values is deep.values or s.index is deep.index
+        False
+
+        Updates to the data shared by shallow copy and original is reflected
+        in both; deep copy remains unchanged.
+
+        >>> s[0] = 3
+        >>> shallow[1] = 4
+        >>> s
+        a    3
+        b    4
+        dtype: int64
+        >>> shallow
+        a    3
+        b    4
+        dtype: int64
+        >>> deep
+        a    1
+        b    2
+        dtype: int64
+
+        Note that when copying an object containing Python objects, a deep copy
+        will copy the data, but will not do so recursively. Updating a nested
+        data object will be reflected in the deep copy.
+
+        >>> s = pd.Series([[1, 2], [3, 4]])
+        >>> deep = s.copy()
+        >>> s[0][0] = 10
+        >>> s
+        0    [10, 2]
+        1     [3, 4]
+        dtype: object
+        >>> deep
+        0    [10, 2]
+        1     [3, 4]
+        dtype: object
         """
         data = self._data.copy(deep=deep)
         return self._constructor(data).__finalize__(self)