DOC: Update pandas.Series.copy docstring
Closes https://github.com/pandas-dev/pandas/issues/19505

- Add extended summary
- Add examples

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

- [X] PR title is "DOC: update the <your-function-or-method> docstring"
- [X] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`
- [X] The PEP8 style check passes: `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] The html version looks good: `python doc/make.py --single <your-function-or-method>`
- [X] It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

```

################################################################################
######################## Docstring (pandas.Series.copy) ########################
################################################################################

Make a copy of this objects indices and data.

Deep copy (default) will create a new object with a copy of the objects
data and indices. Modifications to the data or indices of the copy
will not be reflected in the original object.

Shallow copy (``deep=False``) will create a new object without copying
the data or indices. Any changes to the index or data of the original
will be reflected in the shallow copy (and vice versa).

Parameters
----------
deep : boolean or string, 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.

Returns
-------
copy : type of caller

Examples
--------
>>> s = pd.Series([1,2], dtype="int32", index=["a", "b"])
>>> s
a    1
b    2
dtype: int32
>>> s_copy = s.copy()
>>> s_copy
a    1
b    2
dtype: int32

Shallow copy versus default (deep) copy:

In a shallow copy the index and data is a reference to the original
objects'.

>>> s = pd.Series([1,2], dtype="int32", index=["a", "b"])
>>> deep_copy = s.copy()
>>> shallow_copy = s.copy(deep=False)
>>> id(s) == id(shallow_copy)
False
>>> id(s.index) == id(shallow_copy.index)
True
>>> id(s.values) == id(shallow_copy.values)
True
>>> id(s) == id(deep_copy)
False
>>> id(s.index) == id(deep_copy.index)
False
>>> id(s.values) == id(deep_copy.values)
False
>>> s[0] = 3
>>> s
a    3
b    2
dtype: int32
>>> shallow_copy
a    3
b    2
dtype: int32
>>> deep_copy
a    1
b    2
dtype: int32
>>> shallow_copy[0] = 4
>>> s
a    4
b    2
dtype: int32
>>> shallow_copy
a    4
b    2
dtype: int32
>>> deep_copy
a    1
b    2
dtype: int32

When copying object containing python objects, deep copy will
copy the indices and data but will not do so recursively.

>>> class Point(object):
...     def __init__(self, x, y):
...         self.x = x
...         self.y = y
...     def __repr__(self):
...         return "Point(%d,%d)" % (self.x, self.y)
...
>>> p1 = Point(0, 1)
>>> s = pd.Series([p1], dtype="object")
>>> s
0    Point(0,1)
dtype: object
>>> s_copy = s.copy()
>>> s_copy
0    Point(0,1)
dtype: object
>>> id(s[0]) == id(s_copy[0])
True
>>> p1.x = 1
>>> s
0    Point(1,1)
dtype: object
>>> s_copy
0    Point(1,1)
dtype: object
>>>

For deep-copying python objects, the following can be used:

>>> import copy
>>> deep_deep_copy = pd.Series(copy.deepcopy(s.values),
...                            index=copy.deepcopy(s.index))

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
        See Also section not found

```

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.

Not sure if anything should be added to the See Also section