DOC: update the Series.view docstring
State: closed

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```
################################################################################
######################## Docstring (pandas.Series.view) ########################
################################################################################

Create a new view of the Series.

This function will return a new Series with a view of the same
underlying values in memory, optionally reinterpreted with a new data
type. The new data type must preserve the same size in bytes as to not
cause index misalignment.

Parameters
----------
dtype : data type
    Data type object or one of their string representations.

Returns
-------
Series
    A new Series object as a view of the same data in memory.

See Also
--------
numpy.ndarray.view : Equivalent numpy function to create a new view of
    the same data in memory.

Notes
-----
Series are instantiated with `dtype=float64` by default. While
`numpy.ndarray.view()` will return a view with the same data type as
the original array, Series.view() will try using `float64` and may fail
if the original data type size in bytes is not the same.

Examples
--------
>>> s = pd.Series([-2, -1, 0, 1, 2], dtype='int8')
>>> s
0   -2
1   -1
2    0
3    1
4    2
dtype: int8

The 8 bit signed integer representation of `-1` is `0b11111111`, but
the same bytes represent 255 if read as an 8 bit unsigned integer:

>>> us = s.view('uint8')
>>> us
0    254
1    255
2      0
3      1
4      2
dtype: uint8

The views share the same underlying values:

>>> us[0] = 128
>>> s
0   -128
1     -1
2      0
3      1
4      2
dtype: int8

################################################################################
################################## Validation ##################################
################################################################################

Docstring for "pandas.Series.view" correct. :)
```

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Comments:
As I noted in the documentation:

> Series are instantiated with `dtype=float64` by default, so unlike `numpy.array.view()` this function will not try to preserve the current Series data type.

This is probably unintended behavior. If I simply try to create a view:
```
>>> pd.Series([1,2,3], dtype='int8').view()
...
ValueError: When changing to a larger dtype, its size must be a divisor of the total size in bytes of the last axis of the array.
```

This happens because when `dtype=None` the constructor assumes `float64`.
@villasv Thanks a lot for the contribution!

Let's discuss the actual method itself further in https://github.com/pandas-dev/pandas/issues/20251 and  https://github.com/pandas-dev/pandas/issues/20281