DOC: Clarify the type alias deprecation message

[seberg]

This tries to clarify the type alias deprecation message slightly
to give more guidance on how to review the replacement (if desired).

---

The new messages are as follows:

In [1]: np.bool                                                                                                                                             
<ipython-input-1-190120470286>:1: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.bool
Out[1]: bool

In [2]: np.object                                                                                                                                           
<ipython-input-2-4921af820668>:1: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.object
Out[2]: object

In [3]: np.str                                                                                                                                              
<ipython-input-3-15a72efb409b>:1: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.str
Out[3]: str

In [4]: np.float                                                                                                                                            
<ipython-input-4-b3b46df38e48>:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.float
Out[4]: float

In [5]: np.complex                                                                                                                                          
<ipython-input-5-087dad13a955>:1: DeprecationWarning: `np.complex` is a deprecated alias for the builtin `complex`. To silence this warning, use `complex` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.complex128` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.complex
Out[5]: complex

In [6]: np.int                                                                                                                                              
<ipython-input-6-5dcbb6b27796>:1: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.int
Out[6]: int

In [7]: np.long                                                                                                                                             
<ipython-input-7-2567cec2f44b>:1: DeprecationWarning: `np.long` is a deprecated alias for `np.compat.long`. To silence this warning, use `np.compat.long` by itself. In the likely event your code does not need to work on Python 2 you can use the builtin `int` for which `np.compat.long` is itself an alias. Doing this will not modify any behaviour and is safe. When replacing `np.long`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.long
Out[7]: int

In [8]: np.unicode                                                                                                                                          
<ipython-input-8-a16bff60575c>:1: DeprecationWarning: `np.unciode` is a deprecated alias for `np.compat.unciode`. To silence this warning, use `np.compat.unciode` by itself. In the likely event your code does not need to work on Python 2 you can use the builtin `str` for which `np.compat.unciode` is itself an alias. Doing this will not modify any behaviour and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  np.unicode

[seberg]

I tried to give some more details about integers in the release notes, and mention the precision version float64 rather than float_ in the message. So hopefully it is a bit more clear, it may be slightly opinionated.

[charris]

What was the reason to change the 1.20.0 release note?

[seberg]

Thanks, have to try how to fix that reference. Will fix (maybe tomorrow morning). There was some discussion that this release note/deprecation is maybe a bit unclear. I hope this is a bit clearer (and "recipe style"), so the idea was to change it before the release.

[eric-wieser]

This looks great, thanks @seberg!

[seberg]

Thanks Eric and Ross for figuring out how to correctly link the user guide... applied the changes, so should be good to go (assuming CI passes now).

[charris]

Thanks sebastian.