This is one of my oldest NumPy pain-points:
>>> np.array([1, 2, 'three'])
array(['1', '2', 'three'],
dtype='<U21')
This is almost never what I want. In many cases, I simply write
dtype=object, but for others (e.g., numpy.where), it's a minor annoyance to
explicitly cast inputs to the right type.
Autoconverting numbers into strings occasionally introduces real bugs
(e.g., where using `np.nan` as a sentinel value for NA when working with
strings, as in https://github.com/pydata/xarray/pull/1847), but mostly just
hides bugs until later. It's certainly very un-Pythonic.
The sane promotion rule would be `np.promote_types(str, float) -> object`,
not a size 32 string.
Is it way too late to fix this for NumPy, or is this something we could
change in a major release? It would certainly need at least a deprecation
cycle. This is easy enough to introduce accidentally that there are
undoubtedly many users whose code would break if we changed this.
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