Comparing numpy datatypes to strings

Question

Many numpy functions take dtype arguments as either strings (like "float64") or numpy datatypes (like numpy.float64) or even python datatypes (like float).

I need to compare two datatypes and want to support this flexible interface. Is there a function under which all of these are forms are equivalent? I.e. I want the minimal function f such that

f("float64") == f(numpy.float64) == f(float)

What does numpy use internally?

Answer 1

You should read the Scalars page of the numpy documentation, which describes the data type hierarchy.

For comparing dtypes themselves, you can use np.issubdtype. Some examples:

>>> import numpy as np
>>> np.issubdtype(np.int32, int)
True
>>> np.issubdtype(np.int32, float)
False
>>> np.issubdtype(float, np.floating)
True
>>> np.issubdtype(float, np.inexact)
True
>>> np.issubdtype(np.float32, float)
True
>>> np.issubdtype(np.float32, int)
False
>>> np.issubdtype(np.float32, np.floating)
True

Answer 2

The easiest way to do it would be to create a new numpy.dtype object each time, as it has all the necessary type normalization/standardization and equality-checking already built in. Actually, I haven't taken a look at the internals, so it's possible that it doesn't actually create a new instance of dtype for ones that numpy already has (like how using numpy.array doesn't always create a new array), which would be nicely efficient.

numpy.float64 == numpy.dtype('float64') == numpy.dtype(numpy.float64) == numpy.dtype(float)
numpy.int32 == numpy.dtype('int32') == numpy.dtype(numpy.int32) == numpy.dtype(int)