Converting native python types to numpy dtypes

Question

Not to be confused with the inverse task, that is covered plenty. I am looking for something like np.dtype(7.7) == np.float. The motivation is to be able to handle any array-like input just like numpy itself. To construct the output or temporary data, I sometimes want to use the input type if possible.

Edit: Maybe that was a bad (too specific) example; I know that np.float happens to be just an alias for the builtin float. I was thinking more along the following lines.

myInput = something
# required to have a homogeneous data type in the documentation of my function;
# maybe constrained to float, int, string, lists of same length and type
# but I would like to handle simply as much of that as numpy can handle
numInput = len(myInput)
numOutput = numInput // 2 # just for example
myOutput = np.empty(shape=(numOutput), dtype=???)
for i in range(numOutput):
  myOutput[i] = whatever # maybe just a copy, hence the same data type

np.float is just a weird alias for the regular Python float type. I think it's only there for backward compatibility. It's not a NumPy dtype. — user2357112
– user2357112, Commented May 4, 2020 at 12:30

user2357112 · Accepted Answer · 2020-05-04 12:37:31Z

2

numpy.float is just the regular Python float type. It's not a NumPy dtype. It's almost certainly not what you need:

>>> import numpy
>>> numpy.float is float
True

If you want the dtype NumPy would coerce your scalar to, just make an array and get its dtype:

>>> numpy.array(7.7).dtype
dtype('float64')

If you want the type NumPy uses for scalars of this dtype, access the dtype's type attribute:

>>> numpy.array(7.7).dtype.type
<class 'numpy.float64'>

answered May 4, 2020 at 12:37

user2357112

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2 Comments

Chris K Over a year ago

(I'm the op) Your second block is what i want, thanks. I just would have hoped for something more elegant (and maybe more efficient).

ptth222 Over a year ago

I was looking for how to cast a single value using these types, and the second block is it. pandas is deprecating setting a new value of a different type, but doesn't give an easy way to cast to the destination type.

CallMePerox · Accepted Answer · 2020-05-04 12:41:19Z

You could simply use np.float64(original_float), or whatever numpy type you wish to convert your variable to.

For the record, this code works:

    val = 7.7
    if isinstance(val, float) is True:
        val = np.float64(val)
        if isinstance(val, np.float64) is True:
            print("Success!")

>>>Success!

Hope this helps.

Edit: I just saw @user2357112 supports Monica's comment to your question and it's important to note that effectively np.float acts the same way as float. The implementation I provided is oriented towards special numpy types like np.float32 or np.float64, the one I used in the test code. But if I performed the same test with just np.float this would be the result:

    val = 7.7
    if isinstance(val, float) is True:
       if isinstance(val, np.float) is True:
          print("Success!")
>>>Success!

Thus proving that from the interpreter's point of view float and np.float are pretty much the same type.

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