如何将 numpy 数组从“float64”转换为“float”

Question

How do I convert a numpy array from type 'float64' to type 'float' ? Specifically, how do I convert an entire array with dtype 'float64' to have dtype 'float' ? Is this possible? The answer for scalars in the thought-to-be duplicate question above does not address my question.

Consider this:

>>> type(my_array[0])
<type 'numpy.float64'>

>>> # Let me try to convert this to 'float':
>>> new_array = my_array.astype(float)
>>> type(new_array[0])
<type 'numpy.float64'>

>>> # No luck.  What about this:
>>> new_array = my_array.astype('float')
>>> type(new_array[0])
<type 'numpy.float64'>

>>> # OK, last try:
>>> type(np.inf)
<type 'float'>
>>> # Yeah, that's what I want.
>>> new_array = my_array.astype(type(np.inf))
>>> type(new_array[0])
<type 'numpy.float64'>

If you're unsure why I might want to do this, see this question and its answers.

Answer 1

Yes, actually when you use Python's native float to specify the dtype for an array , numpy converts it to float64 . As given in documentation -

Note that, above, we use the Python float object as a dtype. NumPy knows that int refers to np.int_ , bool means np.bool_ , that float is np.float_ and complex is np.complex_ . The other data-types do not have Python equivalents.

And -

float_ - Shorthand for float64.

This is why even though you use float to convert the whole array to float , it still uses np.float64 .

According to the requirement from the other question , the best solution would be converting to normal float object after taking each scalar value as -

float(new_array[0])

---

A solution that I could think of is to create a subclass for float and use that for casting (though to me it looks bad). But I would prefer the previous solution over this if possible. Example -

In [20]: import numpy as np

In [21]: na = np.array([1., 2., 3.])

In [22]: na = np.array([1., 2., 3., np.inf, np.inf])

In [23]: type(na[-1])
Out[23]: numpy.float64

In [24]: na[-1] - na[-2]
C:\Anaconda3\Scripts\ipython-script.py:1: RuntimeWarning: invalid value encountered in double_scalars
  if __name__ == '__main__':
Out[24]: nan

In [25]: class x(float):
   ....:     pass
   ....:

In [26]: na_new = na.astype(x)


In [28]: type(na_new[-1])
Out[28]: float                           #No idea why its showing float, I would have thought it would show '__main__.x' .

In [29]: na_new[-1] - na_new[-2]
Out[29]: nan

In [30]: na_new
Out[30]: array([1.0, 2.0, 3.0, inf, inf], dtype=object)

Answer 2

You can create an anonymous type float like this

>>> new_array = my_array.astype(type('float', (float,), {}))
>>> type(new_array[0])
<type 'float'>

Answer 3

This is not a good idea if you're trying to stay in numpy, but if you're done calculating and are moving out into native python, you can use

ndarray.tolist()

This converts arrays to lists (of lists) of appropriate native types. It also works on numpy scalar values.