CODEWITHSUNDEEP
pythonnumpyfloating-pointprecision
user6671981
user6671981

Reputation:

Should I use numpy.float64 instead of Python float when also using numpy.array

I have not encountered any problems thus far, so this is a question purely out of curiosity.

In Python I usually define floats and arrays of floats like this:

import numpy as np

s = 1.0
v = np.array([1.0, 2.0, 3.0])

In the case above s is a float, but the elements of v are of type numpy.float64.

To be more consistent I could, for example, do this instead:

import numpy as np

s = np.float64(1.0)
v = np.array([1.0, 2.0, 3.0])

Are there cases, from an accuracy/precision point of view, where it is recommended to use the "consistent" approach? What kind of errors, if any, can I expect in the "inconsistent" approach?

Upvotes: 5

Views: 8567

Answers (1)

MSeifert
MSeifert

Reputation: 152647

Python (at least CPython) uses doubles for it's float type internally - and doubles are 64bit floats (maybe not always but I haven't found a platform + compiler where doubles weren't 64bit floats).

So you shouldn't expect any kind of problems no matter if you keep them as float or np.float64.

However if you use Pythons float and NumPys np.float32 you could expect differences as the float has more precision (64 bits) than a np.float32 (32 bits).

Upvotes: 3

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