Python float 和 numpy float32 的区别

Question

What is the difference between the built in float and numpy.float32 ?

Example

a = 58682.7578125
print type(a)
print a
print type(numpy.float32(a))
print numpy.float32(a)

Output:

<type 'float'>
58682.7578125
<type 'numpy.float32'>
58682.8

I've found here that numpy.float32 is:

float32 Single precision float: sign bit, 8 bits exponent, 23 bits mantissa

didn't find what the built in float format is.

Answer 1

Python's standard float type is a C double : http://docs.python.org/2/library/stdtypes.html#typesnumeric

NumPy's standard numpy.float is the same, and is also the same as numpy.float64 .

Answer 2

Data type-wise numpy floats and built-in Python floats are the same, however boolean operations on numpy floats return np.bool_ objects, which always return False for val is True . Example below:

In [1]: import numpy as np
   ...: an_np_float = np.float32(0.3)
   ...: a_normal_float = 0.3
   ...: print(a_normal_float, an_np_float)
   ...: print(type(a_normal_float), type(an_np_float))

0.3 0.3
<class 'float'> <class 'numpy.float32'>

Numpy floats can arise from scalar output of array operations. If you weren't checking the data type, it is easy to confuse numpy floats for native floats.

In [2]: criterion_fn = lambda x: x <= 0.5
   ...: criterion_fn(a_normal_float), criterion_fn(an_np_float)

Out[2]: (True, True)

Even boolean operations look correct. However the result of the numpy float isn't a native boolean datatype, and thus can't be truthy.

In [3]: criterion_fn(a_normal_float) is True, criterion_fn(an_np_float) is True
Out[3]: (True, False)

In [4]: type(criterion_fn(a_normal_float)), type(criterion_fn(an_np_float))
Out[4]: (bool, numpy.bool_)

According to this github thread , criterion_fn(an_np_float) == True will evaluate properly, but that goes against the PEP8 style guide.

Instead, extract the native float from the result of numpy operations. You can do an_np_float.item() to do it explicitly (ref: this SO post ) or simply pass values through float() .