python什么时候引发FloatingPointError？

Question

Python documentation says that FloatingPointError is raised when a float calculation fails. But what is exactly meant here by "a float calculation" ? I tried adding, multiplying and dividing with floats but never managed to raise this specific error. Instead, i got a TypeError :

10/'a'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for /: 'int' and 'str'

Can someone help me understand when a FloatingPointError is raised in python?

Answer 1

It is part of the fpectl module. The FloatingPointError shouldn't be raised if you don't explicitly turn it on ( fpectl.turnon_sigfpe() ).

However mind the note:

The fpectl module is not built by default, and its usage is discouraged and may be dangerous except in the hands of experts. See also the section fpectl-limitations on limitations for more details.

Update: The fpectl module has been removed as of Python 3.7 .

---

Even with FloatingPointErrors turned on, 10/'a' will never raise one. It will always raise a TypeError. A FloatingPointError will only be raised for operations that reach the point of actually performing floating-point math, like 1.0/0.0 . 10/'a' doesn't get that far.

Answer 2

You can also trigger a FloatingPointError within numpy , by setting the appropriate numpy.seterr (or numpy.errstate context manager) flag. For an example taken from the documentation:

 >>> np.sqrt(-1) nan >>> with np.errstate(invalid='raise'): ... np.sqrt(-1) Traceback (most recent call last): File "<stdin>", line 2, in <module> FloatingPointError: invalid value encountered in sqrt 

Interestingly, it also raises FloatingPointError when all operands are integers:

 >>> old_settings = np.seterr(all='warn', over='raise') >>> np.int16(32000) * np.int16(3) Traceback (most recent call last): File "<stdin>", line 1, in <module> FloatingPointError: overflow encountered in short_scalars 

The documentation notes the conditions under which the FloatingPointError will be raised:

The floating-point exceptions are defined in the IEEE 754 standard [1]:

• Division by zero: infinite result obtained from finite numbers.
• Overflow: result too large to be expressed.
• Underflow: result so close to zero that some precision was lost.
• Invalid operation: result is not an expressible number, typically indicates that a NaN was produced.