Python - abs vs fabs

math.fabs() converts its argument to float if it can (if it can't, it throws an exception). It then takes the absolute value, and returns the result as a float.

In addition to floats, abs() also works with integers and complex numbers. Its return type depends on the type of its argument.

In [7]: type(abs(-2))
Out[7]: int

In [8]: type(abs(-2.0))
Out[8]: float

In [9]: type(abs(3+4j))
Out[9]: float

In [10]: type(math.fabs(-2))
Out[10]: float

In [11]: type(math.fabs(-2.0))
Out[11]: float

In [12]: type(math.fabs(3+4j))
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/home/npe/<ipython-input-12-8368761369da> in <module>()
----> 1 type(math.fabs(3+4j))

TypeError: can't convert complex to float

Edit: as @aix suggested, a better (more fair) way to compare the speed difference:

In [1]: %timeit abs(5)
10000000 loops, best of 3: 86.5 ns per loop

In [2]: from math import fabs

In [3]: %timeit fabs(5)
10000000 loops, best of 3: 115 ns per loop

In [4]: %timeit abs(-5)
10000000 loops, best of 3: 88.3 ns per loop

In [5]: %timeit fabs(-5)
10000000 loops, best of 3: 114 ns per loop

In [6]: %timeit abs(5.0)
10000000 loops, best of 3: 92.5 ns per loop

In [7]: %timeit fabs(5.0)
10000000 loops, best of 3: 93.2 ns per loop

In [8]: %timeit abs(-5.0)
10000000 loops, best of 3: 91.8 ns per loop

In [9]: %timeit fabs(-5.0)
10000000 loops, best of 3: 91 ns per loop

So it seems abs() only has slight speed advantage over fabs() for integers. For floats, abs() and fabs() demonstrate similar speed.

---

In addition to what @aix has said, one more thing to consider is the speed difference:

In [1]: %timeit abs(-5)
10000000 loops, best of 3: 102 ns per loop

In [2]: import math

In [3]: %timeit math.fabs(-5)
10000000 loops, best of 3: 194 ns per loop

So abs() is faster than math.fabs().