difference between np.inf and float('Inf')
TL, DR: There is no difference and they can be used interchangeably.
Besides having the same value as math.inf
and float('inf')
:
>>> import math>>> import numpy as np>>> np.inf == float('inf')True>>> np.inf == math.infTrue
It also has the same type:
>>> import numpy as np>>> type(np.inf)float>>> type(np.inf) is type(float('inf'))float
That's interesting because NumPy also has it's own floating point types:
>>> np.float32(np.inf)inf>>> type(np.float32(np.inf))numpy.float32>>> np.float32('inf') == np.inf # nevertheless equalTrue
So it has the same value and the same type as math.inf
and float('inf')
which means it's interchangeable.
Reasons for using np.inf
It's less to type:
np.inf
(6 chars)math.inf
(8 chars; new in python 3.5)float('inf')
(12 chars)
That means if you already have NumPy imported you can save yourself 6 (or 2) chars per occurrence compared to
float('inf')
(ormath.inf
).Because it's easier to remember.
At least for me, it's far easier to remember
np.inf
than that I need to callfloat
with a string.Also NumPy also defines some additional aliases for infinity:
np.Infnp.infnp.inftynp.Infinitynp.PINF
It also defines an alias for negative infinity:
np.NINF
Similarly for
nan
:np.nannp.NaNnp.NAN
Constants are constants
This point is based on CPython and could be completely different in another Python implementation.
A
float
CPython instance requires 24 Bytes:>>> import sys>>> sys.getsizeof(np.inf)24
If you can re-use the same instance you might save a lot of memory compared to creating lots of new instances. Of course, this point is mute if you create your own
inf
constant but if you don't then:a = [np.inf for _ in range(1000000)]b = [float('inf') for _ in range(1000000)]
b
would use 24 * 1000000 Bytes (~23 MB) more memory thana
.Accessing a constant is faster than creating the variable.
%timeit np.inf37.9 ns ± 0.692 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)%timeit float('inf')232 ns ± 13.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)%timeit [np.inf for _ in range(10000)]552 µs ± 15.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)%timeit [float('inf') for _ in range(10000)]2.59 ms ± 78.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Of course, you can create your own constant to counter that point. But why bother if NumPy already did that for you.