difference between np.inf and float('Inf')

Is there some difference between NumPy np.inf and float('Inf')? float('Inf') == np.inf returns True, so it seems they are interchangeable, thus I was wondering why NumPy has defined its own "inf" constant, and when should I use one constant instead of the other (considering style concerns too)?

1 Answer

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.inf True 

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 equal True 

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

1. 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') (or math.inf).

2. Because it's easier to remember.

At least for me, it's far easier to remember np.inf than that I need to call float with a string.

Also, NumPy defines some additional aliases for infinity:

 np.Inf np.inf np.infty np.Infinity np.PINF 

It also defines an alias for negative infinity:

 np.NINF 

Similarly for nan:

 np.nan np.NaN np.NAN 

3. 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 than a.

4. Accessing a constant is faster than creating the variable.

    %timeit np.inf 37.9 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.