Python – use ints instead of floats

floating pointintpythonpython-3.x

I'm preparing for a class lesson (I'm teaching) and I'm trying to predict any possible questions from the students and I ran into one that I can't answer:

If we have floats, why do we ever use ints at all? What's the point?

I know (or at least I think) that floats take more memory because they have more accuracy, but surely the difference is nearly negligible as far as memory usage goes for most non-embedded applications.

And I realize in many cases we actually don't need a float, but honestly, why do we have ints in the first place? What's the point? There's nothing an int can do that a float can't.

So why are they there at all?

Edit: You could argue they're easier to write (3 vs. 3.0) but you could just make all numbers default to float, so 3 would be treated the same as 3.0. Why make it a different type?

Best Answer

Floating point numbers are approximations in many cases. Some integers (and decimals) can be exactly represented by a float, but most can't. See Floating Point Arithmetic: Issues and Limitations.

>>> a = 1000000000000000000000000000
>>> a+1 == a
False
>>> a = 1000000000000000000000000000.0
>>> a+1 == a
True

Resulting from this approximative nature of floats, some calculations may yield unexpected results (this isn't directly pertinent to the question, but it illustrates the point quite well):

>>> sum(1.1 for _ in range(9))
9.899999999999999

For example, when you're dealing with money calculations, it's better to use integers, or (if speed is not an issue) the decimal module.

Related Solutions

Python – Limiting floats to two decimal points

You are running into the old problem with floating point numbers that not all numbers can be represented exactly. The command line is just showing you the full floating point form from memory.

With floating point representation, your rounded version is the same number. Since computers are binary, they store floating point numbers as an integer and then divide it by a power of two so 13.95 will be represented in a similar fashion to 125650429603636838/(2**53).

Double precision numbers have 53 bits (16 digits) of precision and regular floats have 24 bits (8 digits) of precision. The floating point type in Python uses double precision to store the values.

For example,

>>> 125650429603636838/(2**53)
13.949999999999999

>>> 234042163/(2**24)
13.949999988079071

>>> a = 13.946
>>> print(a)
13.946
>>> print("%.2f" % a)
13.95
>>> round(a,2)
13.949999999999999
>>> print("%.2f" % round(a, 2))
13.95
>>> print("{:.2f}".format(a))
13.95
>>> print("{:.2f}".format(round(a, 2)))
13.95
>>> print("{:.15f}".format(round(a, 2)))
13.949999999999999

If you are after only two decimal places (to display a currency value, for example), then you have a couple of better choices:

Use integers and store values in cents, not dollars and then divide by 100 to convert to dollars.
Or use a fixed point number like decimal.

Python – How to use a decimal range() step value

Rather than using a decimal step directly, it's much safer to express this in terms of how many points you want. Otherwise, floating-point rounding error is likely to give you a wrong result.

You can use the linspace function from the NumPy library (which isn't part of the standard library but is relatively easy to obtain). linspace takes a number of points to return, and also lets you specify whether or not to include the right endpoint:

>>> np.linspace(0,1,11)
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ])
>>> np.linspace(0,1,10,endpoint=False)
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])

If you really want to use a floating-point step value, you can, with numpy.arange.

>>> import numpy as np
>>> np.arange(0.0, 1.0, 0.1)
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])

Floating-point rounding error will cause problems, though. Here's a simple case where rounding error causes arange to produce a length-4 array when it should only produce 3 numbers:

>>> numpy.arange(1, 1.3, 0.1)
array([1. , 1.1, 1.2, 1.3])

Best Answer

Related Solutions

Python – Limiting floats to two decimal points

Python – How to use a decimal range() step value

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