15.
Floating Point Arithmetic: Issues and Limitations¶
Floating-point numbers are represented in computer hardware as base 2 (binary)
fractions. For example, the
decimal
fraction
0.125
has value 1/10 + 2/100 + 5/1000, and in the same way the
binary
fraction
0.001
has value 0/2 + 0/4 + 1/8. These two fractions have identical values, the only
real difference being that the first is written in base 10 fractional notation,
and the second in base 2.
Unfortunately, most decimal fractions cannot be represented exactly as binary
fractions. A consequence is that, in general, the decimal floating-point
numbers you enter are only approximated by the binary floating-point numbers
actually stored in the machine.
The problem is easier to understand at first in base 10. Consider the fraction
1/3. You can approximate that as a base 10 fraction:
or, better,
or, better,
and so on. No matter how many digits youâre willing to write down, the result
will never be exactly 1/3, but will be an increasingly better approximation of
1/3.
In the same way, no matter how many base 2 digits youâre willing to use, the
decimal value 0.1 cannot be represented exactly as a base 2 fraction. In base
2, 1/10 is the infinitely repeating fraction
0.0001100110011001100110011001100110011001100110011...
Stop at any finite number of bits, and you get an approximation. On most
machines today, floats are approximated using a binary fraction with
the numerator using the first 53 bits starting with the most significant bit and
with the denominator as a power of two. In the case of 1/10, the binary fraction
is
3602879701896397
/
2
**
55
which is close to but not exactly
equal to the true value of 1/10.
Many users are not aware of the approximation because of the way values are
displayed. Python only prints a decimal approximation to the true decimal
value of the binary approximation stored by the machine. On most machines, if
Python were to print the true decimal value of the binary approximation stored
for 0.1, it would have to display
>>> 0.1
0.1000000000000000055511151231257827021181583404541015625
That is more digits than most people find useful, so Python keeps the number
of digits manageable by displaying a rounded value instead
Just remember, even though the printed result looks like the exact value
of 1/10, the actual stored value is the nearest representable binary fraction.
Interestingly, there are many different decimal numbers that share the same
nearest approximate binary fraction. For example, the numbers
0.1
and
0.10000000000000001
and
0.1000000000000000055511151231257827021181583404541015625
are all
approximated by
3602879701896397
/
2
**
55
. Since all of these decimal
values share the same approximation, any one of them could be displayed
while still preserving the invariant
eval(repr(x))
==
x
.
Historically, the Python prompt and built-in
repr()
function would choose
the one with 17 significant digits,
0.10000000000000001
. Starting with
Python 3.1, Python (on most systems) is now able to choose the shortest of
these and simply display
0.1
.
Note that this is in the very nature of binary floating-point: this is not a bug
in Python, and it is not a bug in your code either. Youâll see the same kind of
thing in all languages that support your hardwareâs floating-point arithmetic
(although some languages may not
display
the difference by default, or in all
output modes).
For more pleasant output, you may wish to use string formatting to produce a limited number of significant digits:
>>> format(math.pi, '.12g') # give 12 significant digits
'3.14159265359'
>>> format(math.pi, '.2f') # give 2 digits after the point
'3.14'
>>> repr(math.pi)
'3.141592653589793'
Itâs important to realize that this is, in a real sense, an illusion: youâre
simply rounding the
display
of the true machine value.
One illusion may beget another. For example, since 0.1 is not exactly 1/10,
summing three values of 0.1 may not yield exactly 0.3, either:
>>> .1 + .1 + .1 == .3
False
Also, since the 0.1 cannot get any closer to the exact value of 1/10 and
0.3 cannot get any closer to the exact value of 3/10, then pre-rounding with
round()
function cannot help:
>>> round(.1, 1) + round(.1, 1) + round(.1, 1) == round(.3, 1)
False
Though the numbers cannot be made closer to their intended exact values,
the
round()
function can be useful for post-rounding so that results
with inexact values become comparable to one another:
>>> round(.1 + .1 + .1, 10) == round(.3, 10)
True
Binary floating-point arithmetic holds many surprises like this. The problem
with â0.1â is explained in precise detail below, in the âRepresentation Errorâ
section. See The Perils of Floating Point
for a more complete account of other common surprises.
As that says near the end, âthere are no easy answers.â Still, donât be unduly
wary of floating-point! The errors in Python float operations are inherited
from the floating-point hardware, and on most machines are on the order of no
more than 1 part in 2**53 per operation. Thatâs more than adequate for most
tasks, but you do need to keep in mind that itâs not decimal arithmetic and
that every float operation can suffer a new rounding error.
While pathological cases do exist, for most casual use of floating-point
arithmetic youâll see the result you expect in the end if you simply round the
display of your final results to the number of decimal digits you expect.
str()
usually suffices, and for finer control see the
str.format()
methodâs format specifiers in
Format String Syntax
.
For use cases which require exact decimal representation, try using the
decimal
module which implements decimal arithmetic suitable for
accounting applications and high-precision applications.
Another form of exact arithmetic is supported by the
fractions
module
which implements arithmetic based on rational numbers (so the numbers like
1/3 can be represented exactly).
If you are a heavy user of floating point operations you should take a look
at the NumPy package and many other packages for mathematical and
statistical operations supplied by the SciPy project. See <https://scipy.org>.
Python provides tools that may help on those rare occasions when you really
do
want to know the exact value of a float. The
float.as_integer_ratio()
method expresses the value of a float as a
fraction:
>>> x = 3.14159
>>> x.as_integer_ratio()
(3537115888337719, 1125899906842624)
Since the ratio is exact, it can be used to losslessly recreate the
original value:
>>> x == 3537115888337719 / 1125899906842624
True
The
float.hex()
method expresses a float in hexadecimal (base
16), again giving the exact value stored by your computer:
>>> x.hex()
'0x1.921f9f01b866ep+1'
This precise hexadecimal representation can be used to reconstruct
the float value exactly:
>>> x == float.fromhex('0x1.921f9f01b866ep+1')
True
Since the representation is exact, it is useful for reliably porting values
across different versions of Python (platform independence) and exchanging
data with other languages that support the same format (such as Java and C99).
Another helpful tool is the
math.fsum()
function which helps mitigate
loss-of-precision during summation. It tracks âlost digitsâ as values are
added onto a running total. That can make a difference in overall accuracy
so that the errors do not accumulate to the point where they affect the
final total:
>>> sum([0.1] * 10) == 1.0
False
>>> math.fsum([0.1] * 10) == 1.0
True
15.1.
Representation Error¶
This section explains the â0.1â example in detail, and shows how you can perform
an exact analysis of cases like this yourself. Basic familiarity with binary
floating-point representation is assumed.
Representation error
refers to the fact that some (most, actually)
decimal fractions cannot be represented exactly as binary (base 2) fractions.
This is the chief reason why Python (or Perl, C, C++, Java, Fortran, and many
others) often wonât display the exact decimal number you expect.
Why is that? 1/10 is not exactly representable as a binary fraction. Almost all
machines today (November 2000) use IEEE-754 floating point arithmetic, and
almost all platforms map Python floats to IEEE-754 âdouble precisionâ. 754
doubles contain 53 bits of precision, so on input the computer strives to
convert 0.1 to the closest fraction it can of the form
J
/2**
N
where
J
is
an integer containing exactly 53 bits. Rewriting
as
and recalling that
J
has exactly 53 bits (is
>=
2**52
but
<
2**53
),
the best value for
N
is 56:
>>> 2**52 <= 2**56 // 10 < 2**53
True
That is, 56 is the only value for
N
that leaves
J
with exactly 53 bits. The
best possible value for
J
is then that quotient rounded:
>>> q, r = divmod(2**56, 10)
>>> r
6
Since the remainder is more than half of 10, the best approximation is obtained
by rounding up:
Therefore the best possible approximation to 1/10 in 754 double precision is:
7205759403792794 / 2 ** 56
Dividing both the numerator and denominator by two reduces the fraction to:
3602879701896397 / 2 ** 55
Note that since we rounded up, this is actually a little bit larger than 1/10;
if we had not rounded up, the quotient would have been a little bit smaller than
1/10. But in no case can it be
exactly
1/10!
So the computer never âseesâ 1/10: what it sees is the exact fraction given
above, the best 754 double approximation it can get:
>>> 0.1 * 2 ** 55
3602879701896397.0
If we multiply that fraction by 10**55, we can see the value out to
55 decimal digits:
>>> 3602879701896397 * 10 ** 55 // 2 ** 55
1000000000000000055511151231257827021181583404541015625
meaning that the exact number stored in the computer is equal to
the decimal value 0.1000000000000000055511151231257827021181583404541015625.
Instead of displaying the full decimal value, many languages (including
older versions of Python), round the result to 17 significant digits:
>>> format(0.1, '.17f')
'0.10000000000000001'
The
fractions
and
decimal
modules make these calculations
easy:
>>> from decimal import Decimal
>>> from fractions import Fraction
>>> Fraction.from_float(0.1)
Fraction(3602879701896397, 36028797018963968)
>>> (0.1).as_integer_ratio()
(3602879701896397, 36028797018963968)
>>> Decimal.from_float(0.1)
Decimal('0.1000000000000000055511151231257827021181583404541015625')
>>> format(Decimal.from_float(0.1), '.17')
'0.10000000000000001'