I've come across the the same problem / question today and I'm not completely satisfied with any of the answers given so far. The core of the question seems to be:
Can someone suggest a good way to convert from float to Decimal [...] perhaps limiting number of significant digits that can be supported?
Short answer / solution: Yes.
def ftod(val, prec = 15):
return Decimal(val).quantize(Decimal(10)**-prec)
Long Answer:
As nosklo pointed out it is not possible to preserve the input of the user after it has been converted to float.
It is possible though to round that value with a reasonable precision and convert it into Decimal.
In my case I only need 2 to 4 digits after the separator, but they need to be accurate. Let's consider the classic 0.1 + 0.2 == 0.3 check.
>>> 0.1 + 0.2 == 0.3
False
Now let's do this with conversion to decimal (complete example):
>>> from decimal import Decimal
>>> def ftod(val, prec = 15): # float to Decimal
... return Decimal(val).quantize(Decimal(10)**-prec)
...
>>> ftod(0.1) + ftod(0.2) == ftod(0.3)
True
The answer by Ryabchenko Alexander was really helpful for me. It only lacks a way to dynamically set the precision – a feature I want (and maybe also need). The Decimal documentation FAQ gives an example on how to construct the required argument string for quantize():
>>> Decimal(10)**-4
Decimal('0.0001')
Here's how the numbers look like printed with 18 digits after the separator (coming from C programming I like the fancy python expressions):
>>> for x in [0.1, 0.2, 0.3, ftod(0.1), ftod(0.2), ftod(0.3)]:
... print("{:8} {:.18f}".format(type(x).__name__+":", x))
...
float: 0.100000000000000006
float: 0.200000000000000011
float: 0.299999999999999989
Decimal: 0.100000000000000000
Decimal: 0.200000000000000000
Decimal: 0.300000000000000000
And last I want to know for which precision the comparision still works:
>>> for p in [15, 16, 17]:
... print("Rounding precision: {}. Check 0.1 + 0.2 == 0.3 is {}".format(p,
... ftod(0.1, p) + ftod(0.2, p) == ftod(0.3, p)))
...
Rounding precision: 15. Check 0.1 + 0.2 == 0.3 is True
Rounding precision: 16. Check 0.1 + 0.2 == 0.3 is True
Rounding precision: 17. Check 0.1 + 0.2 == 0.3 is False
15 seems to be a good default for maximum precision. That should work on most systems. If you need more info, try:
>>> import sys
>>> sys.float_info
sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)
With float having 53 bits mantissa on my system, I calculated the number of decimal digits:
>>> import math
>>> math.log10(2**53)
15.954589770191003
Which tells me with 53 bits we get almost 16 digits. So 15 ist fine for the precision value and should always work. 16 is error-prone and 17 definitly causes trouble (as seen above).
Anyway ... in my specific case I only need 2 to 4 digits of precision, but as a perfectionist I enjoyed investigating this :-)
Any suggestions / improvements / complaints are welcome.