Are there data types with better precision than float?

Python’s built-in float type has double precision (it’s a C double in CPython, a Java double in Jython). If you need more precision, get NumPy and use its numpy.float128.

Decimal datatype

  • Unlike hardware based binary floating point, the decimal module has a user alterable precision (defaulting to 28 places) which can be as large as needed for a given problem.

If you are pressed by performance issuses, have a look at GMPY

For some applications you can use Fraction instead of floating-point numbers.

>>> from fractions import Fraction
>>> Fraction(1, 3**54)
Fraction(1, 58149737003040059690390169)

(For other applications, there’s decimal, as suggested out by the other responses.)

May be you need Decimal

>>> from decimal import Decimal    
>>> Decimal(2.675)
Decimal('2.67499999999999982236431605997495353221893310546875')

Floating Point Arithmetic

Here is my solution. I first create random numbers with random.uniform, format them in to string with double precision and then convert them back to float. You can adjust the precision by changing ‘.2f’ to ‘.3f’ etc..

import random
from decimal import Decimal

GndSpeedHigh = float(format(Decimal(random.uniform(5, 25)), '.2f'))
GndSpeedLow = float(format(Decimal(random.uniform(2, GndSpeedHigh)), '.2f'))
GndSpeedMean = float(Decimal(format(GndSpeedHigh + GndSpeedLow) / 2, '.2f')))
print(GndSpeedMean)