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Cast float to int in Python results wrong answer [duplicate]

This question already has answers here:
Is floating point math broken?
(31 answers)
Closed 1 year ago.
I have an algorithm that is calculating:
result = int(14949283383840498/5262*27115)
The correct result should be 77033412951888085, but Python3.8 gives me 77033412951888080
I also have tried the following:
>>> result = 77033412951888085
>>> print(result)
77033412951888085
>>> print(int(result))
77033412951888085
>>> print(float(result))
7.703341295188808e+16
>>> print(int(float(result)))
77033412951888080
It seems the problem occours when I cast the float to int. What am I missing?
PS: I have found that using result = 14949283383840498//5262*27115 I get the right answer!

Casting is not the issue. Floating-point arithmetic has limitations with respect to precision. See https://docs.python.org/3/tutorial/floatingpoint.html
Need to either use integer division or use the decimal module which defaults to using 28 places in precision.
Using integer division
result = 14949283383840498 // 5262 * 27115
print(result)
Output:
77033412951888085
Using decimal module
from decimal import Decimal
result = Decimal(14949283383840498) / 5262 * 27115
print(result)
Output:
77033412951888085

It is an precision limitation :
result = 14949283383840498/5262*27115
result
7.703341295188808e+16
In this case, result is a float.
You can see that the precision is of 15 digits.
Convert that to int, you see that the last non zero digit is 8, it is correct to what result: float show when printed.
Try the following:
print(sys.float_info.dig)
15
dig is the maximum number of decimal digits that can be faithfully represented in a float.
A very good explanation regarding this issue is available here.
But there are ways to do better with Python, see from the Python's doc:
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

Python issues with math.floor: int too large to convert to float

I'd like to calculate (⌊2^(1918)*π⌋+124476) in python but I get this error when I do it using the following code:
b = math.floor((2**1918) * math.pi) + 124476
print(b)
OverflowError: int too large to convert to float
How can you get this to work? In the end I just like to have it all as hexadecimal (if that helps with answering my question) but I was actually only trying to get it as an integer first :)

The right solution really depends on how precise the results are required. Since 2^1918 already is too large for both standard integer and floating point containers, it is not possible to get away with direct calculations without loosing all the precision below ~ 10^300.
In order to compute the desired result, you should use arbitrary-precision calculation techniques. You can implement the algorithms yourself or use one of the available libraries.
Assuming you are looking for an integer part of your expression, it will take about 600 decimal places to store the results precisely. Here is how you can get it using mpmath:
from mpmath import mp
mp.dps = 600
print(mp.floor(mp.power(2, 1918)*mp.pi + 124476))
74590163000744215664571428206261183464882552592869067139382222056552715349763159120841569799756029042920968184704590129494078052978962320087944021101746026347535981717869532122259590055984951049094749636380324830154777203301864744802934173941573749720376124683717094961945258961821638084501989870923589746845121992752663157772293235786930128078740743810989039879507242078364008020576647135087519356182872146031915081433053440716531771499444683048837650335204793844725968402892045220358076481772902929784589843471786500160230209071224266538164123696273477863853813807997663357545.0
Next, all you have to do is to convert it to hex representation (or extract hex from its internal binary form), which is a matter for another subject :)

The basic problem is what the message says. Python integers can be arbitrarily large, larger even than the range of a float. 2**1918 in decimal contains 578 significant digits and is way bigger than the biggest float your IEEE754 hardware can represent. So the call just fails.
You could try looking at the mpmath module. It is designed for floating point arithmetic outside the bounds of what ordinary hardware can handle.

I think the problem can be solved without resorting to high-precision arithmetic. floor(n.something + m) where m and n are integers is equal to floor(n.something) + m. So in this case you are looking for floor(2**1918 * pi) plus an integer (namely 124476). floor(2**whatever * pi) is just the first whatever + 2 bits of pi. So just look up the first 1920 bits of pi, add the bits for 124476, and output as hex digits.
A spigot algorithm can generate digits of pi without using arbitrary precision. A quick web search seems to find some Python implementations for generating digits in base 10. I didn't see anything about base 2, but Plouffe's formula generates base 16 digits if I am not mistaken.

The problem is that (2**1918) * math.pi attempts to convert the integer to 64-bit floating point precision, which is insufficiently large. You can convert math.pi to a fraction to use arbitrary precision.
>>> math.floor((2**1918) * fractions.Fraction(math.pi) + 124476)
74590163000744212756918704280961225881025315246628098737524697383138220762542289800871336766911957454080350508173317171375032226685669280397906783245438534131599390699781017605377332298669863169044574050694427882869191541933796848577277592163846082732344724959222075452985644173036076895843129191378853006780204194590286508603564336292806628948212533561286572102730834409985441874735976583720122784469168008083076285020654725577288682595262788418426186598550864392013191287665258445673204426746083965447956681216069719524525240073122409298640817341016286940008045020172328756796
Note that arbitrary precision applies to the calculation; math.pi is defined only with 64-bit floating point precision. Use an external library, such as mpmath, if you need the exact value.
To convert this to a hexadecimal string, use hex or a string format:
>>> hex(math.floor((2**1918) * fractions.Fraction(math.pi) + 124476))
'0xc90fdaa22168c0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001e63c'
>>> '%x' % math.floor((2**1918) * fractions.Fraction(math.pi) + 124476)
'c90fdaa22168c0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001e63c'
>>> f'{math.floor((2**1918) * fractions.Fraction(math.pi) + 124476):X}'
'C90FDAA22168C0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001E63C'
For string formats, x provides lower-case hex whereas X provides upper-case case.

Increasing floating point precision in Python [duplicate]

This question already has answers here:
Is floating point arbitrary precision available?
(5 answers)
Closed 2 years ago.
I was working on a project to compute the Leibniz approximation for pi with the below code:
def pi(precision):
sign = True
ret = 0
for i in range(1,precision+1):
odd = 2 * i - 1
if sign:
ret += 1.0 / odd
else:
ret -= 1.0 / odd
sign = not sign
return ret
However, the output value was always was 12 digits long. How can I increase the precision (e.g. more digits) of the calculation? Does Python support more precise floating points, or will I have to use some external library?

Try using Decimal.
Read Arbitrary-precision elementary mathematical functions (Python)original for more information

Python's float type maps to whatever your platform's C compiler calls a double (see http://en.wikipedia.org/wiki/IEEE_floating_point_number).
The Python standard library also comes with an arbitrary-precision decimal module, called decimal: http://docs.python.org/2/library/decimal.html

The Leibniz formula converges extremely slowly - honestly, you won't live long enough for it get 12 digits of accuracy. Click here for one way to accelerate it enormously.

With Python's float, you get 15–17 digits of precision (if you are seeing fewer, you may need to use a different format specifier when printing).
If you need more, you'll need to use a different method (one that only uses integer arithmetic), or a different way to represent floating-point numbers.
See Python floating point arbitrary precision available?

1 == 2 for large numbers of 1

I'm wondering what causes this behaviour. I haven't been able to find an answer that covers this. It is probably something simple and obvious, but it is not to me. I am using python 2.7.3 in Ubuntu.
In [1]: 2 == 1.9999999999999999
Out[1]: True
In [2]: 2 == 1.999999999999999
Out[2]: False
EDIT:
To clarify my question. Is there a written(in documentation) max number of 9's where python will evaluate the expression above as being equal to 2?

Python uses floating point representation
What a floating point actually is, is a fixed-width binary number (called the "significand") plus a small integer to tell you how many powers of two to shift that value by (the "exponent"). Plus a sign bit. Just like scientific notation, but in base 2 instead of 10.
The closest 64 bit floating point value to 1.9999999999999999 is 2.0, because 64 bit floating point values (so-called "double precision") uses 52 bits of significand, which is equivalent to about 15 decimal places. So the literal 1.9999999999999999 is just another way of writing 2.0. However, the closest value to 1.999999999999999 is less than 2.0 (I think it's 1.9999999999999988897769753748434595763683319091796875 exactly, but I'm too lazy to check that's correct, I'm just relying on Python's formatting code to be exact).
I don't actually know whether the use specifically of 64 bit floats is required by the Python language, or is an implementation detail of CPython. But whatever size is used, the important thing is not specifically the number of decimal places, it is where the closest floating-point value of that size lies to your decimal literal. It will be closer for some literals than others.
Hence, 1.9999999999999999 == 2 for the same reason that 2.0 == 2 (Python allows mixed-type numeric operations including comparison, and the integer 2 is equal to the float 2.0). Whereas 1.999999999999999 != 2.

Types coercion
>>> 2 == 2.0
True
And consequences of maximum number of digits that can be represented in python :
>>> import sys
>>> sys.float_info.dig
15
>>> 1.9999999999999999
2.0
more from docs
>>> float('9876543211234567')
9876543211234568.0
note ..68 on the end instead of expected ..67

This is due to the way floats are implemented in Python. To keep it short and simple: Since floats almost always are an approximation and thus have more digits than most people find useful, the Python interpreter displays a rounded value.
More detailed, floats are stored in binary. This means that they're stored as fractions to the base 2, unlike decimal, were you can display a float as fractions to the base 10. However, most decimal fractions don't have an exact representation in binary. Because of that, they are typically stored with a precision of 53 bits. This renders them pretty much useless if you want to do more complex arithmetic operations, since you'll run into some strange problems, e. g.:
>>> 0.1 + 0.2
0.30000000000000004
>>> round(2.675, 2)
2.67
See The docs on floats as well.

Mathematically speaking, 2.0 does equal 1.9999... forever. They are two different ways of writing the same number.
However, in software, it's important to never compare two floats or decimals for equality - instead, subtract them, take the absolute value, and verify that the (always positive) difference is sufficiently low for your purposes.
EG:
if abs(value1 - value2) < 1e10:
# they are close enough
else:
# they are not
You probably should set EPSILON = 1e10, and use the symbolic constant instead of scattering 1e10 throughout your code, or better still use a comparison function.

More Digits in Irrational Numbers

>>> str(1.41421356237309504880168872420969807856967187537694807317667973799073247846210703885038753432764157273501384623091229702)
'1.41421356237'
Is there a way I can make str() record more digits of the number into the string? I don't understand why it truncates by default.

Python's floating point numbers use double precision only, which is 64 bits. They simply cannot represent (significantly) more digits than you're seeing.
If you need more, have a look at the built-in decimal module, or the mpmath package.

Try this:
>>> from decimal import *
>>> Decimal('1.41421356237309504880168872420969807856967187537694807317667973799073247846210703885038753432764157273501384623091229702')
Decimal('1.41421356237309504880168872420969807856967187537694807317667973799073247846210703885038753432764157273501384623091229702')
The float literal is truncated by default to fit in the space made available for it (i.e. it's not because of str):
>>> 1.41421356237309504880168872420969807856967187537694807317667973799073247846210703885038753432764157273501384623091229702
1.4142135623730951
If you need more decimal places use decimal instead.

The Python compiler is truncating; your float literal has more precision than can be represented in a C double. Express the number as a string in the first place if you need more precision.

That's because it's converting to a float. It's not the conversion to the string that's causing it.
You should use decimal.Decimal for representing such high precision numbers.