A call to int() on a float does not raise an error when it drops the fractional part. It returns a different whole number, and if that number feeds a risk threshold, the rule can pass or fail on values it was never meant to judge. The failure is silent because nothing in the program signals that a fraction was discarded. This article explains exactly what the conversion does, how it turns into a missed control, and how to close the gap before a limit goes live.
What int() does to a float
Python’s built-in types documentation states: “Conversion from float to int truncates, discarding the fractional part.” Truncation means the value moves toward zero. That is different from rounding to the nearest integer and different from flooring, and the difference matters most for negative numbers.
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| Input | int(x) |
round(x) |
math.floor(x) |
|---|---|---|---|
| 99.9 | 99 | 100 | 99 |
| 100.4 | 100 | 100 | 100 |
| -0.5 | 0 | 0 | -1 |
| -1.7 | -1 | -2 | -2 |
The first column is the one a conversion produces. None of the other columns is “more correct” in general. Each encodes a different policy, and int() implements only the truncation policy, whether or not your rule asked for it.
How a threshold quietly changes its answer
Consider a rule that blocks exposure above a cap of 499 units. The check looks reasonable:
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MAX_EXPOSURE = 499
def exposure_ok(exposure):
return not (int(exposure) > MAX_EXPOSURE)
print(exposure_ok(499.99)) # True: the rule passes it
print(int(499.99)) # 499The true exposure is 0.99 units over the cap. After truncation it equals the cap, so the strict comparison never fires. Nothing was rejected, logged, or flagged. The control simply did not see the value it was written to catch.
Float arithmetic adds a second source of error before the conversion even runs. Binary floating point cannot represent 0.29 exactly, and multiplying by 100 can land just below the intended integer:
print(0.29 * 100) # 28.999999999999996
print(int(0.29 * 100)) # 28A rule that expects 29 cents now sees 28. Both errors come from the same place: the value was converted to an integer without anyone deciding what should happen to the fraction.
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A documented payment example
In Python issue 27697, opened on 2016-08-05, the issue author Nathan Snobelen reported: “We have some payment code which formats numbers for processing in our system and we noticed that the payment of 1108431.38 was dropped by a penny to 1108431.37.” That is a historical report about one payment path. It shows the mechanism clearly, but it is not evidence of how any particular production system behaves today, and it does not establish that every multiplication by 100 loses a cent.
Why the failure stays hidden
- No exception is raised.
int()returns a valid integer, so type checks and input validation pass. - No fraction is recorded. The discarded digits do not appear in logs unless the code logs the original value.
- Only boundary values are affected. Most amounts behave correctly in testing, so the defect surfaces only near a limit, which is exactly where risk rules matter most.
- Negative values behave differently. A value such as -0.5 becomes 0, which can pass a check written for negative exposures.
Decide the rule before you convert
Write down what the threshold should do with a fractional input. The usual options are an exact decimal comparison, rounding to nearest, flooring, or rejecting non-integral input. int() is only correct when truncation is the policy you actually want.
- State the rule in one sentence, including how fractions are handled and which boundary is inclusive.
- Validate before conversion. For floats, reject or flag values that are not whole numbers:
def require_whole(value): if not float(value).is_integer(): raise ValueError(f'non-integral input: {value!r}') return int(value) - Log the original value next to the converted value at every decision point, so a discarded fraction can be seen after the fact.
- Test values on both sides of each boundary, including negative values if your domain allows them.
Represent money with Decimal built from strings
For currency and other decimal quantities, construct Decimal from a string or from already-validated decimal input rather than passing a float through the conversion chain:
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from decimal import Decimal
amount = Decimal('1108431.38')
cents = amount * 100 # Decimal('110843138.00')
print(int(cents)) # 110843138Multiplication and conversion stay exact here because the value never passed through binary floating point.
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The Python decimal documentation distinguishes the two forms. Decimal('3.14') represents the decimal value you typed. Decimal(3.14) captures the binary float that is already the nearest approximation, which carries many more digits:
from decimal import Decimal
print(Decimal('3.14'))
# 3.14
print(Decimal(3.14))
# 3.140000000000000124344978758017532527446746826171875If the source data arrives as text, parse it as text. If it arrives as a float, the precision is already gone before your code sees it.
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Make inexact rounding visible
Decimal contexts can turn silent rounding into an error. Setting the Inexact and Rounded traps makes an operation raise when it loses digits:
from decimal import Decimal, Inexact, Rounded, localcontext
with localcontext() as ctx:
ctx.traps[Inexact] = True
ctx.traps[Rounded] = True
Decimal('1.005').quantize(Decimal('0.01')) # raises decimal.InexactUse this in code paths where a silently rounded value would change a decision. Leave it off where rounding is the intended policy and has been specified.
The digit limit is a different problem
CPython also limits conversions between very long decimal strings and integers. The CPython security FAQ for CVE-2020-10735 explains that the limit exists to prevent excessive CPU use when untrusted input is parsed with int() or converted back to a string. That is a denial-of-service safeguard. It has nothing to do with losing a fractional part, and it is not a fix for a truncated risk value. Do not disable it as a remedy for a threshold bug.
Check conversions at library and C boundaries
Implicit integer conversion can also happen inside extension code. Python issue tracker issue 36048, opened on 2019-02-20, documents C integer conversion paths that used __int__ and could truncate non-integral Decimal or Fraction values. That discussion is historical, and the handling has changed across versions, so confirm the behavior of the Python version you deploy before relying on it either way.
Quick Recap
Checklist before a threshold ships
- The rule states how fractional values are treated, and the code implements that rule explicitly.
- Money and other decimal quantities are parsed from strings into
Decimal, not from floats. - Every
int()call in a decision path has a validation step or a documented reason for truncation. - Decision logs record the original value and the converted value.
- Boundary tests cover values just below, at, and just above each limit, including negative inputs where valid.
- The deployed Python version has been checked against the behavior you are relying on.
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