Overflowerror: Convert Int Large to Float in Python

Python overflows when an arithmetic operation yields a number that cannot be represented as a floating point. This happens particularly in the case of very large numbers or complex calculations that go beyond the scope of the Python interpreter. The OverflowError is raised to indicate that the result could not be contained within those constraints. So, what are some causes of OverflowError and how can it be handled in Python, let’s explore this in this article.

What is OverflowError in Python?

OverflowError occurs when the result of an arithmetic operation exceeds those representational limits placed on it by the Python interpreter. Normally this could happen with large numbers or complex computations like:

• Exceeding Floating-Point Representation Limits
• Exponential Growth Leading to Overflow

Why Does Overflowerror: Int Too Large To Convert To Float Occur?

Below, are the reasons for occurring Overflowerror: Int Too Large To Convert To Float in Python.

• Exceed Floating-Point
• Exponential Growth Lead Overflow

Exceeding Floating-Point Representation Limits

In Python, floating-point numbers have finite precision because they are implemented with hardware representation limitations. The OverflowError is raised when trying to change an integer into float while its value is too large for the floating-point representation’s limits.

For instance, in line one there is a variable called “large_integer” which contains a value that is so huge such that converting it to float using float() leads to overflow error due to float representation bounds being surpassed.

Python3

large_integer = 10**1000
result = float(large_integer)
print(result)

Output :

Hangup (SIGHUP)
Traceback (most recent call last):
  File "Solution.py", line 2, in <module>
    result = float(large_integer)
OverflowError: int too large to convert to float

Exponential Growth Leading to Overflow

In Exponential growth, values increase rapidly and when these values become too big to fit into a floating point representation may cause the floating point overflow error.

For example, calculating 2.0 ** 10000 results in a much higher floating-point number that is larger than what can be held in a floating-point which finally causes OverflowError when trying to print it.

Python3

exponential_result = 2.0 ** 10000
print(exponential_result)

Output :

Hangup (SIGHUP)
Traceback (most recent call last):
  File "Solution.py", line 1, in <module>
    exponential_result = 2.0 ** 10000
OverflowError: (34, 'Numerical result out of range')

Fix Overflowerror: Int Too Large to Float

• Use Smaller Integer
• Use Scientific Notation
• Use sys.float_info.max Limit

Use Smaller Integer

When you face the error “OverflowError: int too large to convert to float”, one simple solution is to use smaller integer. Python has a limited precision for floating point numbers, and for big integers, this limit can be exceeded. By either dividing large number by some factor or using small number directly overflow problem can be avoided.

Python3

l = 10**18
s = int(l / 10)
f = float(s)
print(s)

Output

100000000000000000

Use Scientific Notation

Scientific notation is one way of representing very large or very small numerical values in a more compact form.By formatting the integer in scientific notation, you can avoid overflow errors when converting to a float.Here, “{:e}”.format(large_integer) formats the large integer in scientific notation and then it’s converted to a float.

Python3

li = 10**18
fv = float("{:e}".format(li))
print(fv)

Output

1e+18

Use sys.float_info.max Limit

The sys.float_info.max attribute represents the maximum finite representable floating-point number in Python. You can use this information to restrict conversion of huge integers into float values. This technique guarantees that the resulting float value doesn’t extend beyond the greatest possible finite representable float value.

Python3

import sys

li = 10**18
mfv = sys.float_info.max
fv = min(li, mfv)
print(fv)

Output

1000000000000000000

Use a Different Data Type (Decimal)

When you do not necessarily require precision that comes with floating point numbers, you might consider using the Decimal data type from decimal module.Decimal allows for arbitrary precision arithmetic and it can handle very large numbers without losing any precision. Using Decimal will help you surpass the limitations of finite precision that usually accompanies with floating numbers.

Python3

from decimal import Decimal

li = 10**18
dv = Decimal(li)
print(dv)

Output

1000000000000000000

Conclusion

In conclusion, “OverflowError: int too large to convert to float” in Python indicates that there was an attempt to convert an integer value into floating point but the value was so big that it could not be exactly represented by finite precision floating point numbers. It is fundamental because numbers are treated differently and also due to the limitations of how floating-point representation works in python. Different techniques for addressing this problem are available. The choice varies depending on what exactly we need from the program including level of accuracy and range covered by variables.