Handling Floating Point Error
Here we will discuss different example on how to handle floating point errors in Python:
Rounding to a Specific Decimal Place
By rounding the result to a specific decimal place (e.g., 2), you can mitigate the impact of small floating-point errors.
Python3
result = 1.2 - 1.0rounded_result = round(result, 2)print(f"Original Result: {result}\nRounded Result: {rounded_result}") |
Output:
Original Result: 0.19999999999999996
Rounded Result: 0.2
Using Decimal Class for High Precision
The decimal module provides the Decimal class, allowing for higher precision arithmetic. Setting the precision with getcontext().prec can help in managing precision for specific calculations
Python3
from decimal import Decimal, getcontextgetcontext().prec = 4 # Set precision to 4 decimal placesresult = Decimal('1.2') - Decimal('1.0')print(f"High Precision Result: {result}") |
Output:
High Precision Result: 0.2
Using Fractions for Exact Representations
The fractions module allows working with exact fractional representations, avoiding floating-point errors.
Python3
from fractions import Fractionresult = Fraction('1.2') - Fraction('1.0')print(f"Exact Fractional Result: {result}") |
Output:
Exact Fractional Result: 1/5
Handling Intermediate Results with Decimal
Use the Decimal class for intermediate calculations to minimize cumulative errors before converting back to float.
Python3
from decimal import Decimal, getcontextgetcontext().prec = 6 # Set precision to 6 decimal placesintermediate_result = Decimal('1.2') - Decimal('1.0')final_result = float(intermediate_result) # Convert back to float if neededprint(f"Intermediate Result: {intermediate_result}\nFinal Result: {final_result}") |
Output:
Intermediate Result: 0.2
Final Result: 0.2
Conclusion
Still, you thinking why python is not solving this issue, actually it has nothing to do with python. It happens because it is the way the underlying c platform handles floating-point numbers and ultimately with the inaccuracy, we’ll always have been writing down numbers as a string of fixed number of digits. Note that this is in the very nature of binary floating-point: this is not a bug either in Python or C, and it is not a bug in your code either. You’ll see the same kind of behaviors in all languages that support our hardware’s floating-point arithmetic although some languages may not display the difference by default, or in all output modes). We have to consider this behavior when we do care about math problems with needs exact precisions or using it inside conditional statements. Check floating point section in python documentation for more such behaviours.
Floating point error in Python
Python, a widely used programming language, excels in numerical computing tasks, yet it is not immune to the challenges posed by floating-point arithmetic. Floating-point numbers in Python are approximations of real numbers, leading to rounding errors, loss of precision, and cancellations that can throw off calculations. We can spot these errors by looking for strange results and using tools numpy.finfo to monitor precision. With some caution and clever tricks, we can keep these errors in check and ensure our Python calculations are reliable. In this article, we will explore the intricacies of floating-point errors in Python.
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