Why Does Python's Floating-Point Arithmetic Seem Inaccurate?

Python Floating-Point Arithmetic: Understanding the Discrepancies

In Python, floating-point arithmetic can exhibit apparent inaccuracies, leading users to question its correctness. This phenomenon stems from the limitations of representing real numbers in a finite binary system.

As witnessed in the code example you provided:

4.2 - 1.8
2.4000000000000004

1.20 - 1.18
0.020000000000000018

5.1 - 4
1.0999999999999996

5 - 4
1

5.0 - 4.0
1.0

The small discrepancies between the expected results and the actual outcomes are attributed to the use of floating-point representation. Floating-point numbers are approximations of real numbers that use a finite number of bits, resulting in rounding errors and loss of precision.

To understand this concept further, refer to the invaluable document, "The Floating Point Guide," which has been meticulously crafted by the Python community. This comprehensive resource provides profound insights into the intricacies of floating-point arithmetic and offers invaluable guidance for navigating this complex domain. It is essential reading for anyone seeking a thorough understanding of this fundamental aspect of Python.

The above is the detailed content of Why Does Python's Floating-Point Arithmetic Seem Inaccurate?. For more information, please follow other related articles on the PHP Chinese website!