Why does Python's floating-point math sometimes produce unexpected results?

Python Floating-Point Math Precision Woes

Floating-point arithmetic in Python, as exemplified in the code snippet provided, can lead to confusing results due to its limited precision. The values stored in floating-point variables are not always exact representations of the numbers they are intended to represent.

This phenomenon is not a bug in Python, but rather a fundamental limitation of floating-point arithmetic. Floating-point numbers are stored using a finite number of bits, which limits the number of significant digits they can accurately represent. When calculations involve numbers that cannot be exactly represented within these limitations, rounding errors occur.

In the examples provided:

• 4.2 - 1.8 results in 2.4000000000000004, instead of 2.4.
• 1.20 - 1.18 results in 0.020000000000000018, instead of 0.02.
• 5.1 - 4 results in 1.0999999999999996, instead of 1.1.

However, when performing integer calculations or comparisons, such as 5 - 4 and 5.0 - 4.0, Python uses exact integer values, resulting in the expected outputs of 1 and 1.0, respectively.

To overcome these precision issues, it is recommended to use Python's decimal module or the decimal type from the NumPy library, which provide higher precision for floating-point calculations. Additionally, ensuring that calculations involve numbers with similar orders of magnitude can minimize rounding errors.

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