Floating-Point Representation in Python
In Python, floating-point numbers are represented using the IEEE 754 standard, which is a widely adopted standard for representing real numbers in computers. This standard defines the format and behavior of floating-point arithmetic, including the representation of special values like positive and negative infinity, and not-a-number (NaN).
Floating-Point Number Representation
Floating-point numbers in Python are stored in a 64-bit format, also known as "double-precision" floating-point. This format consists of three components:
- Sign bit: Determines whether the number is positive or negative.
- Exponent: Represents the power of 2 to which the significand is raised.
- Significand: The fractional part of the number.
The representation of a floating-point number in Python can be visualized as follows:
graph TD
A[Sign Bit] --> B[Exponent]
B --> C[Significand]
The IEEE 754 standard defines specific bit patterns for representing special values, such as positive and negative zero, positive and negative infinity, and NaN.
Precision and Rounding
Floating-point arithmetic in computers is not always exact due to the finite number of bits used to represent the numbers. This can lead to rounding errors and unexpected behavior when comparing floating-point values. For example, the following code demonstrates the issue:
import sys
x = 0.1
y = 0.2
print(x + y) ## Output: 0.30000000000000004
The reason for this behavior is that the binary representation of 0.1 and 0.2 cannot be represented exactly in the finite number of bits used to store floating-point numbers.
To address this issue, various techniques for precise floating-point comparison are discussed in the next section.