are not used to represent currency due to rounding and accumulation errors. Python provides several primitives specifically develo
Operations
In mathematics and computer science, an operation is an event that is carried out to satisfy a given task. Basic operations of a computer system are input, processing, output, storage, and control.
Basic Operators
An operator is a symbol that indicates an operation to be performed. We are familiar with operators in mathematics; operators used in computer programming are—in many ways—similar to mathematical operators.
Division Operator
We all learnt about division—and the division operator—in school. You probably know of both these symbols as representing division:
Modulus Operator
Modulus can be represented either as (mod or modulo) in computing operation. Modulus comes under arithmetic operations. Any number or variable which produces absolute value is modulus functionality. Magnitude of any function is totally changed by modulo operator as it changes even negative value to positive.
Operators
In the realm of programming, operators refer to the symbols that perform some function. They are tasked with instructing the compiler on the type of action that needs to be performed on the values passed as operands. Operators can be used in mathematical formulas and equations. In programming languages like Python, C, and Java, a variety of operators are defined.
2.19 LAB*: Program: Food receipt
Note: When accuracy is essential, floats are not used to represent currency due to rounding and accumulation errors. Python provides several primitives specifically developed to implement financial applications. However, these topics are beyond the scope of this lab.
Output each floating-point value with two digits after the decimal point, which can be achieved as follows:
print(f'{your_value:.2f}')
Summary
Floating-point arithmetic:
Mostly It is considered an esoteric topic. This is very surprising because floating point numbers are ubiquitous in computer systems. Almost all languages have floating point data types. Computers from personal computers to supercomputers have floating-point accelerators. Most compilers are occasionally asked to compile floating-point algorithms. Also, almost all operating systems must respond to floating-point exceptions such as overflow. This white paper presents a tutorial on aspects of floating-point numbers that directly impact computer system designers.
Compressing an infinitely large number of real numbers into a finite number of bits requires an approximate representation. There are infinitely many integers, but most programs can store the results of integer arithmetic in 32 bits. In contrast, most computations involving real numbers given a fixed number of bits result in quantities that cannot be represented exactly with that many bits. Therefore, the result of a floating-point computation often has to be rounded to fit in its finite representation. This rounding error is characteristic of floating-point arithmetic. The Relative Error and Ulps section describe how it is measured.
Anyway, since most floating-point calculations have rounding errors, does it matter if basic arithmetic operations introduce more rounding errors than necessary? This question is the main topic of this section. . The Guard Digits section describes guard digits, a means of reducing error when subtracting two adjacent numbers. Guard digits were considered significant enough by IBM that in 1968 they were added to the double precision format of the System/360 architecture (single precision already had guard digits) and all existing digits in the field machine have been improved. Two examples are given to illustrate the usefulness of guard digits.
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