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Conversely, precision can be lost when converting representations from integer to floating-point, since a floating-point type may be unable to exactly represent all possible values of some integer type. For example, float might be an IEEE 754 single precision type, which cannot represent the integer 16777217 exactly, while a 32-bit integer type ...
Integer Floating-point String Array Associative array/Object; Ion: null null.null null.bool null.int null.float null.decimal null.timestamp null.string null.symbol null.blob null.clob null.struct null.list null.sexp. true: false: 685230-685230 0xA74AE 0b111010010101110: 6.8523015e5 "A to Z" ''' A to Z '''
MessagePack is more compact than JSON, but imposes limitations on array and integer sizes.On the other hand, it allows binary data and non-UTF-8 encoded strings. In JSON, map keys have to be strings, but in MessagePack there is no such limitation and any type can be a map key, including types like maps and arrays, and, like YAML, numbers.
Double-precision floating-point format (sometimes called FP64 or float64) is a floating-point number format, usually occupying 64 bits in computer memory; it represents a wide range of numeric values by using a floating radix point. Double precision may be chosen when the range or precision of single precision would be insufficient.
Regular expression (Perl compatible regular expressions ("PCRE") version 8.41 with UTF-8 support) [6] An important differentiator to JSON is that BSON contains types not present in JSON (e.g. datetime, byte array, and proper IEEE 754 floats) and offers type-strict handling for several numeric types instead of a universal "number" type.
In JavaScript, bitwise operators convert their operands to 32-bit signed integers and give integer results. This means that a bitwise OR with zero converts a value to an integer (a very simple "conceptual" presentation of bitwise operators may not deal with type conversion at all, but every programming language defines operators for its own ...
In addition to support for vectorized arithmetic and relational operations, these languages also vectorize common mathematical functions such as sine. For example, if x is an array, then y = sin (x) will result in an array y whose elements are sine of the corresponding elements of the array x. Vectorized index operations are also supported.
The standard type hierarchy of Python 3. In computer science and computer programming, a data type (or simply type) is a collection or grouping of data values, usually specified by a set of possible values, a set of allowed operations on these values, and/or a representation of these values as machine types. [1]