Julia is a high-level, dynamic, general-purpose programming language that can be used to write software applications, and is well-suited for data analysis and computational science. Numbers are important in any programming language. Numbers in Julia is classified into two types: Integer and Floating-Point Numbers. Julia provides a wide range of primitive numeric types. These inbuilt numeric datatypes help Julia to take full advantage of computational resources. Julia also provides software support for Arbitrary Precision Arithmetic, which can handle numeric operations which is difficult to represent in native hardware representations. However, support for Arbitrary Precision Arithmetic comes at the cost of slower performance. Complex and Rational Numbers are defined on top of primitive numeric types.
The following are Julia’s primitive numeric types:
Type Signed Number of bits Range Int8 Yes 8 -2^7 to 2^7 – 1 UInt8 No 8 0 to 2^8-1 Int16 Yes 16 -2^15 to 2^15-1 UInt16 No 16 0 to 2^16 – 1 Int32 Yes 32 -2^31 to 2^31-1 UInt32 No 32 0 to 2^32-1 Int64 Yes 64 -2^63 to 2^63-1 UInt64 No 64 0 to 2^64 – 1 Int128 Yes 128 -2^127 to 2^127 – 1 UInt128 No 128 0 to 2^128 – 1 Bool N/A 8 false(0) and true(1)
- Floating point numbers
Type Precision Number of bits Float16 half 16 Float32 single 32 Float64 double 64
The default type for an integer literal depends on whether the system is 32-bit or 64-bit. The variable Sys.WORD_SIZE indicates whether the system is 32-bit or 64-bit:
However, larger integer literals that cannot be represented using 32 bits are represented using 64 bits, regardless of the system type.
Unsigned integers are represented using the 0x prefix and hexadecimal digits 0-9a-f or 0-9A-F. Size of the unsigned integer is determined by the number of hex digits used.
Binary and octal literals are also supported in Julia.
Min-Max Values of Integers
Floating Point Numbers
Floating Point Numbers are represented in a standard format. There is no literal format for Float32, but we can convert values to Float32 by writing an ‘f’ or explicit typecasting.
Floating point zero
Floating point numbers have a positive zero and a negative zero which are equal to each other but have different binary representations.
julia> 0.0 == -0.0 true julia> bitstring(0.0) "0000000000000000000000000000000000000000000000000000000000000000" julia> bitstring(-0.0) "1000000000000000000000000000000000000000000000000000000000000000"
Special floating-point values
|positive infinity||Inf16, Inf32, Inf||a value greater than all finite floating-point values|
|negative infinity||-Inf16, -Inf32, -Inf||a value less than all finite floating-point values|
|not a number||NaN16, NaN32, NaN||a value not comparable to any floating point values including itself|
Global constant ‘im’ is used to represent complex number i where i is square root of -1.
Julia has rational numbers to represent exact ratios of integers. Rational numbers are constructed using the // operator.
- Julia end Keyword | Marking end of blocks in Julia
- Julia function keyword | Create user-defined functions in Julia
- Julia continue Keyword | Continue iterating to next value of a loop in Julia
- Julia break Keyword | Exiting from a loop in Julia
- Julia local Keyword | Creating a local variable in Julia
- Julia global Keyword | Creating a global variable in Julia
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- Counting ones and zeros in binary representation of a number in Julia - count_ones() and count_zeros() Methods
- Counting leading ones and zeros in binary representation of a number in Julia - leading_ones() and leading_zeros() Methods
- Counting trailing ones and zeros in binary representation of a number in Julia - trailing_ones() and trailing_zeros() Methods
- Getting square root of a number in Julia - sqrt() and isqrt() Methods
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