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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.
In computer programming, operators are constructs defined within programming languages which behave generally like functions, but which differ syntactically or semantically. Common simple examples include arithmetic (e.g. addition with +), comparison (e.g. "greater than" with >), and logical operations (e.g. AND, also written && in
The basic arithmetic operators are normally all left-associative, [1] which means that 1-2-3 = (1-2)-3 ≠ 1-(2-3), for instance. This does not hold true for higher operators. For example, exponentiation is normally right-associative in mathematics, [1] but is implemented as left-associative in some computer applications like Excel. In ...
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
Example Meaning (of example) Unicode code point High minus [14] ¯ ¯3: Denotes a negative number U+00AF ¯ MACRON: Lamp, Comment ⍝ ⍝This is a comment: Everything to the right of ⍝ denotes a comment U+235D ⍝ APL FUNCTIONAL SYMBOL UP SHOE JOT: RightArrow, Branch, GoTo → →This_Label: →This_Label sends APL execution to This_Label:
Function rank is an important concept to array programming languages in general, by analogy to tensor rank in mathematics: functions that operate on data may be classified by the number of dimensions they act on. Ordinary multiplication, for example, is a scalar ranked function because it operates on zero-dimensional data (individual numbers).
Python uses the + operator for string concatenation. Python uses the * operator for duplicating a string a specified number of times. The @ infix operator is intended to be used by libraries such as NumPy for matrix multiplication. [104] [105] The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to ...
Pairwise summation is the default summation algorithm in NumPy [9] and the Julia technical-computing language, [10] where in both cases it was found to have comparable speed to naive summation (thanks to the use of a large base case).