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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]
In array languages, operations are generalized to apply to both scalars and arrays. Thus, a+b expresses the sum of two scalars if a and b are scalars, or the sum of two arrays if they are arrays. An array language simplifies programming but possibly at a cost known as the abstraction penalty.
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.
Processors, especially vector processors, are often optimized for array operations. Arrays are useful mostly because the element indices can be computed at run time. Among other things, this feature allows a single iterative statement to process arbitrarily many elements of an array. For that reason, the elements of an array data structure are ...
Keys and values need not be the same type within an alist. Lisp and Scheme provide operators such as assoc to manipulate alists in ways similar to associative arrays. A set of operations specific to the handling of association lists exists for Common Lisp, each of these working non-destructively.
An array data structure can be mathematically modeled as an abstract data structure (an abstract array) with two operations get(A, I): the data stored in the element of the array A whose indices are the integer tuple I. set(A, I, V): the array that results by setting the value of that element to V. These operations are required to satisfy the ...
Dask Array [16] is a high-level collection that parallelizes array-based workloads and maintains the familiar NumPy API, such as slicing, arithmetic, reductions, mathematics, etc., making it easy for Numpy users to scale up array operations. A Dask array comprises many smaller n-dimensional Numpy arrays and uses a blocked algorithm to enable ...
The dynamic array has performance similar to an array, with the addition of new operations to add and remove elements: Getting or setting the value at a particular index (constant time) Iterating over the elements in order (linear time, good cache performance) Inserting or deleting an element in the middle of the array (linear time)