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The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one. The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations.
Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a user to act as though the index is an array-like sequence of integers, regardless of how it is actually defined. [9]: 110–113 Pandas supports hierarchical indices with multiple values per data point.
Structure of arrays (SoA) is a layout separating elements of a record (or 'struct' in the C programming language) into one parallel array per field. [1] The motivation is easier manipulation with packed SIMD instructions in most instruction set architectures, since a single SIMD register can load homogeneous data, possibly transferred by a wide internal datapath (e.g. 128-bit).
A stack can be easily implemented either through an array or a linked list, as it is merely a special case of a list. [19] In either case, what identifies the data structure as a stack is not the implementation but the interface: the user is only allowed to pop or push items onto the array or linked list, with few other helper operations.
As exchanging the indices of an array is the essence of array transposition, an array stored as row-major but read as column-major (or vice versa) will appear transposed. As actually performing this rearrangement in memory is typically an expensive operation, some systems provide options to specify individual matrices as being stored transposed.
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 ...
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.
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 required to have the same size and should use the same data representation.