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Slice semantics potentially differ per object; new semantics can be introduced when operator overloading the indexing operator. With Python standard lists (which are dynamic arrays), every slice is a copy. Slices of NumPy arrays, by contrast, are views onto the same underlying buffer.
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] The predecessor of NumPy, Numeric, was originally created by Jim Hugunin with ...
The possible slicings depend on the implementation details: for example, Fortran allows slicing off one column of a matrix variable, but not a row, and treat it as a vector; whereas C allow slicing off a row from a matrix, but not a column. On the other hand, other slicing operations are possible when array types are implemented in other ways.
Support for multi-dimensional arrays may also be provided by external libraries, which may even support arbitrary orderings, where each dimension has a stride value, and row-major or column-major are just two possible resulting interpretations. Row-major order is the default in NumPy [19] (for Python).
Some compiled languages such as Ada and Fortran, and some scripting languages such as IDL, MATLAB, and S-Lang, have native support for vectorized operations on arrays. For example, to perform an element by element sum of two arrays, a and b to produce a third c, it is only necessary to write c = a + b
Even if arrays are always created with contiguous elements, some array slicing operations may create non-contiguous sub-arrays from them. Illustration of row- and column-major order There are two systematic compact layouts for a two-dimensional array.
A Dask array comprises many smaller n-dimensional Numpy arrays and uses a blocked algorithm to enable computation on larger-than-memory arrays. During an operation, Dask translates the array operation into a task graph, breaks up large Numpy arrays into multiple smaller chunks, and executes the work on each chunk in parallel.
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.