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CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.
Python supports a wide variety of string operations. Strings in Python are immutable, so a string operation such as a substitution of characters, that in other programming languages might alter the string in place, returns a new string in Python. Performance considerations sometimes push for using special techniques in programs that modify ...
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]
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. 1992: Fortran 90 and above
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. [98] [99] The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to ...
The append operation merely increments the counter; until the whole array is used, when the append operation may be defined to fail. This is an implementation of a dynamic array with a fixed capacity, as in the string type of Pascal.
Following Lisp, other high-level programming languages which feature linked lists as primitive data structures have adopted an append. To append lists, as an operator, Haskell uses ++, OCaml uses @. Other languages use the + or ++ symbols to nondestructively concatenate a string, list, or array.
By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.