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Given a function that accepts an array, a range query (,) on an array = [,..,] takes two indices and and returns the result of when applied to the subarray [, …,].For example, for a function that returns the sum of all values in an array, the range query (,) returns the sum of all values in the range [,].
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.
In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever next() is called on the iterator, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.
Since Python is a dynamically-typed language, Python values, not variables, carry type information. All variables in Python hold references to objects, and these references are passed to functions. Some people (including Guido van Rossum himself) have called this parameter-passing scheme "call by object reference".
This is the original definition for maplist, mapping a function over successive rest lists: maplist[x;f] = [null[x] -> NIL;T -> cons[f[x];maplist[cdr[x];f]]] The function maplist is still available in newer Lisps like Common Lisp, [5] though functions like mapcar or the more generic map would be preferred.
Binary search: assuming the array is sorted, check the middle value of the current search range, then if the value is lesser check the lower range, and if the value is greater check the upper range. Block sort uses two variants: one which finds the first position to insert a value in the sorted array, and one which finds the last position.
A memory address a is said to be n-byte aligned when a is a multiple of n (where n is a power of 2). In this context, a byte is the smallest unit of memory access, i.e. each memory address specifies a different byte.
Julia has the vec(A) function as well. In Python NumPy arrays implement the flatten method, [ note 1 ] while in R the desired effect can be achieved via the c() or as.vector() functions. In R , function vec() of package 'ks' allows vectorization and function vech() implemented in both packages 'ks' and 'sn' allows half-vectorization.