python Create a dict mapping keys (N = 100, 200, 400, ... 51200) to arrays with linear entries (length N) derived from random sampling from a uniform distribution [0, 100000].

How can I insert, remove, and search nodes in a data structure with a time complexity that is less than O(n)?

Using Dask in Python

Produce a column-based (dict[str, list[str]] table given the first N(a parameter) rows  In python cannot use pandas, iloc, head, or .df  loop through the first N iterms of the tables columns assign the produced list of column values to the empty dictionary  finally return the dictionary  for example: if we have a dataframe of colors, people, and cars colors = {pink, blue, green, yellow, white} people = [marc, max, maria, lauren, emma} cars = {suv, sedan, coupe, off-road, luxury} the function given a N parameter such as 2. will return a table of color = {pinl, blue}, people = {marc, max}, cars = {suv, sedan}

Write Algorithm procedure code to illustrates the procedure for Fibonacci search. Here n, the number of data elements is such that:i) Fk+1 > (n+1); andii) Fk + m = (n +1) for some m ≥ 0, where Fk+1 and Fk are two consecutiveFibonacci numbers.

What is the a(3) term of the sequence {a(n)} where a(n) = 2n - 6?