#### Category : comparison

I have this implementation to calculate the median of an array in linear time, but I need to count the number of comparisons that the algorithm makes, I have tried but I can’t make it work, any ideas of how to make it work? ”’ import sys import math import random DOMAIN = range(100000) # ..

I am trying to find the list in nested list, which has the maximum number of highest numbers, as shown bellow: test_list = [[200,1,100],[50,120,0],[40,300,0]] desired output = [200,1,100] [200,1,100] is the winner here, as out of its 3 digits, 2 are higher than the other lists 2 numbers. This would not be sequence sensitive however, ..

I need to get data from database for combo boxes and get their value and texts and while these data gathered I need to get users id from a csv file and compare them with database and insert non-duplicate one into the database. I’ve coded comparing csv file with database and it’s ok. as I’m ..

I am reading two dataframes looking at one column and then showing the difference in position between the two dataframe with a -1 or +1 etc. I have try the following code but it only shows 0 in Position Change when there should be a difference between British Airways and Ryanair first = pd.read_csv("C:UsersairmaPycharmProjectsVatsim_StatsVatsim_statsBase.csv", encoding=’unicode_escape’) ..

I am wondering if in Python exists a data structure for which is possible induce a custom internal ordering policy. I am aware of OrderedDict and whatnot, but they do not provide explicity what I am asking for. For example, OrderedDict just guarantees insertion order. I really would like something that in C++ is provided ..

Is there a good explanation why the max() function returns a different result given two arrays below (with the same elements, different ordering)? Possibly, just the fact it returns the first instance of "max" value which appears after the argument is evaluated? >>> max([1, False, 0, True]) # returns 1 >>> max([True, False, 0, 1]) ..

I need to identify companies in a list of tuples, named watchlist watchlist = [(‘TUI’, 820.0), (‘Whitbread’, 4300.0), (‘AstraZeneca’, 7500.0), (‘Standard Chartered’, 920.0), (‘Barclays’, 135.5)] That meet at least one of the following requirements, when compared to a df named comparison_df [enter code here][1] i) Any company in watchlist whose prices is equal to or ..