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Google Sheets – Get unique values from a column and return the values a single column

I was hoping to get some help and direction. I have two sheets–a ‘Raw data’ sheet and a ‘Dashboard’ sheet. A user would dump the raw data in the first sheet and the dashboard will summarise figures automatically (built some scripts for it).

In the ‘Raw Data’ sheet, there is a column for Teams which contain the names of the different teams a user is a part of (screenshot below).

Screenshot of raw data

Job to be done: I would like to get the list of unique teams from this column excluding the blank ones.

Challenge: I have this formula which works well. However, it includes the blank value which I want to exclude and I couldn’t seem to figure out to adjust the formula. I included the output of the formula below. I tried rethinking the formula using JOIN or CONCATENATE but kept hitting the 50000 character limit error.


Output of the formula above

Appreciate any help.

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Unique Distancing Problem, Memoization in Haskell

I’ve been reading CodeReview for a while but this is my first post here. This is also my first Haskell program. I’ve tried to learn Haskell unsuccessfully before, so I was looking for a puzzle to program in haskell, to go head first in the world of pure functionnal programming.

The code bellow is basically a brute-force solution to Matt Parker’s Unique Distancing Problem. The challenge is to find a way of arranging N counters on a N by N grid such that every distance is different, Matt Parker’s video explains it well. I stepped up the difficulty a bit by generalising this to any dimensions (2 by 3 by 4 grid for example) and any number of counters.

So having nowhere to start, I made a python script in imperative style and gradually removed assignations and mutable object in favor of list comprehensions and functions. I then rewrote the program in Haskell, and spends countless hours searching things on the web. (I know my python code is very very bad but please focus on the haskell version.)

Python Code

import math

def nd_range(d):
    return ((j,) + i for i in nd_range(d(1:)) for j in range(d(0))) if d else ((),)

def g(candidate, dists, counters, n, dims):
    return () if counters and any((not d or d in dists or dists.add(d)) for d in (math.dist(candidate, counter) for counter in counters))else f(n - 1, dims, counters | {candidate})

def f(n, dims, counters=set()):
    dists = {math.dist(i, j) for i in counters for j in counters}
    return {x for candidate in nd_range(dims) for x in g(candidate, dists.copy(), counters, n, dims)} if n else {frozenset(counters)} if counters else set()

if __name__ == "__main__":
    print(len(f(6, (6, 6))))

So, I have no idea on how to optimise Haskell code, I did A-B testing every time i made a modification to see if it was faster or slower. For 6 counters and a 6 by 6 grid, the Haskell code now takes 33s on my computer while Python takes 170s (unfair comparison since i’ve not writen good python).

Haskell Code

import qualified Data.Set as Set

type P = (Int) -- Point
type S = Set.Set P -- Solution (Set of Point) :: Set (Int)
type SS = Set.Set S -- Set of possible Solutions (Set of Set of Points) :: Set Set (Int)

-- List the points of an N-Dimentionnal grid with dimentions given as (width, height, depth,...)
-- Example: nd_range (1,2,3) gives us ((0,0,0),(0,0,1),(0,0,2),(0,1,0),(0,1,1),(0,1,2))
nd_range :: (Int) -> (P)
nd_range () = (())
nd_range d = ((j : i) | j <- (0..(pred(head d))), i <- nd_range (tail d))

-- returns the square of the distance between two N-Dimentionnal points
dist :: P -> P -> Int
dist a = sum . zipWith (((^2).).(-)) a
-- This function comes from and is noticeably faster than mine:
-- dist a b = sum (map (^2) (zipWith (-) a b))

countDists candidate i (dists, n)
    | Set.member d dists = (dists, n)
    | otherwise = (Set.insert d dists, n+1)
    where d = dist candidate i

-- returns the set of all distances between points of a set
dists :: S -> Set.Set Int
dists counter = Set.fromList ( dist i j | i <- Set.toList counter, j <- Set.toList counter)

-- returns True if the point given satisfies the unique distancing constrain given the other points
isPossible :: P -> S -> Bool
isPossible candidate counter = snd (foldr (countDists candidate) (dists counter, 0) counter) == length counter

-- recursively runs for every possible points (back-tracking style) and flattens result into a set
f :: Int -> (Int) -> S -> SS
f 0 dims counter = Set.singleton counter
f n dims counter = Set.fromList ( x |
    candidate <- nd_range dims,
    x <- Set.toList (if isPossible candidate counter
                        then f (n-1) dims (Set.insert candidate counter)
                        else Set.empty))

-- calls the main recursive function with initial empty set of point
solve :: Int -> (Int) -> SS
solve n dims = f n dims Set.empty

ans = solve 6 (6, 6)
main = print ans >> print ( length ans )

The majority of the ressources are utilised to compute distances, as you can see using the profiler, I think it says that the dist function was called 352542672 times (maybe it’s not what it means), but since there are only 648 possible pairs of points, I was wondering on how to improve on the performances.

Profiler log

    total time  =      134.88 secs   (134876 ticks @ 1000 us, 1 processor)
    total alloc = 216,038,138,480 bytes  (excludes profiling overheads)

COST CENTRE  MODULE    SRC                            %time %alloc

dist         Main      parker_dist.hs:15:1-38          66.2   73.9
countDists   Main      parker_dist.hs:(19,1)-(22,30)   16.3   12.3
isPossible   Main      parker_dist.hs:30:1-110          8.2    3.8
dists        Main      parker_dist.hs:26:1-91           3.9    5.5
countDists.d Main      parker_dist.hs:22:11-30          2.2    0.0
nd_range     Main      parker_dist.hs:(10,1)-(11,73)    2.0    4.2
f            Main      parker_dist.hs:(34,1)-(39,40)    1.2    0.3

                                                                                           individual      inherited
COST CENTRE         MODULE                SRC                           no.     entries  %time %alloc   %time %alloc

MAIN                MAIN                  <built-in>                    138           0    0.0    0.0   100.0  100.0
 CAF                Main                  <entire-module>               275           0    0.0    0.0   100.0  100.0
  ans               Main                  parker_dist.hs:45:1-20        278           1    0.0    0.0   100.0  100.0
   solve            Main                  parker_dist.hs:43:1-33        279           1    0.0    0.0   100.0  100.0
    f               Main                  parker_dist.hs:(34,1)-(39,40) 280     2090329    1.2    0.3   100.0  100.0
     isPossible     Main                  parker_dist.hs:30:1-110       282    74837124    8.2    3.8    96.8   95.5
      countDists    Main                  parker_dist.hs:(19,1)-(22,30) 283   352542672   16.3   12.3    76.5   77.6
       countDists.d Main                  parker_dist.hs:22:11-30       284   352542672    2.2    0.0    60.3   65.3
        dist        Main                  parker_dist.hs:15:1-38        285   352542672   58.1   65.3    58.1   65.3
      dists         Main                  parker_dist.hs:26:1-91        286     2078808    3.9    5.5    12.1   14.1
       dist         Main                  parker_dist.hs:15:1-38        287    46638684    8.1    8.6     8.1    8.6
     nd_range       Main                  parker_dist.hs:(10,1)-(11,73) 281     6236427    2.0    4.2     2.0    4.2
  main              Main                  parker_dist.hs:46:1-40        276           1    0.0    0.0     0.0    0.0
 CAF                Data.Set.Internal     <entire-module>               274           0    0.0    0.0     0.0    0.0
 CAF                GHC.Conc.Signal       <entire-module>               238           0    0.0    0.0     0.0    0.0
 CAF                GHC.IO.Encoding       <entire-module>               219           0    0.0    0.0     0.0    0.0
 CAF                GHC.IO.Encoding.Iconv <entire-module>               217           0    0.0    0.0     0.0    0.0
 CAF                GHC.IO.Handle.FD      <entire-module>               208           0    0.0    0.0     0.0    0.0
 CAF                GHC.IO.Handle.Text    <entire-module>               206           0    0.0    0.0     0.0    0.0
 main               Main                  parker_dist.hs:46:1-40        277           0    0.0    0.0     0.0    0.0

My guess is that memoization is not possible as easily as in python because it uses mutable state to work (in python, you just add @lru_cache(max_size=n) in front of your function). So if you have any suggestions on how to implement memoization or on how to improve my code, feel free to share them.

Query for unique item in list

I have an SP list of items, each item has a unique Title. I would like to use the title as my way to find and then update that row of data. I thought the below code would find only the item that has a Title = “Apptest” but it always gives me all items in the list and all of them are updated. I get the same results if I remove the ViewXml line, so I think there is something wrong with that line but I can’t find it. Before:
Before running code After:After running the code

var list = clientContext.Web.Lists.GetByTitle("Test");
            var camlQuery = new CamlQuery
                ViewXml = @"<Query><Where><Eq><FieldRef Name='Title' /><Value Type='Text'>Apptest</Value></Eq></Where></Query>"

            var listItems = list.GetItems(camlQuery);

            foreach (var listItem in listItems)
                listItem("Title") = "AppUpdateTest";

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