Output:
[hr]
data = importdata('C:\\Users\\...\\scaletestmatrix.csv');
xvalues = data(:,1);     %%% Extract column 1 of the data as xvalues
yvalues = data(:,2);     %%% Extract column 2 of the data as yvalues

smallest = min(xvalues);    %%% Finds the smallest xvalue
biggest = max(xvalues);     %%% Finds the largest xvalue

length = (biggest - smallest + 1);     %%% Finds number of xvalues to evaluate  in the impending For Loop

startvalue = smallest    %%% Set the starting  x value of the impending For Loop

%%%  During each loop we find the indices for all of the xvalues equal to startvalue (which increases by 1 each loop).
%%%  Then we find the minimum and maximum yvalue that corresponds to the set of indices obtained during that loop.
%%%  We then assign those values to finaly1values(y) and finaly2values(y) where y is the index specified by the loop number.

for y=1:length      %%%  Set the number of loops to run
       finaly1values(y) = min(yvalues(xvalues == startvalue))  
       finaly2values(y) = max(yvalues(xvalues == startvalue))  
       startvalue = (startvalue +1); 
end

Output:
5,	20,
5,	30,
7,	25,
7,	15,
7,	30,
9,	50,
9,	20,
9,	35,
6,	25,
6,	35,
10,	33,
10,	23,
10,	13,
8,	17,
8,	15,
8,	16,
[hr]

Output:
finaly1values,	finaly2values,
20,	30,
25,	35,
15,	30,
15,	17,
20,	50,
13,	33,
[hr]

import pandas as pd
import numpy as np
filename = 'C:\\Users\\...\\scaletestmatrix.csv'
data = pd.read_csv(filename, header = None);  ### Read the data file
data = data.values ;  ### Convert DataFrame to array of values (numbers)
data = data.astype(np.float); ### Convert from scientific notation to floating

xvalues = data[:,0]; ### Extract column 1 of the data as xvalues
yvalues = data[:,1]; ### Extract column 2 of the data as yvalues

smallest = min(xvalues); ### Find the smallest xvalue, his may not be necessary in Python
biggest= max(xvalues);  ### Find the largest xvalue, this may not be necessary in Python

startvalue = smallest ### This may not be necessary in Python.

length = int((biggest - smallest) + 1); ### This may not be necessary in Python

data = [ ] # I have no idea where this is coming from

import collections
max_values = collections.defaultdict(int)

for pair in data:
    key, value = pair
    if value > max_values[key]:
        max_values[key] = value

print(max_values)

data = [ ] # I have no idea where this is coming from
 
import collections
max_values = collections.defaultdict(int)
 
for pair in data:
    key, value = pair
    if value > max_values[key]:
        max_values[key] = value

#### My attempt at minimum value
       
min_values = collections.defaultdict(int)
 
for pair2 in data:
    key2, value2 = pair2
    if value2 < min_values[key2]:
        min_values[key2] = value2
 
print(max_values)
print(min_values)

xvalues = data[:,0]; ### Extract column 1 of the data as xvalues
yvalues = data[:,1]; ### Extract column 2 of the data as yvalues

xmin = min(xvalues) ### Find smallest x value
xmax = max(xvalues) ### Find largest x value

thickArray = [] ### Create empty array thickArray

itt = xmin  ## Set starting value for iteration

while itt < (xmax+1): # first while loop code
  i = np.where(xvalues==itt) ### Gets indicies of xvalues = itt for each loop
  i = np.asarray(i) ### Converts i to array
  i = (yvalues[i])  ### Gets values for indices as array
  size = np.shape(i) ### Returns array dimensions. Not Used
  i = max(i)  ### Idk. Reduces to [] from [[]]
  maxi = max(i) ### Finds max of array
  mini = min(i) ### Finds min of array
  diff = maxi - mini ### Find difference between max and min
  thickArray.append(diff) ### Adds diff for that iteration to thickArray
  itt = itt + 1 ### Increment iteration by 1

FinalThickness1 = np.mean(thickArray) ### Find the mean of thickArray