Convert indexing For Loop from MATLAB (uses numpy and pandas)

[bentaz]

Hello,

I am trying to convert a program to Python (using numpy and pandas as well) from MATLAB. The input is a (n by 2) array called scaletestmatrix (in this case a 6x2 array). Column one is the x values, which contains some values more than once. Column two is the y values. I need to find the largest and smallest y values that corresponds to each x value, then calculate the difference between the two y values for each x value, and finally find the mean of the differences.

The MATLAB code looks like this:

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

thickness = (finaly2values - finaly1values) %%% Creates a 1D array of the difference between the largest and smallest yvalues that correspond to each xvalue

meanthickness = (mean(thickness))%%% Finds the average of the thickness array

---

The input array (scaletestmatrix.csv) is:

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]

The finaly1values and finaly2values are:

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

Which results in: meanthickness = 14.5

This is what I have before the For Loop in Python:

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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

This is where I am stuck. Thank you in advance for your help.

[bentaz]

Hi,

If you had an array like:

---

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,

---

How would you you find the biggest value from column 2 for each number in column 1 at the same time? E.g. for 7 the biggest value is 30.

Thanks!

[nilamo]

Seems fairly easy with a dict, right? The key is whatever's on the left, and the value is the max value.

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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)

[bentaz]

That is much cleaner than the way I just figured out! I need the minimum value too (and then the mean of the difference between max and min). I tried tweaking your code by switching '>' to '<', but it returned all zeros.

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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)

The way I clunkily did it:

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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

Thank you for your advice! I've only been using Python for a few days, and I am not at all familiar with all the commands/functions/etc.