Question

so I have a matrix Data in this format:
Data = [Date Time Price]

Now what I want to do is plot the Price against the Time, but my data is very large and has lines where there are multiple Prices for the same Date/Time, e.g. 1st, 2nd lines

29  733575.459548611  40.0500000000000
29  733575.459548611  40.0600000000000
29  733575.459548612  40.1200000000000
29  733575.45954862   40.0500000000000

I want to take an average of the prices with the same Date/Time and get rid of any extra lines. My goal is to do linear intrapolation on the values which is why I must have only one Time to one Price value.

How can I do this? I did this (this reduces the matrix so that it only takes the first line for the lines with repeated date/times) but I don’t know how to take the average

function [ C ] = test( DN )

[Qrows, cols] = size(DN);
C = DN(1,:);

for i = 1:(Qrows-1)
if DN(i,2) == DN(i+1,2)
    %n = 1;
    %while DN(i,2) == DN(i+n,2) && i+n<Qrows
    %        n = n + 1;
    %end
    % somehow take average;
else
    C = [C;DN(i+1,:)];
end
end

Answer 1

[C,ia,ic] = unique(A,'rows') also returns index vectors ia and ic
such that C = A(ia,:) and A = C(ic,:)

If you use as input A only the columns you do not want to average over (here: date & time), ic with one value for every row where rows you want to combine have the same value.

Getting from there to the means you want is for MATLAB beginners probably more intuitive with a for loop: Use logical indexing, e.g. DN(ic==n,3) you get a vector of all values you want to average (where n is the index of the date-time-row it belongs to). This you need to do for all different date-time-combinations.

A more vector-oriented way would be to use accumarray, which leads to a solution of your problem in two lines:

[DateAndTime,~,idx] = unique(DN(:,1:2),'rows');
Price = accumarray(idx,DN(:,3),[],@mean);

I’m not quite sure how you want the result to look like, but [DataAndTime Price] gives you the three-row format of the input again.

Note that if your input contains something like:

1 0.1 23
1 0.2 47
1 0.1 42
1 0.1 23

then the result of applying unique(...,'rows') to the input before the above lines will give a different result for 1 0.1 than using the above directly, as the latter would calculate the mean of 23, 23 and 42, while in the former case one 23 would be eliminates as duplicate before and the differing row with 42 would have a greater weight in the average.