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I have some very long code, which I know can done more efficiently using a for loop. For context, the "matrix" is my dataset and I need to extract all the values where the second column is equal to 1,2,3...20, and sum the last four columns of those (resulting in a 20x4 matrice with the summed values)

But I need to write it using a loop, I would guess a for-loop.

I've tried the following:

M=np.zeros([20,10]) #creating empty matrix to fill in
for i in range(1,21):
    M=matrix[matrix[:,1]==i]
    sub=sum(M[:,6:10])

But the result is only the last run of the loop, that is the values where stackD[stackD[:,1]==20]. How can I do this with a for-loop? Thanks in advance.

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  • To generalize this question, is the second column the range of numbers from 1 ... to N for arbitrary positive nonzero integer N? Or is N potentially a value larger than 12? Commented Jan 15, 2018 at 20:03

2 Answers 2

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Your problem is that you repeatedly overwrite M at each loop iteration (M=...).

Here's a correct solution that uses a loop:

M = np.stack([stackD[stackD[:,1] == i+1, 6:10].sum(axis=0) 
              for i in range(12)])
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In your new code, M is a single variable and is repeatedly given a new value in the for loop. If you want to store the results of each iteration, you need to create a list. Note that when you name variables with numbers, this almost always means that you should use a list instead. For example, sub1, sub2, sub3, etc. can be replaced with a list:

sub = []
dat = []
for i in range(1,13):
    sub.append(stackD[stackD[:,1]==i])
    dat.append(sum(sub[i]))

Now you can access values with indexes like sub[5] and dat[12]. If sub is never used outside of this loop, then you only need the single list dat:

dat = []
for i in range(1,13):
    sub = stackD[stackD[:,1]==i]
    dat.append(sum(sub))

Disclaimer:

I am not familiar with numpy and there is likely a better way to do what you want with its tools. I am only explaining how to use a basic list with your for loop. I strongly encourage you to learn more about lists and loops because these are very important tools when writing Python programs.

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