How to select specific row column pairs in numpy array which have specific value?

Question

I am trying to have a numpy array with random numbers from 0 to 1:

import numpy as np
x = np.random.random((3,3))

yields

[[ 0.11874238  0.71885484  0.33656161]
 [ 0.69432263  0.25234083  0.66118676]
 [ 0.77542651  0.71230397  0.76212491]]

And, from this array, I need the row,column combinations which have values bigger than 0.3. So the expected output should look like:

(0,1),(0,2),(1,0),(1,2),(2,0),(2,1),(2,2)

To be able to extract the item (the values of x[row][column]),and tried to write the output to a file. I tried the following command:

with open('newfile.txt', 'w') as fd:
    for row in x:
        for item in row:
            if item > 0.3:
                print(item)
                for row in item:
                    for col in item:
                        print(row,column,'\n')
                        fd.write(row,column,'\n')

However, it raises an error :

TypeError: 'numpy.float64' object is not iterable

Also, I searched but could not find how to start the numpy index from 1 instead of 0. For example, the expected output would look like this:

 (1,2),(1,3),(2,1),(2,3),(3,1),(3,2),(3,3)

Do you know how to get these outputs?

Divakar · Accepted Answer · 2017-06-08 11:47:25Z

3

Get the indices along first two axes that match that criteria with np.nonzero/np.where on the mask of comparisons and then simply index with integer array indexing -

r,c = np.nonzero(x>0.3)
out = x[r,c]

If you are looking to get those indices a list of tuples, zip those indices -

zip(r,c)

To get those starting from 1, add 1 and then zip -

zip(r+1,c+1)

On Python 3.x, you would need to wrap it with list() : list(zip(r,c)) and list(zip(r+1,c+1)).

Sample run -

In [9]: x
Out[9]: 
array([[ 0.11874238,  0.71885484,  0.33656161],
       [ 0.69432263,  0.25234083,  0.66118676],
       [ 0.77542651,  0.71230397,  0.76212491]])

In [10]: r,c = np.nonzero(x>0.3)

In [14]: zip(r,c)
Out[14]: [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1), (2, 2)]

In [18]: zip(r+1,c+1)
Out[18]: [(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2), (3, 3)]

In [13]: x[r,c]
Out[13]: 
array([ 0.71885484,  0.33656161,  0.69432263,  0.66118676,  0.77542651,
        0.71230397,  0.76212491])

Writing indices to file -

Use np.savetxt with int format, like so -

In [69]: np.savetxt("output.txt", np.argwhere(x>0.3), fmt="%d", comments='')

In [70]: !cat output.txt
0 1
0 2
1 0
1 2
2 0
2 1
2 2

With the 1 based indexing, add 1 to np.argwhere output -

In [71]: np.savetxt("output.txt", np.argwhere(x>0.3)+1, fmt="%d", comments='')

In [72]: !cat output.txt
1 2
1 3
2 1
2 3
3 1
3 2
3 3

edited Jun 8, 2017 at 11:47

answered Jun 8, 2017 at 10:52

Divakar

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3 Comments

bapors Over a year ago

Thank you for your answer. However, when I use zip(r,c), it makes a zip object at memory. How can I print them out to a file?

Divakar Over a year ago

@podedo I guess if you are on Python 3.x, you would need list(zip(r,c)). About printing to a file, I am not sure really.

Divakar Over a year ago

@podedo Added section on writing to text file. Check that out!

P. Camilleri · Accepted Answer · 2017-06-08 11:34:49Z

1

You could use np.where, which returns two arrays (when applied to a 2D array), with indices of rows (and corresponding columns) satisfy the condition you specifiy as an argument. Then you can zip these two arrays to get back a list of tuples:

list(zip(*np.where(x > 0.3)))

If you want to add 1 to every element of every tuple (use 1 based indexing), either loop over the tuples, either add 1 to each array returned by where:

res = np.where(x > 0.3)
res[0] += 1  # adds one to every element of res[0] thanks to broadcasting
res[1] += 1
list(zip(*res))

edited Jun 8, 2017 at 11:34

answered Jun 8, 2017 at 11:14

P. Camilleri

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1 Comment

bapors Over a year ago

It is very clean! Thank you. But how can add +1 to each tuple?

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