I want to get minimun number's index for a row excluding zeros

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Consider the following code that generates the following dst matrix.

tmp = pd.DataFrame()
tmp['a'] = np.random.randint(1, 10, 5)
tmp['b'] = np.random.randint(1, 10, 5)
dst = pairwise_distances(tmp, tmp, metric='l2')
dst

which looks like the following

array([[0. , 5.38516481, 5. , 4.12310563, 2. ],
[5.38516481, 0. , 1.41421356, 3.16227766, 5. ],
[5. , 1.41421356, 0. , 4. , 4.12310563],
[4.12310563, 3.16227766, 4. , 0. , 5. ],
[2. , 5. , 4.12310563, 5. , 0. ]])

Now, I want to somehow get 4 as an output column, because for row=0 and col=4 lies the minimum distance of row0 to another row apart from itself. I'm trying to use the following code to do the job! but np.nonzeros() is messing up the game. np.argmin(dst[0, np.nonzero(dst[0,:])]) I'm getting 3 as an output, where I should be getting 4. I understand that np.nonzero() return another set of dimensions [1,2,3,4] of which argmin picks 3rd column which is actual 4th column of the dst matrix. Need help! Thanks in advance!!

scikit-learn pandas numpy python
1 Answers

Instead of argmin, use np.min and compare the result to dst[0,:]. Finally, pass it to np.flatnonzero or np.nonzero

np.flatnonzero(np.min(dst[0,np.nonzero(dst[0,:])]) == dst[0,:])

Out[150]: array([4], dtype=int64)

Or

np.nonzero(np.min(dst[0,np.nonzero(dst[0,:])]) == dst[0,:])[0]

Out[151]: array([4], dtype=int64)

If you want to return an integer index, you may use np.argmax at the last step

np.argmax(np.min(dst[0,np.nonzero(dst[0,:])]) == dst[0,:])

Out[157]: 4
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