Numpy index columns

Question

I have a 2d matrix D

array([[1, 1, 8, 8],
       [3, 3, 4, 4],
       [8, 8, 1, 1]])

I would like to find each column's lowest value by indexing it with the amin array:

amin = D.argmin(0)
amin

array([0, 0, 2, 2])

How do I properly index D with amin such that the returned array is array([1, 1, 1, 1])?

@SembeiNorimaki sorry, the initial question was posed wrong, The vector in question would be [1, 1, 1, 1] — John Titor
– John Titor, Commented Apr 5, 2022 at 9:35
you don't need to do argmin if you want the values. argmin is if you want the indexes. Assuming your array is stored in the variable myArray just do np.min(myArray, axis=0) (where np comes from doing import numpy as np) — user2261062
– user2261062, Commented Apr 5, 2022 at 9:35
This works, but I would like to see the indexing syntax to it using amin — John Titor
– John Titor, Commented Apr 5, 2022 at 9:36

Daniel F · Accepted Answer · 2022-04-05 09:45:30Z

1

You need a combination of expand_dims and take_along_axis

np.take_along_axis(D, np.expand_dims(amin, 0), 0)
Out[]: array([[1, 1, 1, 1]])

This is arguably not the most intuitive formulation, but without going into design philosophy of numpy, I can say it's as "pythonic" as possible in this case.

answered Apr 5, 2022 at 9:45

Daniel F

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John Titor Over a year ago

Thanks! I was hoping for something more intuitive indeed. I always find myself struggling way too long with such problems. One is wondering why things like D[:, amin[None,:]] do not work ...

hpaulj · Accepted Answer · 2022-04-05 15:51:31Z

In [99]: D=np.array([[1, 1, 8, 8],
    ...:        [3, 3, 4, 4],
    ...:        [8, 8, 1, 1]])
In [100]: amin = D.argmin(0)
In [101]: amin
Out[101]: array([0, 0, 2, 2])

What is amin? It has one value per column, but that value is a row index. So what you want is:

In [102]: D[amin[0], 0]
Out[102]: 1
In [103]: D[amin[2], 2]
Out[103]: 1

Or doing all columns at once:

In [104]: D[amin, [0,1,2,3]]
Out[104]: array([1, 1, 1, 1])

Expressions like D[:, amin] try to apply amin to indexing the columns, not the rows. And the slice selects all rows at once. It doesn't pair them up.

May be these versions will make the action clearer:

In [105]: D[[0,0,2,2], [0,1,2,3]]
Out[105]: array([1, 1, 1, 1])
In [106]: [D[i,j] for i,j in zip([0,0,2,2],[0,1,2,3])]
Out[106]: [1, 1, 1, 1]

take_along_axis does the same thing as [104]

In [107]: np.take_along_axis(D, amin[None,:],0)
Out[107]: array([[1, 1, 1, 1]])

It's a bit cleaner if we tell argmin to keepdims (a new feature in v 1.22)

In [108]: amin = D.argmin(0, keepdims=True)
In [109]: amin
Out[109]: array([[0, 0, 2, 2]])
In [110]: np.take_along_axis(D, amin,0)
Out[110]: array([[1, 1, 1, 1]])

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