HansSnah - 1 year ago 111

Python Question

I have spent the last hour trying to figure this out

Suppose we have

`import numpy as np`

a = np.random.rand(5, 20) - 0.5

amin_index = np.argmin(np.abs(a), axis=1)

print(amin_index)

> [ 0 12 5 18 1] # or something similar

this does not work:

`a[amin_index]`

So, in essence, I need to find the minima along a certain axis for the array np.abs(a), but then extract the values from the array a at these positions. How can I apply an index to just one axis?

Probably very simple, but I can't get it figured out. Also, I can't use any loops since I have to do this for arrays with several million entries.

thanks

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Answer Source

One way is to pass in the array of row indexes (e.g. `[0,1,2,3,4]`

) and the list of column indexes for the minimum in each corresponding row (your list `amin_index`

).

This returns an array containing the value at `[i, amin_index[i]]`

for each row `i`

:

```
>>> a[np.arange(a.shape[0]), amin_index]
array([-0.0069325 , 0.04268358, -0.00128002, -0.01185333, -0.00389487])
```

This is basic indexing (rather than advanced indexing), so the returned array is actually a view of `a`

rather than a new array in memory.