lizaveta - 1 year ago 119

Python Question

I have a 2 dimensional NumPy ndarray.

`array([[ 0., 20., -2.],`

[ 2., 1., 0.],

[ 4., 3., 20.]])

How can I get all indices of the maximum elements? So I would like as output array([0,1],[2,2]).

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

Use `np.argwhere`

on *max-equality mask* -

```
np.argwhere(a == a.max())
```

Sample run -

```
In [552]: a # Input array
Out[552]:
array([[ 0., 20., -2.],
[ 2., 1., 0.],
[ 4., 3., 20.]])
In [553]: a == a.max() # Max equality mask
Out[553]:
array([[False, True, False],
[False, False, False],
[False, False, True]], dtype=bool)
In [554]: np.argwhere(a == a.max()) # array of row, col indices of max-mask
Out[554]:
array([[0, 1],
[2, 2]])
```

If you are working with floating point numbers, you might want to use some tolerance there. So, with that consideration, you could use `np.isclose`

that has some default absolute and relative tolerance values. This would replace the earlier `a == a.max()`

part, like so -

```
In [555]: np.isclose(a, a.max())
Out[555]:
array([[False, True, False],
[False, False, False],
[False, False, True]], dtype=bool)
```

Recommended from our users: **Dynamic Network Monitoring from WhatsUp Gold from IPSwitch**. ** Free Download**