## What is the fastest way of extracting indexes of non "-1"s from an array?

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I have a numpy array with dimension 1500 x 3300. I want to fetch indexes of all values which have value greater than 0.40.

For example a sub-array:

```a = [0,0.5,0.4,-1,-1,0.9,0.3,-1,0.7]
```

Desired result: [0,1,5,8]

I have written the following code, but it takes a lot of time to run. It takes 20 minutes to run on an array of dimension 1500 x 3300.

```def non_zero(lst):
""" return indexes of items which are not -1 and value is greater than 0.40 """
return [i for i, e in enumerate(lst) if e > 0.40]
```

What can be the fastest alternative to do this?

Try the following directly in the 2D array:

```i, j = np.where(np.array(lst) > 0.4)
```

What's the fastest way to extract non-zero indices from a byte array , With a byte array that is mostly zero, being a sparse array, you can take advantage of a 32 bit CPU by doing comparisons 4 bytes at a time. According to @xiaotian-peiI answer, I think it would be even better simply to insert pairs (key, index) in a deterministically balanced binary search tree (avl or red-black) sorted by keys; that takes O(n lg n). Then you traverse the binary tree inorder extracting the indexes, what takes O(n). Finally you free the tree, what takes O(n).

You can use `np.argwhere` to get the indexes.

```import numpy as np
idx = np.argwhere(a != -1 & a > 0.4)
```

And of course, a!= -1 is not necessary..

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```import numpy as np
np.where(np.array(a) > 0.40).tolist()
```

values > 0.40 are ofcourse > -1

Also Iam assuming that "a" is a list of numbers (not a list of lists)

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