Gabriel - 10 months ago 41

Python Question

Consider a

`numpy`

`> a = np.random.uniform(0., 100., (10, 1000))`

and a list of indexes to elements in that array that I want to keep track of:

`> idx_s = [0, 5, 7, 9, 12, 17, 19, 32, 33, 35, 36, 39, 40, 41, 42, 45, 47, 51, 53, 57, 59, 60, 61, 62, 63, 65, 66, 70, 71, 73, 75, 81, 83, 85, 87, 88, 89, 90, 91, 93, 94, 96, 98, 100, 106, 107, 108, 118, 119, 121, 124, 126, 127, 128, 129, 133, 135, 138, 142, 143, 144, 146, 147, 150]`

I also have a list of indexes of elements I need to remove from

`a`

`> idx_d = [4, 12, 18, 20, 21, 22, 26, 28, 29, 31, 37, 43, 48, 54, 58, 74, 80, 86, 99, 109, 110, 113, 117, 134, 139, 140, 141, 148, 154, 156, 160, 166, 169, 175, 183, 194, 198, 199, 219, 220, 237, 239, 241, 250]`

which I delete with:

`> a_d = np.delete(arr, idx_d, axis=1)`

But this process alters the indexes of elements in

`a_d`

`idx_s`

`a_d`

`a`

`np.delete()`

`4`

`a`

`4`

`idx_s`

`a_d`

`v Index 5 points to 'f' in a`

0 1 2 3 4 5 6

a -> a b c d e f g ... # Remove 4th element 'e' from a

a_d -> a b c d f g h ... # Now index 5 no longer points to 'f' in a_d, but to 'g'

0 1 2 3 4 5 6

How do I update the

`idx_s`

`a`

`a_d`

In the case of an element that is present in

`idx_s`

`idx_d`

`a`

`a_d`

Answer Source

You could use `np.searchsorted`

to get the shifts for each element in `idx_s`

and then simply subtract those from `idx_s`

for the new *shifted-down* values, like so -

```
idx_s - np.searchsorted(idx_d, idx_s)
```

If `idx_d`

is not already sorted, we need to feed in a sorted version. Thus, for simplicity assuming these as arrays, we would have -

```
idx_s = idx_s[~np.in1d(idx_s, idx_d)]
out = idx_s - np.searchsorted(np.sort(idx_d), idx_s)
```

A sample run to help out getting a better picture -

```
In [530]: idx_s
Out[530]: array([19, 5, 17, 9, 12, 7, 0])
In [531]: idx_d
Out[531]: array([12, 4, 18])
In [532]: idx_s = idx_s[~np.in1d(idx_s, idx_d)] # Remove matching ones
In [533]: idx_s
Out[533]: array([19, 5, 17, 9, 7, 0])
In [534]: idx_s - np.searchsorted(np.sort(idx_d), idx_s) # Updated idx_s
Out[534]: array([16, 4, 15, 8, 6, 0])
```