Delete some elements from numpy array

Keep in mind that np.delete(arr, ind) deletes the element at index ind NOT the one with that value.

This means that as you delete things, the array is getting shorter. So you start with

values = [0,1,2,3,4,5]
np.delete(values, 3) 
[0,1,2,4,5]  #deleted element 3 so now only 5 elements in the list
#tries to delete the element at the fifth index but the array indices only go from 0-4
np.delete(values, 5) 

One of the ways you can solve the problem is to sort the indices that you want to delete in descending order (if you really want to delete the array).

inds_to_delete = sorted([3,1,5], reverse=True) # [5,3,1]
# then delete in order of largest to smallest ind

Or:

inds_to_keep = np.array([0,2,4])
values = values[inds_to_keep]

A probably faster way (because you don't need to delete every single value but all at once) is using a boolean mask:

values = np.array([0,1,2,3,4,5])
tobedeleted = np.array([False, True, False, True, False, True])
# So index 3, 5 and 1 are True so they will be deleted.
values_deleted = values[~tobedeleted]
#that just gives you what you want.

It is recommended on the numpy reference on np.delete

To your question: You delete one element so the array get's shorter and index 5 is no longer in the array because the former index 5 has now index 4. Delete in descending order if you want to use np.delete.

If you really want to delete with np.delete use the shorthand:

np.delete(values, [3,5,1])

If you want to delete where the values are (not the index) you have to alter the procedure a bit. If you want to delete all values 5 in your array you can use:

values[values != 5]

or with multiple values to delete:

to_delete = (values == 5) | (values == 3)  | (values == 1)
values[~to_delete]

all of these give you the desired result, not sure how your data really looks like so I can't say for sure which will be the most appropriate.

The problem is that you have deleted items from values so when you are trying to delete item in index 5 there is no longer value at that index, it's now at index 4.

If you sort the list of indices to delete, and iterate over them from large to small that should workaround this issue.

import numpy as np

values = np.array([0,1,2,3,4,5])
print values
for i in [5,3,1]:  # iterate in order
    values = np.delete(values,i)
print values