Is there any way to delete the specific elements o

问题:

when calling the "np.delete()", I am not interested to define a new variable for the reduced size array. I want to execute the delete on the original numpy array. Any thought?

>>> arr = np.array([[1,2], [5,6], [9,10]])
>>> arr
array([[ 1,  2],
       [ 5,  6],
       [ 9, 10]])
>>> np.delete(arr, 1, 0)
array([[ 1,  2],
       [ 9, 10]])
>>> arr
array([[ 1,  2],
       [ 5,  6],
       [ 9, 10]])
but I want:
>>> arr
array([[ 1,  2],
       [ 9, 10]])

回答1:

NumPy arrays are fixed-size, so there can't be an in-place version of np.delete. Any such function would have to change the array's size.

The closest you can get is reassigning the arr variable:

arr = numpy.delete(arr, 1, 0)

回答2:

The delete call doesn't modify the original array, it copies it and returns the copy after the deletion is done.

>>> arr1 = np.array([[1,2], [5,6], [9,10]])
>>> arr2 = np.delete(arr, 1, 0)
>>> arr1
array([[ 1,  2],
   [ 5,  6],
   [ 9, 10]])
>>> arr2 
array([[ 1,  2],
   [ 9, 10]])

回答3:

If its a matter of performance you might want to try (but test it since I'm not sure) creating a view* instead of of using np.delete. You can do it by slicing which should be an inplace operation:

import numpy as np

arr = np.array([[1,  2], [5,  6], [9, 10]])
arr = arr[(0, 2), :]
print(arr)

resulting in:

[[ 1  2]
 [ 9 10]]

This, however, will not free the memory occupied from the excluded row. It might increase performance but memory wise you might have the same or worse problem. Also notice that, as far as I know, there is no way of indexing by exclusion (for instance arr[~1] would be very useful) which will necessarily make you spend resources in building an indexation array.

For most cases I think the suggestion other users have given, namely:

arr = numpy.delete(arr, 1, 0)

, is the best. In some cases it might be worth exploring the other alternative.

EDIT: *This is actually incorrect (thanks @user2357112). Fancy indexing does not create a view but instead returns a copy as can be seen in the documentation (which I should have checked before jumping to conclusions, sorry about that):

Advanced indexing always returns a copy of the data (contrast with basic slicing that returns a view).

As so I'm unsure if the fancy indexing suggestion might be worth something as an actual suggestion unless it has any performance gain against the np.delete method (which I'll try to verify when opportunity arises, see EDIT2).

EDIT2: I performed a very simple test to see if there is any perfomance gain from using fancy indexing by opposition to delete function. Used timeit (actually the first time I've used but it seems the number of executions per snippet is 1 000 000, thus the hight numbers for time):

import numpy as np
import timeit

def test1():
    arr = np.array([[1, 2], [5, 6], [9, 10]])
    arr = arr[(0, 2), :]

def test2():
    arr = np.array([[1, 2], [5, 6], [9, 10]])
    arr = np.delete(arr, 1, 0)

print("Equality test: ", test1() == test2())

print(timeit.timeit("test1()", setup="from __main__ import test1"))
print(timeit.timeit("test2()", setup="from __main__ import test2"))

The results are these:

Equality test:  True
5.43569152576767
9.476918448174644

Which represents a very considerable speed gain. Nevertheless notice that building the sequence for the fancy indexing will take time. If it is worth or not will surely depend on the problem being solved.

回答4:

You could implement your own version of delete which copies data elements after the elements to be deleted forward, and then returns a view excluding the (now obsolete) last element:

import numpy as np


# in-place delete
def np_delete(arr, obj, axis=None):
    # this is a only simplified example
    assert (isinstance(obj, int))
    assert (axis is None)

    for i in range(obj + 1, arr.size):
        arr[i - 1] = arr[i]
    return arr[:-1]


Test = 10 * np.arange(10)
print(Test)

deleteIndex = 5
print(np.delete(Test, deleteIndex))
print(np_delete(Test, deleteIndex))

回答5:

Nothing wrong in your code. you just have to override the variable

    arr = np.array([[1,2], [5,6], [9,10]])
    arr = np.delete(arr, 1, 0)