R style Negative Indexing in Python. Take NOT IN Slices

R style Negative Indexing in Python. Take NOT IN Slices

My R programming head is wired to use negative slicing indexes to exclude
elements.
As an example:
my_list = [0,1,2,3,4,5,6,7,8,9]
my_neg_slice = [-2, -8, 0, -5]
Would return
[1 3 4 6 7 9]
i.e. return all indexes not in (0, 2, 5, 8).
This is more of a question to satisfy my curiosity as the Pythonic
negative indexing is quite novel to me (this is not a critique of Python's
implementation as I quite like it). Has anyone implemented a
R_Style_Negative_Indexing in Python? I am very new to Python so this type
of indexing may already exist? Maybe someone has created a custom
extension (sorry if this is not the correct terminology) to extend an
appropriate library?
Obviously, this would be very tricky to implement for strings, but I am
hoping people can see the concept of wanting to slice into an object
(List, Dict, DataFrame, ...) by excluding a set of known sparse elements?
My Neanderthal way to perform the negative R style indexing in Python:
import numpy as np
my_list = [0,1,2,3,4,5,6,7,8,9]
my_neg_slice = [-2, -8, 0, -5]
# Convert my_list to a numpy array as it's much easier to work with
step0 = np.array(my_list)
# Same for the negative_slices
step1 = np.array(my_neg_slice)
# Identify the indexes that are negative (assume 0 implies exclude)
step2 = step1 <= 0
# For the negative indexes, flip the sign so they are positive
step3 = -step1[step2]
# Generate the complete index for my_list
step4 = np.arange(len(my_list))
# Identify the indices required by exlucing my_neg_slice indices
step5 = np.setdiff1d(step4, step3)
# Make a cup of tea! Maybe time to rewire the brain and think like a snake!
step6 = step0[step5]
print(step6)
[1 3 4 6 7 9]
I dont have a particular problem that I am trying to crack, I am simply
looking to build my understanding of the possibilities with indexing? Many
thanks in advance. Bertie.