Indexing with List of Indices to Exclude

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This is similar to some other questions (Explicitly select items from a Python list or tuple, Grabbing specific indices of a list in Python), but I'm looking to do the opposite:

What is a clean way to specify a list/tuple of indices to exclude, instead of to select? I'm thinking of something similar to R or MATLAB where you can specify indices to exclude, like:

vector1 <- c('a', 'b', 'c', 'd')
vector2 <- vector1[-1] # ['b', 'c', 'd']
vector3 <- vector1[c(-1, -2)] # ['c', 'd']

Is there a good way to accomplish the same thing in Python? Apologizes if this is a dupe, I wasn't sure exactly what to search for.

Index all *except* one item in python, For a list, you could use a list comp. For example, to make b a copy of a without the 3rd element: a = range(10)[::-1] # [9, 8, 7, 6, 5, 4, 3, 2, 1, 0] b = [x for i,x in� Use enumerate () and exclude any indices you want removed: [elem for i, elem in enumerate(inputlist) if i not in excluded_indices] For performance, it'd be fastest if excluded_indices was a set. share. Share a link to this answer. Copy link.

import numpy
target_list = numpy.array(['1','b','c','d','e','f','g','h','i','j'])
to_exclude = [1,4,5]
print target_list[~numpy.in1d(range(len(target_list)),to_exclude)]

because numpy is fun

Efficiently index rows of numpy array by exclusion, Another is to do the equivalent with index values ind = range(n) + range(n+1:A. shape[0]] # using list concatenate A1 = A[ind,:] And as you note,� Open the mentioned Searching Windows page in Settings. On the right, go to the section Excluded Folders. Click on the folder you want to remove. Click on the Remove excluded folder button. Finally, you can use the classic Indexing Options dialog to manage folders excluded from the search index.

Use enumerate() and exclude any indices you want removed:

[elem for i, elem in enumerate(inputlist) if i not in excluded_indices]

For performance, it'd be fastest if excluded_indices was a set.

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Use np.delete

In [38]: a
Out[38]: array([ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13])

In [39]: b
Out[39]: [3, 4, 5, 9]

In [40]: a[b]
Out[40]: array([ 7,  8,  9, 13])

In [41]: np.delete(a, b)
Out[41]: array([ 4,  5,  6, 10, 11, 12])

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numpy.delete(original_list,index_of_the_excluded_elements)

note that in python, index starts from 0, so for the example in the question, the codes should be:

import numpy as np
vector1=['a', 'b', 'c', 'd']
vector2 =np.delete(vector1,[0]) # ['b', 'c', 'd']
vector3 =np.delete(vector1,[0,1]) # ['c', 'd']

Indexing — NumPy v1.20.dev0 Manual, As in Python, all indices are zero-based: for the i-th index n_i instead of a copy as in the case of builtin Python sequences such as string, tuple and list. basic indexing (excluding integers) and the subspace from the advanced indexing part. Question about Search Indexing locations and exclusions. I just installed Build 10130 and I'm having some trouble with some apps (my Steam game shortcuts) not showing up in search results, even though they appear in the All Apps section of the start menu.

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Numpy exclude index, List has to maintain 4 parameter for each element i. with parameters to exclude some Iterating through an numpy array and index to a value in another Missing� Browse and Explore Stock Indexing Right Now at Helpful.Tips. Visit Our Results Now. Search Stock Indexing. Relevant Results on Helpful.Tips. Check Us Out!

Comments
  • Aha, of course. Thanks for the detailed explanation (I'll accept it when SO lets me).
  • Plus, if you're translating MATLAB code to Python, you probably should be looking at numpy rather than native lists and loops…
  • set won't actually be faster than list until there are more than a few elements (from a previous question, the cutoff is anywhere between 3 and 12 with strings, depending on your implementation). But conceptually it makes more sense anyway.
  • @abarnert: Doesn't that depend on the number of elements in the input list as well? And for this filter, it could make a difference if excluded_indices is sorted or randomized as well; I am a little skeptical that the cutoff is every anywhere near 12; is the fixed cost of the set lookup (hash calculation and lookup, mainly) really that high?
  • From what I vaguely remember, with very large unicode objects in Python 2.7, I found a case with a cutoff between 6 and 7… but someone else found a case that was almost twice as high, possibly in a different Python implementation. Of course notice the "with strings"; hashing ints is a lot faster, even huge ints, so I'd expect it to be around 2-3 at worst… And I'm not sure how sorting would make a difference (unless you want a third implementation using bisect or a tree or something).
  • @abarnert: Hrm, you are right, sorting doesn't make a difference, the total cost of all the searches is going to be the same no matter what the order.
  • And also, how would the number of input elements make a difference? It's going to be linear on those, except in a few edge cases (e.g., if you have lots of references to a small number of distinct slow-to-hash builtin objects, the most important factor could be the number of unique elements.)