I have an array "removable" containing a few numbers from another array "All" containing all numbers from 0 to k.
I want to remove all numbers in A which are listed in removable.
All = np.arange(k)
removable = np.ndarray([1, 3, 4 , 7, 9, ..., 200])
for i in removable:
if i in All:
All.remove(i)
ndarray has no remove attribute, but I'm sure there is an easy method in numpy to solve this problem, but I can't find it in the documentation.
You could use the function setdiff1d from NumPy:
>>> a = np.array([1, 2, 3, 2, 4, 1])
>>> b = np.array([3, 4, 5, 6])
>>> np.setdiff1d(a, b)
array([1, 2])
a (not done by the pseudocode in the question), and the result will be sorted
setdiff1d is even faster than explicit set conversion and differencing. I guess that makes sense, probably more optimized. I didn't know numpy had this!
np.setdiff1d() will de-duplicate the original entries, and will also return the result sorted.
That's fine in some cases, but if you want to avoid one or both of these aspects, have a look at np.in1d() with an (inverted) boolean mask:
>>> a = np.array([1, 2, 3, 2, 4, 1])
>>> b = np.array([3, 4, 5, 6])
>>> a[~np.in1d(a, b)]
array([1, 2, 2, 1])
The ~ operator does inversion on the boolean mask:
>>> np.in1d(a, b)
array([False, False, True, False, True, False])
>>> ~np.in1d(a, b)
array([ True, True, False, True, False, True])
Disclaimer:
Note that this is not truly removal, as you indicated in your question; the result is a view into filtered elements of the original array a. Same goes for np.delete(); there's no concept of in-place element deletion for NumPy arrays.
Solution - fast for big arrays, no need to transform into list (slowing down computation)
orig=np.arange(15)
to_remove=np.array([1,2,3,4])
mask = np.isin(orig, to_remove)
orig=orig[np.invert(mask)]
>>> orig
array([ 0, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
np.isin() calls np.asarray() + np.in1d() and does reshaping. If both of the inputs are 1d, those checks are probably not needed
numpy arrays have a fixed shape, you cannot remove elements from them.
You cannot do this with ndarrays.
You should do this with sets instead of lists/arrays, which is easy enough:
remaining = np.array(set(arr).difference(removable))
where arr is your All array above ("all" is a keyword and should not be overwritten).
Granted, using sets will get rid of repeated elements if you have those in your arr, but it sounds like arr is just a sequence of unique values. Sets have much more efficient membership checking (constant-time vs. order N), so you get to go a lot faster. By comparison, I made a list version that builds a list if a value is not in removable:
def remove_list(arr, rem):
result = []
for i in arr:
if i not in rem:
result.append(i)
return result
and made my set version a function as well:
def remove_set(arr, rem):
return np.array(set(arr).difference(rem))
Timing comparison with arr = np.arange(10000) and removable = np.random.randint(0, 10000, 1000):
remove_list(arr, removable)
# 55.5 ms ± 664 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
remove_set(arr, removable)
# 947 µs ± 3.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
Set is 50 times faster.
