Assign scalar value to all elements of numpy array which are in another array

Question

For example, I have a numpy array a = np.arange(10), and I need to assign the values in ignore = [2,3,4] to be number 255, like this:

a = np.arange(10)
ignore_value = [3,4,5]
a[a in ignore_value] = 255 # what is the correct way to implement this?

The last line in the program above cannot be accepted by Python3.5 but it shows what I want to do.

Edit:

I found a solution, but it's not vectorized.

for el in ignore_value:
    a[a == el] = 255

This looks really ugly and is very slow since there is a for loop here, so do I have a better way ?

Just to be clear, you want to select elements by value, not by index? The desired result is [0,1,2,255,255,255,6,7,8,9]? — hpaulj
– hpaulj, Commented Nov 30, 2018 at 6:06

hpaulj · Accepted Answer · 2018-11-30 06:08:49Z

1

In [500]: a = np.arange(10)
In [501]: ignore_value = [3,4,5]
In [502]: np.isin(a, ignore_value)
Out[502]: 
array([False, False, False,  True,  True,  True, False, False, False,
       False])
In [503]: a[np.isin(a, ignore_value)]=255
In [504]: a
Out[504]: array([  0,   1,   2, 255, 255, 255,   6,   7,   8,   9])

You could also construct the mask with:

In [506]: a[:,None]==ignore_value
Out[506]: 
array([[False, False, False],
       [False, False, False],
       [False, False, False],
       [ True, False, False],
       [False,  True, False],
       [False, False,  True],
       [False, False, False],
       [False, False, False],
       [False, False, False],
       [False, False, False]])
In [507]: (a[:,None]==ignore_value).any(axis=1)
Out[507]: 
array([False, False, False,  True,  True,  True, False, False, False,
       False])

answered Nov 30, 2018 at 6:08

hpaulj

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Comments

timgeb · Accepted Answer · 2018-11-30 06:15:09Z

1

You can use numpy.isin with boolean indexing.

>>> a = np.arange(10)
>>> ignore_value = [3,4,5]
>>> a[np.isin(a, ignore_value)] = 255
>>> a
array([  0,   1,   2, 255, 255, 255,   6,   7,   8,   9])

... or with numpy.where:

>>> a = np.arange(10)
>>> a = np.where(np.isin(a, ignore_value), 255, a)
>>> a
array([  0,   1,   2, 255, 255, 255,   6,   7,   8,   9])

In both cases, np.isin(a, ignore_value) will give you a boolean array indicating where a has a value occuring in ignore_value.

>>> np.isin(a, ignore_value)
array([False, False, False,  True,  True,  True, False, False, False, False])

edited Nov 30, 2018 at 6:15

answered Nov 30, 2018 at 6:09

timgeb

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