Numpy mask based on if a value is in some other list

Question

I have searched high and low and just can't find a way to do it. (It's possible I was searching for the wrong terms.)

I would like to create a mask (eg: [True False False True True]) based on whether each value is in some other list.

a = np.array([11, 12, 13, 14, 15, 16, 17])
mask = a in [14, 16, 8] #(this doesn't work at all!)
#I would like to see [False False False True False True False]

So far the best I can come up with is a list comprehension.

mask = [True if x in other_list else False for x in my_numpy_array]

Please let me know if you know of some secret sauce to do this with NumPy and fast (computationally), as this list in reality is huge.

NPE · Accepted Answer · 2012-11-29 15:29:29Z

33

Use numpy.in1d():

In [6]: np.in1d(a, [14, 16, 18])
Out[6]: array([False, False, False,  True, False,  True, False], dtype=bool)

answered Nov 29, 2012 at 15:29

NPE

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2 Comments

Joran Beasley Over a year ago

perfect I knew there had to be something :) I'll accept in 12 mins when it lets me ... thanks!

Brian D Feb 20 at 21:25

if using dictionary keys or values as the 'other list' make sure to cast them as a list or it doesn't work. i.e., np.in1d(a, list(dict.keys()))

sgt pepper · Accepted Answer · 2020-01-12 10:35:08Z

6

Accepted answer is right but currently numpy's docs recommend using isin function instead of in1d

answered Jan 12, 2020 at 10:35

sgt pepper

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