Find multiple values within a Numpy array

Question

I am looking for a numpy function to find the indices at which certain values are found within a vector (xs). The values are given in another array (ys). The returned indices must follow the order of ys.

In code, I want to replace the list comprehension below by a numpy function.

>> import numpy as np
>> xs = np.asarray([45, 67, 32, 52, 94, 64, 21])
>> ys = np.asarray([67, 94])
>> ndx = np.asarray([np.nonzero(xs == y)[0][0] for y in ys]) # <---- This line
>> print(ndx)
[1 4]

Is there a fast way?

Thanks

Sven Marnach · Accepted Answer · 2012-03-05 12:36:43Z

22

For big arrays xs and ys, you would need to change the basic approach for this to become fast. If you are fine with sorting xs, then an easy option is to use numpy.searchsorted():

xs.sort()
ndx = numpy.searchsorted(xs, ys)

If it is important to keep the original order of xs, you can use this approach, too, but you need to remember the original indices:

orig_indices = xs.argsort()
ndx = orig_indices[numpy.searchsorted(xs[orig_indices], ys)]

edited Mar 5, 2012 at 12:36

answered Mar 5, 2012 at 12:31

Sven Marnach

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1 Comment

Picarus Over a year ago

if you don't need to keep track of what elements where found and which ones where not you can filter the output to get rid of all the indexes beyond limits: ndx = [ e for e in np.searchsorted(xs,ys) if e<len(xs) ]

Łukasz Kwieciński · Accepted Answer · 2020-12-17 15:11:32Z

4

In this kind of cases, just easily use np.isin() function to mask those elements conform your conditions, like this:

xs = np.asarray([45, 67, 32, 52, 94, 64, 21])
ys = np.asarray([67, 94])

mask=xs[np.isin(xs,xy)]
print(xs[mask])

edited Dec 17, 2020 at 15:11

Łukasz Kwieciński

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answered Nov 30, 2020 at 20:50

farshad madani

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1 Comment

Yano Over a year ago

This question does not answer the the question. It ends up printing the value of ys. The variable xy is not defined and should be ys.

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