Numpy.where() with an array in its conditional

Question

I don't know how to describe this well so I'll just show it.

How do I do this...

for iy in random_y:
    print(x[np.where(y == iy)], iy)

  X        y
[ 0.5] :   0.247403959255
[ 2.]  :   0.841470984808
[ 49.5]:  -0.373464754784

without for loops and I get a solution as a single array like when you use np.where() or array[cond]. Since you know, this is Python B)

NOTE: The reason why I want to do this is because I have a random subset of the Y values and I want to find the corresponding X values.

Are the values in y unique?

unutbu
– unutbu

2016-05-10 17:09:37 +00:00
Commented May 10, 2016 at 17:09 — unutbu
– unutbu, Commented May 10, 2016 at 17:09
yes each of the y values are unique

SARose
– SARose

2016-05-10 17:10:33 +00:00
Commented May 10, 2016 at 17:10 — SARose
– SARose, Commented May 10, 2016 at 17:10

Divakar · Accepted Answer · 2016-05-10 17:16:56Z

2

If you are looking for exact matches, you can simply use np.in1d as this is a perfect scenario for its usage, like so -

first_output = x[np.in1d(y,random_y)]
second_output = random_y[np.in1d(random_y,y)

If you are dealing with floating-point numbers, you might want to use some tolerance factor into the comparisons. So, for such cases, you can use NumPy broadcasting and then use np.where, like so -

tol = 1e-5 # Edit this to change tolerance
R,C = np.where(np.abs(random_y[:,None] - y)<=tol)

first_output = x[C]
second_output = random_y[R]

edited May 10, 2016 at 17:16

answered May 10, 2016 at 17:14

Divakar

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5 Comments

SARose Over a year ago

Looks great, testing this now. Does this or a similar method generalize to 2d arrays? (PS: I wish I could double thumb up thing!)

Divakar Over a year ago

@SARose So, for 2D arrays, you need to decide if by "==" do you mean all elements in a row or any one element in a row, assuming you would like a row based comparison. If you are really looking to extend this to 2D, it might be a good idea to post a sample case in the question or post a new question altogether, because it won't be a straight forward extension.

SARose Over a year ago

Okay I'll post a sample case. But yeah I do want all the elements in one row from one array to match all the elements in one row from another array.

Divakar Over a year ago

@SARose So, a quick way with broadcasting for 2D case, would be : R,C = np.where((random_y[:,None] == y).all(2)). Then x[C] and random_y[R] would be the outputs just as listed for 1D solution. Not a great deal of change there! :)

Divakar Over a year ago

@SARose Likewise for tolerance, you can use R,C = np.where((np.abs(random_y[:,None] - y) <= tol).all(2)) keeping rest the same.

David Perez · Accepted Answer · 2016-05-10 17:06:53Z

0

Maybe this could do the trick(not tested):

print(Str(x[np.where(y == iy)]) + " " + Str(iy) + "\n") for iy in random_y

answered May 10, 2016 at 17:06

David Perez

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