Using multiple filter on multiple columns of numpy array - more efficient way?

Question

I have the following 2 arrays:

arr = np.array([[1, 2, 3, 4],
                [5, 6, 7, 8], 
                [7, 5, 6, 3],
                [2, 4, 8, 9]]

 ids = np.array([6, 5, 7, 8])

Each row in the array arr describes a 4-digit id, there are no redundant ids - neither in their values nor their combination. So if [1, 2, 3, 4] exists, no other combination of these 4 digits can exist. This will be important in a sec.

The array ids contains a 4-digit id, however the order might not be correct. Now I need to go through each row of arr and look if this id exists. In this example ids fits to the 2nd row from the top of arr. So arr[1,:].

My current solution creates a filter of each column to check if the values of ids exist in any of the 4 columns. After that I use these filters on arr. This seems way too complicated.

So I pretty much do this:

 filter_1 = np.in1d(arr[:, 0], ids)
 filter_2 = np.in1d(arr[:, 1], ids)
 filter_3 = np.in1d(arr[:, 2], ids)
 filter_4 = np.in1d(arr[:, 3], ids)

 result = arr[filter_1 & filter_2 & filter_3 & filter_4]

Does anyone know a simpler solution? Maybe using generators?

Divakar · Accepted Answer · 2020-11-02 17:20:40Z

1

Use np.isin all across arr and all-reduce to get result -

In [15]: arr[np.isin(arr, ids).all(1)]
Out[15]: array([[5, 6, 7, 8]])

answered Nov 2, 2020 at 17:20

Divakar

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1 Comment

zeniapy Over a year ago

Cheers, this is way more elegant.

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