Sort numpy 2d array by multiple columns

Question

I have a 2D numpy array that looks like this

array([[5, 0],
       [3, 1],
       [7, 0],
       [2, 1]])

I'd like to (sub) sort by each column (say right to left) to get this:

array([[5, 0],
       [7, 0],
       [2, 1],
       [3, 1]])

How can I do that in numpy?

Did you try to change the column index like array[np.argsort(array[:, 0])]? — Ugurcan
– Ugurcan, Commented Feb 15, 2021 at 19:16
Does this answer your question? Sorting arrays in NumPy by column — Ugurcan
– Ugurcan, Commented Feb 15, 2021 at 19:20

anon01 · Accepted Answer · 2021-02-19 18:28:44Z

3

Numpy includes a native function for sub-sorting by columns, lexsort:

idx = np.lexsort((arr[:,0], arr[:,1]))
arr_sorted = arr[idx]

Alternatively, you can use pandas syntax if you're more familiar; this will have some memory/time overhead but should be small for < 1m rows:

arr = [
    [5,  0],
    [3,  1],
    [7,  0],
    [2,  1]
]
df = pd.DataFrame(data=arr).sort_values([1,0])
arr_sorted = df.to_numpy()

output (both):

array([[5, 0],
       [7, 0],
       [2, 1],
       [3, 1]])

answered Feb 15, 2021 at 19:42

anon01

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3 Comments

Ah yes, but it is a very large array unfortunately

@FlorisFancypants how large is very large? This should be acceptable up to a few million rows anyway

oh, its like 10000, but the lexsort works too, thanks <3

s3dev · Accepted Answer · 2021-02-15 21:26:18Z

0

You can use np.lexsort to sort an array on multiple columns:

idx = np.lexsort((a[:,0], a[:,1]))

a[idx]

Output:

array([[5, 0], 
       [7, 0],
       [2, 1],
       [3, 1]])

answered Feb 15, 2021 at 19:22

s3dev

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