2

I have a numpy array a like this

In [318]: a
Out[318]:
array([[0. , 1. , 2. , 3. ],
       [0.5, 0.3, 0.2, 0.25]])

I need to sort along the second row (the one with [0.5,0.3,0.2,0.25]), while having the first row changed accordingly. In this case, the expected result is

2    3     1.   0
0.2, 0.25, 0.3, 0.5

How can I do this? Thank you. I tried np.sort with axis=-1 and 0; they are not what I need.

Important note: The performance is the key in my problem solving. My arrays, from an image processing application, are generally of N columns with an N close to 4 million.

Improve this question
0

1 Answer 1

Reset to default
2

Use np.argsort() to get the index of sorted row and then use it as a mask to sort the whole array (column based):

In [69]: mask = np.argsort(a[1])                                                                                                                                                                            

In [70]: a[:, mask]                                                                                                                                                                                         
Out[70]: 
array([[2.  , 3.  , 1.  , 0.  ],
       [0.2 , 0.25, 0.3 , 0.5 ]])
Improve this answer
Sign up to request clarification or add additional context in comments.

Comments

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.