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# Sorting value of one array according to another in Python

schedule Aug 11, 2023
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To sort the value of one array according to another in Python use the `np.argsort(~)` method.

# Example

Consider the following NumPy arrays:

``` import numpy as npx = np.array(['A','B','C','D'])y = np.array([4,3,2,1]) ```

## Ascending order

To sort array `y` in ascending order and also sort array `x` maintaining the pair relationships between elements in the two arrays:

``` sort = np.argsort(y)print(y[sort])print(x[sort]) [1 2 3 4]['D' 'C' 'B' 'A'] ```

First we get the integer indices that would result in array `y` being sorted in ascending order, and store them to variable `sort`. If we actually printed this variable we would see an array `[3 2 1 0]`. This means that to obtain an ascending sorted copy of array `y`, the element at index position 3 should come first, element at index position 2 should come second and so on.

We then take `sort` and apply it to both `y` and `x`. For both arrays we see the printed arrays have the element that was at index position 3 (`1` and `'D'` respectively) appear first, then have element that was at index position 2 (`2` and `'C'` respectively) appear next.

## Descending order

To sort array `x` in descending order and also sort array `y` maintaining the pair relationships between elements in the two arrays:

``` sort = np.argsort(x)print(x[~sort])print(y[~sort]) ['D' 'C' 'B' 'A'][1 2 3 4] ```

Although `np.argsort(~)` does not natively support descending order, you can leverage negation (`~`) to implement the functionality. By negating the `sort` array here, the index position of the highest elements will now come first and lowest elements last.

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