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Parameters Return value Examples Basic usage Using a mask Two-dimensional arrays

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NumPy | argwhere method

schedule Aug 12, 2023

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Parameters Return value Examples Basic usage Using a mask Two-dimensional arrays

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NumPy's argwhere(~) method returns the indices of non-zero values.

Parameters

1. a | array_like

The input array.

Return value

A NumPy array of indices of non-zero values. If the input array is 1D or 2D, then the returned array will be 2D.

Examples

Basic usage


        
        
            
                
                
                    x = np.array([1,2,3])
np.argwhere(x)
                
            
            array([[0],
       [1],
       [2]])

Here, since all values in x are non-zero, all indices are returned. Notice the returned array is 2D.

Using a mask

In NumPy, False values are equivalent to 0, while True values are equivalent to 1. This means that the argwhere(~) can be used for masks.

Suppose we wanted to determine the indices of values larger than 2. We can do so like follows:


        
        
            
                
                
                    x = np.array([1,2,3,4])
mask = x > 2
                
            
            array([False, False,  True,  True])

Applying the mask gives:


        
        
            
                
                
                    np.argwhere(mask)
                
            
            array([[2],
       [3]])

Two-dimensional arrays

Suppose we wanted to determine the indices of values larger than 2 in a 2D Numpy array


        
        
            
                
                
                    x = np.array([[1,2],[3,4],[5,6]])
mask = x > 2
                
            
            array([[False, False],
       [ True,  True],
       [ True,  True]])

Applying the mask gives:


        
        
            
                
                
                    np.argwhere(mask)
                
            
            array([[1, 0],
       [1, 1],
       [2, 0],
       [2, 1]])

The way to interpret this is as follows:


        
        
            
                
                
                    [1,0] -> row 1, column 0 is non-zero (i.e. greater than 2)
[1,1] -> row 1, column 1 is non-zero
[2,0] -> row 2, column 0 is non-zero
[2,1] -> row 2, column 1 is non-zero

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Published by Isshin Inada

Edited by 0 others

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