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Parameters Return value Examples Basic mapping Using a boolean mask Replace values based on number Multi-dimensional arrays

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NumPy | where method

schedule Aug 12, 2023

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Parameters Return value Examples Basic mapping Using a boolean mask Replace values based on number Multi-dimensional arrays

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Numpy's where(~) method maps an array of booleans to the specified outputs, similar to how an if-else works.

Parameters

1. condition | array_like of booleans

An array of booleans.

2. x | array_like

All instances of True will be replaced by x.

3. y | array_like

All instances of False will be replaced by y.

Return value

A Numpy array.

Examples

Basic mapping

To replace all True with 5, and all False with 10:


        
        
            
                
                
                    x = np.array([True, True, False, False])
np.where(x, 5, 10)
                
            
            array([5, 5, 10, 10])

Using a boolean mask

On a more practical note, the where() method works well with a boolean mask. Suppose we wanted all values larger than 2 to be 10, and all other values to be -1.

We first build a boolean mask like follows:


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

We then apply the mapping:


        
        
            
                
                
                    np.where(mask, 10, -1)
                
            
            array([-1, -1, 10, 10])

Replace values based on number

Suppose we wanted to perform some arithmetics based on a certain criteria. As an example, we want to add 5 to values larger than 2, and subtract by 5 otherwise:


        
        
            
                
                
                    x = np.array([1,2,3,4])
np.where(x>2, x+5, x-5)
                
            
            array([-4, -3, 8, 9])

Multi-dimensional arrays

The method where(~) can also be used for multi-dimensional arrays:


        
        
            
                
                
                    x = np.array([[1,2],[3,4]])
np.where(x>2, 10, 5)
                
            
            array([[5, 5],
       [10, 10]])

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Published by Isshin Inada

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