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NumPy | trunc method

schedule Aug 10, 2023

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Parameters Return value Examples Basic usage Specifying an output array Specifying a boolean mask

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Numpy's trunc(~) method removes the decimals of each value in the input array. See examples below for clarification.

Parameters

1. a | array-like

The input array.

2. out | Numpy array | optional

Instead of creating a new array, you can place the computed result into the array specified by out.

3. where | array of boolean | optional

Values that are flagged as False will be ignored, that is, their original value will be uninitialized. If you specified the out parameter, the behavior is slightly different - the original value will be kept intact. Since this is a source of confusion for many, check examples below.

Return value

If a is a scalar, then a scalar is returned. Otherwise, a Numpy array is returned.

Examples

Basic usage


        
        
            
                
                
                    np.trunc([-3.2, -1.7, 1.5, 5.7])
                
            
            array([-3., -1.,  1.,  5.])

Specifying an output array


        
        
            
                
                
                    a = np.zeros(3)
np.trunc([-3.2, -1.7, 1.5], out=a)
a
                
            
            array([-3., -1.,  1.])

Specifying a boolean mask


        
        
            
                
                
                    np.trunc([-3.2, -1.7, 1.5], where=[False, True, False])
                
            
            array([3454., -1.,  1867.])

Here, only the second number was used for calculation since it has a corresponding boolean of True in the mask. You should notice how the values with False yielded strange results - in fact, you should disregard them because they are uninitialized numbers that are of no practical use.

Now, if you specified the out parameter, instead of uninitalized values, the original values will be left intact:


        
        
            
                
                
                    a = np.zeros(3)
np.trunc([-3.2, -1.7, 1.5], out=a, where=[False, True, False])
a
                
            
            array([ 0., -1.,  0.])

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Published by Isshin Inada

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