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NumPy | put_along_axis method

schedule Aug 10, 2023

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Parameters Return value Examples Basic usage 2D arrays Setting values row-wise Setting values column-wise

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Numpy's put_along_axis(~) sets a specific value in the input array. This is done in-place, that is, no new Numpy array is created.

Parameters

1. a | array-like

The input array. All input arrays are treated as a flattened array.

2. indices | Numpy array

The indices in the specified axis where the values will be set. For 2D arrays, indices would represent the column/row index. The dimensions must be the same as that of a.

3. values | array-like

The values to set. If values is shorter than a, then values will be repeated to ensure the shape matches up.

4. axislink | int

The axis along which to insert the values. For 2D arrays, the allowed values are as follows:

Value	Description
0	Values are set row-wise.
1	Values are set column-wise.

By default, mode="raise".

Return value

None - the setting is done in-place.

Examples

Basic usage


        
        
            
                
                
                    a = np.array([5,6,7,8])
np.put_along_axis(a, np.array([1]), [9], axis=0)
a
                
            
            array([5, 9, 7, 8])

2D arrays

Consider the following 2D array:


        
        
            
                
                
                    a = np.array([[5,6],[7,8]])
a
                
            
            array([[5, 6],
       [7, 8]])

Setting values row-wise

Set axis=0, like so:


        
        
            
                
                
                    a = np.array([[5,6],[7,8]])
np.put_along_axis(a, np.array([[1]]), [9], axis=0)
a
                
            
            array([[5, 6],
       [9, 9]])

Notice how the dimensions of the indices matches that of the input array a.

Setting values column-wise

Set axis=1, like so:


        
        
            
                
                
                    a = np.array([[5,6],[7,8]])
np.put_along_axis(a, np.array([[1]]), [9], axis=1)
a
                
            
            array([[5, 9],
       [7, 9]])

Here, note how [1,0] does not represent the 2nd row 1st column. Instead, it represents the 1st and 0th index of the flattened version of a. Therefore, we could have equivalently used [0,1] instead.

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Published by Isshin Inada

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