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Parameters Return value Examples Basic usage Getting the sum of the weighted average Computing the weighted average of a 2D array Weighted average of all values Weighted average of each column Weighted average of each row

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NumPy | average method

schedule Aug 12, 2023

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Parameters Return value Examples Basic usage Getting the sum of the weighted average Computing the weighted average of a 2D array Weighted average of all values Weighted average of each column Weighted average of each row

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Numpy's average(~) method computes the weighted average along the specified axis.

Parameters

1. a | array-like

The input array.

2. axis | None or int or tuple of int | optional

The axis along which to compute the mean.

Axis	Meaning
0	Row-wise computation of mean
1	Column-wise computation of mean
None	All values used to compute the mean

3. weights | axis | optional

The array containing the weights. The dimension must be 1D with size equal to that of a, or the exact same shape as a. By default, weights=None, that is, a simple mean will be computed.

4. return | boolean | optional

Whether you want the sum of weights returned. By default return=False.

Return value

If axis in unset, then a scalar is returned. Otherwise, a Numpy array of weighted averages is returned.

Examples

Basic usage

Consider the following:


        
        
            
                
                
                    a = np.array([1,2,3])
np.average(a, weights=[0,2,4])
                
            
            2.6667

Here, the weighted average is:


        
        
            
                
                
                    (1*0 + 2*2 + 3*4) / (0+2+4) = 2.6667

Getting the sum of the weighted average

To get the sum of the weighted used (i.e. 0+2+4=6), set returned=True:


        
        
            
                
                
                    np.average([1,2,3], weights=[0,2,4], returned=True)
                
            
            (2.6666666666666665, 6.0)

Computing the weighted average of a 2D array

Suppose we have the following 2D array:


        
        
            
                
                
                    a = np.array([[1,2],[3,4]])
a
                
            
            array([[1, 2],
       [3, 4]])

Weighted average of all values

Computing the weighted average of all values:


        
        
            
                
                
                    np.average(a, weights=[[5,6],[7,8]])
                
            
            2.6923076923076925

Weighted average of each column

Computing the weighted average of each column, set axis=0:


        
        
            
                
                
                    np.average(a, weights=[[5,6],[7,8]], axis=0)
                
            
            array([2.16666667, 3.14285714])

Weighted average of each row

Computing the weighted average of each row, set axis=1:


        
        
            
                
                
                    np.average(a, weights=[[5,6],[7,8]], axis=1)
                
            
            array([1.54545455, 3.53333333])

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Published by Isshin Inada

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