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Parameters Return value Examples Computing the mean of 1D array Computing the mean of 2D array Mean of all values Mean of each column Mean of each row

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NumPy | mean method

schedule Aug 10, 2023

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Parameters Return value Examples Computing the mean of 1D array Computing the mean of 2D array Mean of all values Mean of each column Mean of each row

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NumPy's mean(~) method computes the mean value along the specified axis.

Parameters

1. a | array-like

The input array.

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

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

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

By default, axis=None.

3. dtype | string or type | optional

The data-type to use during the computation of the mean. If the inputs are integers, then float64 is used. Otherwise, the same data-type will be used.

NOTE

As a best practice, you should specify float64 as dtype since if your input is of type float32, then float32 will be used during the computation of the mean, which would result in less accurate results.

4. out | NumPy array | optional

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

Return value

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

Examples

Computing the mean of 1D array


        
        
            
                
                
                    np.mean([1,2,3])
                
            
            2.0

Computing the mean of 2D array

Consider the following array:


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

Mean of all values

Mean of each column


        
        
            
                
                
                    np.mean(a, axis=0)
                
            
            array([2., 3.])

Mean of each row


        
        
            
                
                
                    np.mean(a, axis=1)
                
            
            array([1.5, 3.5])

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Published by Isshin Inada

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