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Parameters Return value Examples Basic usage Specifying weights Specifying the min length

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NumPy | bincount method

schedule Aug 10, 2023

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Parameters Return value Examples Basic usage Specifying weights Specifying the min length

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Numpy's bincount(~) method computes the bin count (i.e. the number of values that fall in an interval) given an array of values. By default, the interval size of each bin is one, and so the number of bins is dependent on the range of the input values.

Parameters

1. a | array-like

The input array.

2. weights | array-like | optional

An array containing the weights placed on each of the input values. If a value falls in a particular bin, instead of incrementing the count by one, we increment by the corresponding weight. The shape must be the same as that of a. By default, the weights are all one.

3. minlength | int | optional

The minimum number of bins.

Return value

A Numpy array containing the bin counts.

Examples

Basic usage


        
        
            
                
                
                    my_hist = np.bincount([1,3,6,6,10])
my_hist
                
            
            array([0, 1, 0, 1, 0, 0, 2, 0, 0, 0, 1])

Specifying weights


        
        
            
                
                
                    my_hist = np.bincount([1,3,6,6,10], weights=[2,3,4,5,6])
my_hist
                
            
            array([0., 2., 0., 3., 0., 0., 9., 0., 0., 0., 6.])

The reason why we get a 9 there is that the two 6s we have in the input array obviously fall in the same bin, and since their respective weights are 4 and 5, we end up with a total bin count of 4+5=9.

Specifying the min length


        
        
            
                
                
                    my_hist = np.bincount([1,3,6,6,10], minlength=20)
my_hist
                
            
            array([0, 1, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Notice bin size remained as one - the minlength just widened the global range.

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Published by Isshin Inada

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