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.

5. densitylink | boolean | optional

Whether to normalise to a probability density function (i.e. total area equaling one). By default, density=False.

Return value

A tuple of two NumPy arrays:

The values of the histogram (i.e. the frequency counts)
The bin edges

Examples

Basic usage


        
        
            
                
                
                    my_hist, bin_edges = np.histogram([1,3,6,6,10])
print("hist:", my_hist)
print("bin_edges:", bin_edges)
                
            
            hist: [1 0 1 0 0 2 0 0 0 1]
bin_edges: [ 1.   1.9  2.8  3.7  4.6  5.5  6.4  7.3  8.2  9.1 10. ]

Here, the bin_edges represent the intervals of the bins, and the hist represents the number of values that fall between the interval. For instance, there is a total of one item that falls between the interval 1 and 1.9, so we get a value 1 for the spot in the histogram.

Specifying the number of bins


        
        
            
                
                
                    my_hist, bin_edges = np.histogram([1,3,6,6,10], bins=5)
print("hist:", my_hist)
print("bin_edges:", bin_edges)
                
            
            hist: [1 1 2 0 1]
bin_edges: [ 1.   2.8  4.6  6.4  8.2 10. ]

Specifying bin edges


        
        
            
                
                
                    my_hist, bin_edges = np.histogram([1,3,6,6,10], bins=[1,5,10])
print("hist:", my_hist)
print("bin_edges:", bin_edges)
                
            
            hist: [2 3]
bin_edges: [ 1  5 10]

Specifying a range


        
        
            
                
                
                    my_hist, bin_edges = np.histogram([1,3,6,6,10], range=(0,20))
print("hist:", my_hist)
print("bin_edges:", bin_edges)
                
            
            hist: [1 1 0 2 0 1 0 0 0 0]
bin_edges: [ 0.  2.  4.  6.  8. 10. 12. 14. 16. 18. 20.]

Specifying weights


        
        
            
                
                
                    my_hist, bin_edges = np.histogram([1,3,6,6,10], weights=[1,5,1,1,1])
print("hist:", my_hist)
print("bin_edges:", bin_edges)
                
            
            hist: [1 0 5 0 0 2 0 0 0 1]
bin_edges: [ 1.   1.9  2.8  3.7  4.6  5.5  6.4  7.3  8.2  9.1 10. ]

The reason why we get a 5 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.

Normalising


        
        
            
                
                
                    my_hist, bin_edges = np.histogram([1,3,6,6,10], density=True)
print("hist:", my_hist)
print("bin_edges:", bin_edges)
                
            
            hist: [0.22222222 0.         0.22222222 0.         0.         0.44444444 0.         0.         0.         0.22222222]
bin_edges: [ 1.   1.9  2.8  3.7  4.6  5.5  6.4  7.3  8.2  9.1 10. ]

Published by Isshin Inada

Edited by 0 others

Did you find this page useful?

thumb_up

thumb_down

Comment

Citation

Ask a question or leave a feedback...

thumb_up

thumb_down

chat_bubble_outline

settings

Enjoy our search

Hit / to insta-search docs and recipes!