scipy.stats.relfreq(a, numbins, defaultreallimits, weights)
is a relative frequency histogram, using the histogram function.
Parameters :
arr : [array_like] input array.
numbins : Number of bins to use for the histogram. [Default = 10]
defaultreallimits : (lower, upper) range of the histogram.
weights : [array_like] weights for each array element.Results :
– relative frequency binned values
– width of each bin
– lower real limit
– extra points.
Code #1:
# relative frequency from scipy import stats import numpy as np arr1 = [ 1 , 3 , 27 , 2 , 5 , 13 ] print ( "Array element : " , arr1, "\n" ) a, b, c, d = stats.relfreq(arr1, numbins = 4 ) print ( "cumulative frequency : " , a) print ( "Lower Limit : " , b) print ( "bin size : " , c) print ( "extra-points : " , d) |
Output :
Array element : [1, 3, 27, 2, 5, 13] cumulative frequency : [0.66666667 0.16666667 0. 0.16666667] Lower Limit : -3.333333333333333 bin size : 8.666666666666666 extra-points : 0
Code #2:
# relative frequency from scipy import stats import numpy as np arr1 = [ 1 , 3 , 27 , 2 , 5 , 13 ] print ( "Array element : " , arr1, "\n" ) a, b, c, d = stats.relfreq(arr1, numbins = 4 , weights = [. 1 , . 2 , . 1 , . 3 , 1 , 6 ]) print ( "cumfreqs : " , a) print ( "lowlim : " , b) print ( "binsize : " , c) print ( "extrapoints : " , d) |
Output :
Array element : [1, 3, 27, 2, 5, 13] cumfreqs : [0.26666667 1. 0. 0.01666667] lowlim : -3.333333333333333 binsize : 8.666666666666666 extrapoints : 0
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