sciPy function stats.relfreq () | python

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, " " )   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)

Exit:

 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, "" )   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