scipy.stats.cumfreq(a, numbins, defaultreallimits, weights) works using the histogram function and calculates the cumulative frequency histogram. It includes cumulative frequency binned values, width of each bin, lower real limit, extra points.
Parameters :
arr : [array_like] input array.
numbins : [int] number of bins to use for the histogram. [Default = 10]
defaultlimits : (lower, upper) range of the histogram.
weights : [array_like] weights for each array element.
Results :
– cumulative frequency binned values
– width of each bin
– lower real limit
– extra points.
Code #1:
# cumulative 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.cumfreq(arr1, numbins = 4 )
print ( "cumulative frequency : " , a)
print ( "Lower Limit : " , b)
print ( "bin size : " , c)
print ( "extra-points : " , d)
|
Array element : [1, 3, 27, 2, 5, 13] cumulative frequency : [ 4. 5. 5. 6.] Lower Limit : -3.33333333333 bin size : 8.66666666667 extra-points : 0
Code #2:
# cumulative 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.cumfreq(arr1, numbins = 4 ,
weights = [. 1 , . 2 , . 1 , . 3 , 1 , 6 ])
print ( "cumfreqs : " , a)
print ( "lowlim : " , b)
print ( "binsize : " , c)
print ( "extrapoints : " , d)
|
Array element : [1, 3, 27, 2, 5, 13] cumfreqs : [ 1.6 7.6 7.6 7.7] lowlim : -3.33333333333 binsize : 8.66666666667 extrapoints : 0