sciPy function stats.cumfreq () | python

scipy.stats.cumfreq (a, numbins, defaultreallimits, weights) works using the histogram function and calculates the histogram of the cumulative frequency. It includes cumulative frequency values, width of each element, lower real limit, additional 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, " " )

 

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)

Output:

 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, "" )

 

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)

Exit :

 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