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:
Python3
# 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) |
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:
Python3
# 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) |
Output:
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
