sciPy stats.cumfreq() function | Python
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
