scipy.stats.scoreatpercentile(a, score, kind='rank') function helps us to calculate the score at a given percentile of the input array.
The score at percentile = 50 is the median. If the desired quantile lies between two data points, we interpolate between them, according to the value of interpolation.
Parameters :
arr : [array_like] input array.
per : [array_like] Percentile at which we need the score.
limit : [tuple] the lower and upper limits within which to compute the percentile.
axis : [int] axis along which we need to calculate the score.Results : Score at Percentile relative to the array element.
Code #1:
# scoreatpercentile from scipy import stats import numpy as np   # 1D array  arr = [20, 2, 7, 1, 7, 7, 34, 3]   print("arr : ", arr)    print ("\nScore at 50th percentile : ",        stats.scoreatpercentile(arr, 50))   print ("\nScore at 90th percentile : ",        stats.scoreatpercentile(arr, 90))   print ("\nScore at 10th percentile : ",        stats.scoreatpercentile(arr, 10))   print ("\nScore at 100th percentile : ",        stats.scoreatpercentile(arr, 100))   print ("\nScore at 30th percentile : ",        stats.scoreatpercentile(arr, 30)) |
Output:
arr : [20, 2, 7, 1, 7, 7, 34, 3] Score at 50th percentile : 7.0 Score at 90th percentile : 24.2 Score at 10th percentile : 1.7 Score at 100th percentile : 34.0 Score at 30th percentile : 3.4
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Code #2:
# scoreatpercentile from scipy import stats import numpy as np   arr = [[14, 17, 12, 33, 44],          [15, 6, 27, 8, 19],         [23, 2, 54, 1, 4, ]]   print("arr : ", arr)    print ("\nScore at 50th percentile : ",        stats.scoreatpercentile(arr, 50))   print ("\nScore at 50th percentile : ",        stats.scoreatpercentile(arr, 50, axis = 1))   print ("\nScore at 50th percentile : ",        stats.scoreatpercentile(arr, 50, axis = 0)) |
Output:
arr : [[14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4]] Score at 50th percentile : 15.0 Score at 50th percentile : [ 17. 15. 4.] Score at 50th percentile : [ 15. 6. 27. 8. 19.]
