# sciPy stats.scoreatpercentile() function | Python

• Last Updated : 13 Feb, 2019

`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
```

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.]
```

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