# sciPy stats.zscore() function | Python

Last Updated : 08 Apr, 2024

scipy.stats.zscore(arr, axis=0, ddof=0) function computes the relative Z-score of the input data, relative to the sample mean and standard deviation.

Its formula:

Parameters :arr : [array_like] Input array or object for which Z-score is to be calculated. axis : Axis along which the mean is to be computed. By default axis = 0. ddof : Degree of freedom correction for Standard Deviation. Results : Z-score of the input data.

Code #1: Working

Python3 1== ```# stats.zscore() method import numpy as np from scipy import stats arr1 = [[20, 2, 7, 1, 34], [50, 12, 12, 34, 4]] arr2 = [[50, 12, 12, 34, 4], [12, 11, 10, 34, 21]] print (&quot;\narr1 : &quot;, arr1) print (&quot;\narr2 : &quot;, arr2) print (&quot;\nZ-score for arr1 : \n&quot;, stats.zscore(arr1)) print (&quot;\nZ-score for arr1 : \n&quot;, stats.zscore(arr1, axis = 1)) ```

Output :

`arr1 :  [[20, 2, 7, 1, 34], [50, 12, 12, 34, 4]]arr2 :  [[50, 12, 12, 34, 4], [12, 11, 10, 34, 21]]Z-score for arr1 :  [[-1. -1. -1. -1.  1.] [ 1.  1.  1.  1. -1.]]Z-score for arr1 :  [[ 0.57251144 -0.85876716 -0.46118977 -0.93828264  1.68572813] [ 1.62005758 -0.61045648 -0.61045648  0.68089376 -1.08003838]]`

Code #2 : Z-score

Python3 1== ```import numpy as np from scipy import stats arr2 = [[50, 12, 12, 34, 4], [12, 11, 10, 34, 21]] print (&quot;\nZ-score for arr2 : \n&quot;, stats.zscore(arr2, axis = 0)) print (&quot;\nZ-score for arr2 : \n&quot;, stats.zscore(arr2, axis = 1)) ```

Output :

`Z-score for arr2 :  [[ 1.  1.  1. nan -1.] [-1. -1. -1. nan  1.]]Z-score for arr2 :  [[ 1.62005758 -0.61045648 -0.61045648  0.68089376 -1.08003838] [-0.61601725 -0.72602033 -0.83602341  1.80405051  0.37401047]]`