# sciPy stats.zscore() function | Python

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

 `# 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` `(``"\narr1 : "``, arr1) ` `print` `(``"\narr2 : "``, arr2) ` ` `  `print` `(``"\nZ-score for arr1 : \n"``, stats.zscore(arr1)) ` `print` `(``"\nZ-score for arr1 : \n"``, 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

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

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