sciPy stats.zmap() function | Python

scipy.stats.zmap(scores, compare, axis=0, ddof=0) function computes the relative Z-score of the input data. The scores that are standardized to zero mean and unit variance, where mean and variance are calculated from the comparison array.

Its formula:

Parameters :
scores : [array_like]Input array or object for which Z-score is to calculate.
compare : [array_like]Input array or object for which the mean and standard deviation of the normalization are taken
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

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# stats.zmap() 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 : \n", stats.zmap(arr1, arr2))

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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 : 
 [[ -0.57894737 -19.          -4.                 -inf   2.52941176]
 [  1.           1.           1.                  nan  -1.        ]]

 
Code #2 : Z-score

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# stats.zmap() 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 ("\nZ-score : \n", stats.zmap(arr1, arr2, axis = 1))

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Output :

sem ratio for arr1 : 
 [[-0.14087457 -1.19743386 -0.90394517 -1.2561316   0.68089376]
 [ 3.5640998  -0.61601725 -0.61601725  1.80405051 -1.49604189]]


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