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bundles / scipy 1.17.1 / scipy / stats / _stats_py / gzscore

function

`scipy.stats._stats_py:gzscore`

source: /scipy/stats/_stats_py.py :2763

Signature

def gzscore ( a , * , axis = 0 , ddof = 0 , nan_policy = propagate )

Summary

Compute the geometric standard score.

Extended Summary

Compute the geometric z score of each strictly positive value in the sample, relative to the geometric mean and standard deviation. Mathematically the geometric z score can be evaluated as

gzscore = log(a/gmu) / log(gsigma)

where gmu (resp. gsigma) is the geometric mean (resp. standard deviation).

Parameters

a : array_like: Sample data.
axis : int or None, optional: Axis along which to operate. Default is 0. If None, compute over the whole array a.
ddof : int, optional: Degrees of freedom correction in the calculation of the standard deviation. Default is 0.
nan_policy : {'propagate', 'raise', 'omit'}, optional: Defines how to handle when input contains nan. 'propagate' returns nan, 'raise' throws an error, 'omit' performs the calculations ignoring nan values. Default is 'propagate'. Note that when the value is 'omit', nans in the input also propagate to the output, but they do not affect the geometric z scores computed for the non-nan values.

Returns

gzscore : array_like: The geometric z scores, standardized by geometric mean and geometric standard deviation of input array a.

Notes

This function preserves ndarray subclasses, and works also with matrices and masked arrays (it uses asanyarray instead of asarray for parameters).

Array API Standard Support

gzscore has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ✅                   
JAX                   ✅                     ✅                   
Dask                  ✅                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

Draw samples from a log-normal distribution:

import numpy as np
from scipy.stats import zscore, gzscore
import matplotlib.pyplot as plt

✓

rng = np.random.default_rng()
mu, sigma = 3., 1.  # mean and standard deviation
x = rng.lognormal(mu, sigma, size=500)

✓

Display the histogram of the samples:

fig, ax = plt.subplots()

✓

ax.hist(x, 50)

✗

plt.show()

✓

Display the histogram of the samples standardized by the classical zscore. Distribution is rescaled but its shape is unchanged.

fig, ax = plt.subplots()

✓

ax.hist(zscore(x), 50)

✗

plt.show()

✓

Demonstrate that the distribution of geometric zscores is rescaled and quasinormal:

fig, ax = plt.subplots()

✓

ax.hist(gzscore(x), 50)

✗

plt.show()

✓

Aliases

scipy.stats.gzscore