`scipy.stats._stats_py:gmean`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_stats_py.py:153

Signature

def gmean(a, axis = 0, dtype = None, weights = None, *, nan_policy = propagate, keepdims = False)

Summary

Compute the weighted geometric mean along the specified axis.

Extended Summary

The weighted geometric mean of the array $a_{i}$ associated to weights $w_{i}$ is:

exp (\frac{\sum _{i = 1}^{n} w _{i} ln a _{i}}{\sum _{i = 1}^{n} w _{i}}),

and, with equal weights, it gives:

n i = 1 \prod n a_{i} .

Parameters

a : array_like

Input array or object that can be converted to an array.

axis : int or None, default: 0

If an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If None, the input will be raveled before computing the statistic.

dtype : dtype, optional

(Floating point) dtype to which numerical arguments are cast before performing the calculation. If dtype is not specified, it defaults to the floating point result dtype of the numerical arguments.

weights : array_like, optional

The weights array must be broadcastable to the same shape as a. Default is None, which gives each value a weight of 1.0.

nan_policy : {'propagate', 'omit', 'raise'}

Defines how to handle input NaNs.

propagate: if a NaN is present in the axis slice (e.g. row) along which the statistic is computed, the corresponding entry of the output will be NaN.
omit: NaNs will be omitted when performing the calculation. If insufficient data remains in the axis slice along which the statistic is computed, the corresponding entry of the output will be NaN.
raise: if a NaN is present, a ValueError will be raised.

nan_policy='raise' and nan_policy='omit' are not supported by lazy backends, and nan_policy='omit' is not compatible with MArray input. For multidimensional input, nan_policy='omit' is supported only by the NumPy backend. Instead, mask NaN values using MArray; and use the default nan_policy. See Notes for support information.

keepdims : bool, default: False

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Returns

gmean : ndarray: See dtype parameter above.

Notes

The sample geometric mean is the exponential of the mean of the natural logarithms of the observations. Negative observations will produce NaNs in the output because the natural logarithm (as opposed to the complex logarithm) is defined only for non-negative reals.

Beginning in SciPy 1.9, np.matrix inputs (not recommended for new code) are converted to np.ndarray before the calculation is performed. In this case, the output will be a scalar or np.ndarray of appropriate shape rather than a 2D np.matrix. Similarly, while masked elements of masked arrays are ignored, the output will be a scalar or np.ndarray rather than a masked array with mask=False.

Examples

from scipy.stats import gmean

✓

gmean([1, 4])
gmean([1, 2, 3, 4, 5, 6, 7])
gmean([1, 4, 7], weights=[3, 1, 3])

✗

np.float64(2.80668351922014)

Aliases

scipy.stats.gmean