shapiro — SciPy v1.15.3 Manual (original) (raw)

scipy.stats.

scipy.stats.shapiro(x, *, axis=None, nan_policy='propagate', keepdims=False)[source]#

Perform the Shapiro-Wilk test for normality.

The Shapiro-Wilk test tests the null hypothesis that the data was drawn from a normal distribution.

Parameters:

xarray_like

Array of sample data. Must contain at least three observations.

axisint or None, default: None

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.

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.

keepdimsbool, 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:

statisticfloat

The test statistic.

p-valuefloat

The p-value for the hypothesis test.

Notes

The algorithm used is described in [4] but censoring parameters as described are not implemented. For N > 5000 the W test statistic is accurate, but the p-value may not be.

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.

References

[2]

Shapiro, S. S. & Wilk, M.B, “An analysis of variance test for normality (complete samples)”, Biometrika, 1965, Vol. 52, pp. 591-611, DOI:10.2307/2333709

[3]

Razali, N. M. & Wah, Y. B., “Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests”, Journal of Statistical Modeling and Analytics, 2011, Vol. 2, pp. 21-33.

[4]

Royston P., “Remark AS R94: A Remark on Algorithm AS 181: The W-test for Normality”, 1995, Applied Statistics, Vol. 44,DOI:10.2307/2986146

Examples

import numpy as np from scipy import stats rng = np.random.default_rng() x = stats.norm.rvs(loc=5, scale=3, size=100, random_state=rng) shapiro_test = stats.shapiro(x) shapiro_test ShapiroResult(statistic=0.9813305735588074, pvalue=0.16855233907699585) shapiro_test.statistic 0.9813305735588074 shapiro_test.pvalue 0.16855233907699585

For a more detailed example, see Shapiro-Wilk test for normality.