{ "__type": "IngestedDoc", "__tag": 4010, "_content": { "Notes": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This function preserves ndarray subclasses, and works also with matrices and masked arrays (it uses " }, { "__type": "InlineCode", "__tag": 4051, "value": "asanyarray" }, { "__type": "Text", "__tag": 4046, "value": " instead of " }, { "__type": "InlineCode", "__tag": 4051, "value": "asarray" }, { "__type": "Text", "__tag": 4046, "value": " for parameters)." } ] }, { "__type": "Admonition", "__tag": 4056, "kind": "versionadded", "base_type": "neutral", "children": [ { "__type": "AdmonitionTitle", "__tag": 4055, "children": [ { "__type": "Text", "__tag": 4046, "value": "versionadded 1.8" } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Strong", "__tag": 4048, "children": [ { "__type": "Text", "__tag": 4046, "value": "Array API Standard Support" } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "CrossRef", "__tag": 4002, "value": "gzscore", "reference": { "__type": "RefInfo", "__tag": 4000, "module": null, "version": null, "kind": "local", "path": "gzscore" }, "kind": "local" }, { "__type": "Text", "__tag": 4046, "value": " has experimental support for Python Array API Standard compatible backends in addition to NumPy. 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Default is 0. If None, compute over the whole array " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": "." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "ddof", "annotation": "int, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Degrees of freedom correction in the calculation of the standard deviation. Default is 0." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "nan_policy", "annotation": "{'propagate', 'raise', 'omit'}, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "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." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Extended Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "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 " } ] }, { "__type": "Code", "__tag": 4050, "value": "gzscore = log(a/gmu) / log(gsigma)", "execution_status": null, "out": "" }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "where " }, { "__type": "InlineCode", "__tag": 4051, "value": "gmu" }, { "__type": "Text", "__tag": 4046, "value": " (resp. " }, { "__type": "InlineCode", "__tag": 4051, "value": "gsigma" }, { "__type": "Text", "__tag": 4046, "value": ") is the geometric mean (resp. standard deviation)." } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "/scipy/stats/_stats_py.py", "item_line": 2757, "item_type": "function", "aliases": [ "scipy.stats.gzscore" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Text", "__tag": 4046, "value": "Draw samples from a log-normal distribution:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "import numpy as np\nfrom scipy.stats import zscore, gzscore\nimport matplotlib.pyplot as plt\n", "execution_status": "success", "out": "" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "rng = np.random.default_rng()\nmu, sigma = 3., 1. # mean and standard deviation\nx = rng.lognormal(mu, sigma, size=500)\n", "execution_status": "success", "out": "" }, { "__type": "Text", "__tag": 4046, "value": "\nDisplay the histogram of the samples:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "fig, ax = plt.subplots()\n", "execution_status": "success", "out": "" }, { "__type": "Code", "__tag": 4050, "value": "ax.hist(x, 50)\n", "execution_status": "failure", "out": "(array([95., 91., 75., 48., 43., 30., 27., 12., 16., 10., 7., 4., 7.,\n 3., 4., 4., 4., 2., 3., 1., 4., 1., 1., 0., 1., 0.,\n 1., 1., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 1.]), array([ 1.19764, 7.78864, 14.37964, 20.97064, 27.56165, 34.15265,\n 40.74365, 47.33465, 53.92565, 60.51665, 67.10765, 73.69865,\n 80.28966, 86.88066, 93.47166, 100.06266, 106.65366, 113.24466,\n 119.83566, 126.42666, 133.01766, 139.60867, 146.19967, 152.79067,\n 159.38167, 165.97267, 172.56367, 179.15467, 185.74567, 192.33668,\n 198.92768, 205.51868, 212.10968, 218.70068, 225.29168, 231.88268,\n 238.47368, 245.06469, 251.65569, 258.24669, 264.83769, 271.42869,\n 278.01969, 284.61069, 291.20169, 297.79269, 304.3837 , 310.9747 ,\n 317.5657 , 324.1567 , 330.7477 ]), )\n" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success", "out": "" }, { "__type": "Figure", "__tag": 4024, "value": { "__type": "RefInfo", "__tag": 4000, "module": "scipy", "version": "1.18.0", "kind": "assets", "path": "fig-a2d085f7c02572dc.png" } }, { "__type": "Text", "__tag": 4046, "value": "\nDisplay the histogram of the samples standardized by the classical zscore.\nDistribution is rescaled but its shape is unchanged.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "fig, ax = plt.subplots()\n", "execution_status": "success", "out": "" }, { "__type": "Code", "__tag": 4050, "value": "ax.hist(zscore(x), 50)\n", "execution_status": "failure", "out": "(array([95., 91., 75., 48., 43., 30., 27., 12., 16., 10., 7., 4., 7.,\n 3., 4., 4., 4., 2., 3., 1., 4., 1., 1., 0., 1., 0.,\n 1., 1., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 1.]), array([-0.80714, -0.63481, -0.46249, -0.29017, -0.11785, 0.05447,\n 0.2268 , 0.39912, 0.57144, 0.74376, 0.91609, 1.08841,\n 1.26073, 1.43305, 1.60537, 1.7777 , 1.95002, 2.12234,\n 2.29466, 2.46699, 2.63931, 2.81163, 2.98395, 3.15627,\n 3.3286 , 3.50092, 3.67324, 3.84556, 4.01789, 4.19021,\n 4.36253, 4.53485, 4.70717, 4.8795 , 5.05182, 5.22414,\n 5.39646, 5.56879, 5.74111, 5.91343, 6.08575, 6.25807,\n 6.4304 , 6.60272, 6.77504, 6.94736, 7.11969, 7.29201,\n 7.46433, 7.63665, 7.80897]), )\n" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success", "out": "" }, { "__type": "Figure", "__tag": 4024, "value": { "__type": "RefInfo", "__tag": 4000, "module": "scipy", "version": "1.18.0", "kind": "assets", "path": "fig-1747c897e72b0165.png" } }, { "__type": "Text", "__tag": 4046, "value": "\nDemonstrate that the distribution of geometric zscores is rescaled and\nquasinormal:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "fig, ax = plt.subplots()\n", "execution_status": "success", "out": "" }, { "__type": "Code", "__tag": 4050, "value": "ax.hist(gzscore(x), 50)\n", "execution_status": "failure", "out": "(array([ 1., 0., 3., 2., 1., 1., 1., 3., 4., 8., 7., 11., 7.,\n 10., 11., 15., 15., 14., 18., 14., 17., 23., 20., 24., 21., 24.,\n 15., 20., 23., 20., 21., 23., 14., 15., 11., 12., 7., 7., 8.,\n 7., 4., 4., 6., 1., 2., 2., 0., 0., 1., 2.]), array([-2.79229, -2.68006, -2.56783, -2.45561, -2.34338, -2.23116,\n -2.11893, -2.00671, -1.89448, -1.78226, -1.67003, -1.5578 ,\n -1.44558, -1.33335, -1.22113, -1.1089 , -0.99668, -0.88445,\n -0.77223, -0.66 , -0.54777, -0.43555, -0.32332, -0.2111 ,\n -0.09887, 0.01335, 0.12558, 0.2378 , 0.35003, 0.46226,\n 0.57448, 0.68671, 0.79893, 0.91116, 1.02338, 1.13561,\n 1.24783, 1.36006, 1.47229, 1.58451, 1.69674, 1.80896,\n 1.92119, 2.03341, 2.14564, 2.25786, 2.37009, 2.48232,\n 2.59454, 2.70677, 2.81899]), )\n" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success", "out": "" }, { "__type": "Figure", "__tag": 4024, "value": { "__type": "RefInfo", "__tag": 4000, "module": "scipy", "version": "1.18.0", "kind": "assets", "path": "fig-e84d6cc8932367cd.png" } } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "gmean", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._stats_py:gmean" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Geometric mean" } ] } ], "type": "func" }, { "__type": 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