{ "__type": "IngestedDoc", "__tag": 4010, "_content": { "Notes": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Given a sample " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": " from an underlying distribution, " }, { "__type": "CrossRef", "__tag": 4002, "value": "quantile", "reference": { "__type": "RefInfo", "__tag": 4000, "module": null, "version": null, "kind": "local", "path": "quantile" }, "kind": "local" }, { "__type": "Text", "__tag": 4046, "value": " provides a nonparametric estimate of the inverse cumulative distribution function." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "By default, this is done by interpolating between adjacent elements in " }, { "__type": "InlineCode", "__tag": 4051, "value": "y" }, { "__type": "Text", "__tag": 4046, "value": ", a sorted copy of " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": ": " } ] }, { "__type": "Code", "__tag": 4050, "value": "(1-g)*y[j] + g*y[j+1]", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "where the index " }, { "__type": "InlineCode", "__tag": 4051, "value": "j" }, { "__type": "Text", "__tag": 4046, "value": " and coefficient " }, { "__type": "InlineCode", "__tag": 4051, "value": "g" }, { "__type": "Text", "__tag": 4046, "value": " are the integral and fractional components of " }, { "__type": "InlineCode", "__tag": 4051, "value": "q * (n-1)" }, { "__type": "Text", "__tag": 4046, "value": ", and " }, { "__type": "InlineCode", "__tag": 4051, "value": "n" }, { "__type": "Text", "__tag": 4046, "value": " is the number of elements in the sample." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This is a special case of Equation 1 of H&F " }, { "__type": "FootnoteReference", "__tag": 4066, "label": "1" }, { "__type": "Text", "__tag": 4046, "value": ". 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The " }, { "__type": "InlineCode", "__tag": 4051, "value": "- 1" }, { "__type": "Text", "__tag": 4046, "value": " in the formulas for " }, { "__type": "InlineCode", "__tag": 4051, "value": "j" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "g" }, { "__type": "Text", "__tag": 4046, "value": " accounts for Python's 0-based indexing." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The table above includes only the estimators from H&F that are continuous functions of probability " }, { "__type": "ParamRef", "__tag": 4071, "name": "q" }, { "__type": "Text", "__tag": 4046, "value": " (estimators 4-9). 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Like " }, { "__type": "InlineCode", "__tag": 4051, "value": "method='linear'" }, { "__type": "Text", "__tag": 4046, "value": ", all have " }, { "__type": "InlineCode", "__tag": 4051, "value": "m = 1 - q" }, { "__type": "Text", "__tag": 4046, "value": " so that " }, { "__type": "InlineCode", "__tag": 4051, "value": "j = q*(n-1) // 1" }, { "__type": "Text", "__tag": 4046, "value": ", but " }, { "__type": "InlineCode", "__tag": 4051, "value": "g" }, { "__type": "Text", "__tag": 4046, "value": " is defined as follows." } ] }, { "__type": "BulletList", "__tag": 4053, "ordered": false, "start": 1, "children": [ { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "lower" }, { "__type": "Text", "__tag": 4046, "value": ": " }, { "__type": "InlineCode", "__tag": 4051, "value": "g = 0" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "midpoint" }, { "__type": "Text", "__tag": 4046, "value": ": " }, { "__type": "InlineCode", "__tag": 4051, "value": "g = 0.5" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "higher" }, { "__type": "Text", "__tag": 4046, "value": ": " }, { "__type": "InlineCode", "__tag": 4051, "value": "g = 1" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "nearest" }, { "__type": "Text", "__tag": 4046, "value": ": " }, { "__type": "InlineCode", "__tag": 4051, "value": "g = (q*(n-1) % 1) > 0.5" } ] } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Strong", "__tag": 4048, "children": [ { "__type": "Text", "__tag": 4046, "value": "Weighted quantiles:" } ] }, { "__type": "Text", "__tag": 4046, "value": " More formally, the quantile at probability level " }, { "__type": "InlineMath", "__tag": 4057, "value": "q" }, { "__type": "Text", "__tag": 4046, "value": " of a cumulative distribution function " }, { "__type": "InlineMath", "__tag": 4057, "value": "F(y)=P(Y \\leq y)" }, { "__type": "Text", "__tag": 4046, "value": " with probability measure " }, { "__type": "InlineMath", "__tag": 4057, "value": "P" }, { "__type": "Text", "__tag": 4046, "value": " is defined as any number " }, { "__type": "InlineMath", "__tag": 4057, "value": "x" }, { "__type": "Text", "__tag": 4046, "value": " that fulfills the " }, { "__type": "Emphasis", "__tag": 4047, "children": [ { "__type": "Text", "__tag": 4046, "value": "coverage conditions" } ] } ] }, { "__type": "Math", "__tag": 4058, "value": "P(Y < x) \\leq q \\quad\\text{and}\\quad P(Y \\leq x) \\geq q" }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "with random variable " }, { "__type": "InlineMath", "__tag": 4057, "value": "Y\\sim P" }, { "__type": "Text", "__tag": 4046, "value": ". Sample quantiles, the result of " }, { "__type": "CrossRef", "__tag": 4002, "value": "quantile", "reference": { "__type": "RefInfo", "__tag": 4000, "module": null, "version": null, "kind": "local", "path": "quantile" }, "kind": "local" }, { "__type": "Text", "__tag": 4046, "value": ", provide nonparametric estimation of the underlying population counterparts, represented by the unknown " }, { "__type": "InlineMath", "__tag": 4057, "value": "F" }, { "__type": "Text", "__tag": 4046, "value": ", given a data vector " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": " of length " }, { "__type": "InlineCode", "__tag": 4051, "value": "n" }, { "__type": "Text", "__tag": 4046, "value": "." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Some of the estimators above arise when one considers " }, { "__type": "InlineMath", "__tag": 4057, "value": "F" }, { "__type": "Text", "__tag": 4046, "value": " as the empirical distribution function of the data, i.e. " }, { "__type": "InlineMath", "__tag": 4057, "value": "F(y) = \\frac{1}{n} \\sum_i 1_{a_i \\leq y}" }, { "__type": "Text", "__tag": 4046, "value": ". Then, different methods correspond to different choices of " }, { "__type": "InlineMath", "__tag": 4057, "value": "x" }, { "__type": "Text", "__tag": 4046, "value": " that fulfill the above coverage conditions. Methods that follow this approach are " }, { "__type": "InlineCode", "__tag": 4051, "value": "inverted_cdf" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "averaged_inverted_cdf" }, { "__type": "Text", "__tag": 4046, "value": "." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "For weighted quantiles, the coverage conditions still hold. The empirical cumulative distribution is simply replaced by its weighted version, i.e. " }, { "__type": "InlineMath", "__tag": 4057, "value": "P(Y \\leq t) = \\frac{1}{\\sum_i w_i} \\sum_i w_i 1_{x_i \\leq t}" }, { "__type": "Text", "__tag": 4046, "value": ". Only " }, { "__type": "InlineCode", "__tag": 4051, "value": "method=\"inverted_cdf\"" }, { "__type": "Text", "__tag": 4046, "value": " supports weights." } ] } ], "title": [], "level": 0, "target": null }, "Warns": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Raises": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Yields": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Methods": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Returns": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "quantile", "annotation": "scalar or ndarray", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "If " }, { "__type": "ParamRef", "__tag": 4071, "name": "q" }, { "__type": "Text", "__tag": 4046, "value": " is a single probability and " }, { "__type": "InlineRole", "__tag": 4003, "value": "axis=None", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": ", then the result is a scalar. If multiple probability levels are given, first axis of the result corresponds to the quantiles. The other axes are the axes that remain after the reduction of " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": ". If the input contains integers or floats smaller than " }, { "__type": "InlineCode", "__tag": 4051, "value": "float64" }, { "__type": "Text", "__tag": 4046, "value": ", the output data-type is " }, { "__type": "InlineCode", "__tag": 4051, "value": "float64" }, { "__type": "Text", "__tag": 4046, "value": ". Otherwise, the output data-type is the same as that of the input. If " }, { "__type": "ParamRef", "__tag": 4071, "name": "out" }, { "__type": "Text", "__tag": 4046, "value": " is specified, that array is returned instead." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Compute the q-th quantile of the data along the specified axis." } ] } ], "title": [], "level": 0, "target": null }, "Receives": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Warnings": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Attributes": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Parameters": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "a", "annotation": "array_like of real numbers", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Input array or object that can be converted to an array." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "q", "annotation": "array_like of float", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Probability or sequence of probabilities of the quantiles to compute. Values must be between 0 and 1 inclusive." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "axis", "annotation": "{int, tuple of int, None}, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Axis or axes along which the quantiles are computed. The default is to compute the quantile(s) along a flattened version of the array." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "out", "annotation": "ndarray, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "overwrite_input", "annotation": "bool, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "If True, then allow the input array " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": " to be modified by intermediate calculations, to save memory. In this case, the contents of the input " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": " after this function completes is undefined." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "method", "annotation": "str, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This parameter specifies the method to use for estimating the quantile. There are many different methods, some unique to NumPy. The recommended options, numbered as they appear in " }, { "__type": "FootnoteReference", "__tag": 4066, "label": "1" }, { "__type": "Text", "__tag": 4046, "value": ", are:" } ] }, { "__type": "BulletList", "__tag": 4053, "ordered": true, "start": 1, "children": [ { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'inverted_cdf'" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'averaged_inverted_cdf'" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'closest_observation'" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'interpolated_inverted_cdf'" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'hazen'" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'weibull'" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'linear' (default)" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'median_unbiased'" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'normal_unbiased'" } ] } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The first three methods are discontinuous. For backward compatibility with previous versions of NumPy, the following discontinuous variations of the default 'linear' (7.) option are available:" } ] }, { "__type": "BulletList", "__tag": 4053, "ordered": false, "start": 1, "children": [ { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'lower'" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'higher'," } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'midpoint'" } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "'nearest'" } ] } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "See Notes for details." } ] }, { "__type": "Admonition", "__tag": 4056, "kind": "versionchanged", "base_type": "neutral", "children": [ { "__type": "AdmonitionTitle", "__tag": 4055, "children": [ { "__type": "Text", "__tag": 4046, "value": "versionchanged 1.22.0" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This argument was previously called \"interpolation\" and only offered the \"linear\" default and last four options." } ] } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "keepdims", "annotation": "bool, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "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 original array " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": "." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "weights", "annotation": "array_like, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "An array of weights associated with the values in " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": ". Each value in " }, { "__type": "ParamRef", "__tag": 4071, "name": "a" }, { "__type": "Text", "__tag": 4046, "value": " contributes to the quantile according to its associated weight. 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"__tag": 4050, "value": "import numpy as np\na = np.array([[10, 7, 4], [3, 2, 1]])\na\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "np.quantile(a, 0.5)\n", "execution_status": "failure" }, { "__type": "Code", "__tag": 4050, "value": "np.quantile(a, 0.5, axis=0)\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "np.quantile(a, 0.5, axis=1)\n", "execution_status": "failure" }, { "__type": "Code", "__tag": 4050, "value": "np.quantile(a, 0.5, axis=1, keepdims=True)\nm = np.quantile(a, 0.5, axis=0)\nout = np.zeros_like(m)\nnp.quantile(a, 0.5, axis=0, out=out)\nm\nb = a.copy()\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "np.quantile(b, 0.5, axis=1, overwrite_input=True)\n", "execution_status": "failure" }, { "__type": "Code", "__tag": 4050, "value": "assert not np.all(a == b)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nSee also `numpy.percentile` for a visualization of most methods." } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "mean", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "current-module", "version": "current-version", "kind": "to-resolve", "path": "mean" }, "kind": "module" }, "descriptions": [], "type": null }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "median", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "current-module", "version": "current-version", "kind": "to-resolve", "path": "median" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "equivalent to " }, { "__type": "InlineCode", "__tag": 4051, "value": "quantile(..., 0.5)" } ] } ], "type": null }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "nanquantile", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "current-module", "version": "current-version", "kind": "to-resolve", "path": "nanquantile" }, "kind": "module" }, "descriptions": [], "type": null }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "percentile", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "current-module", "version": "current-version", "kind": "to-resolve", "path": "percentile" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "equivalent to quantile, but with q in the range [0, 100]." } ] } ], "type": null } ], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "a", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", 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[1] R. J. Hyndman and Y. Fan,", " \"Sample quantiles in statistical packages,\"", " The American Statistician, 50(4), pp. 361-365, 1996" ], "qa": "numpy:quantile", "arbitrary": [], "local_refs": [ "a", "axis", "keepdims", "method", "out", "overwrite_input", "q", "quantile", "weights" ] }