{
  "__type": "IngestedDoc",
  "__tag": 4010,
  "_content": {
    "Notes": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "The data are assumed to be drawn from probability distributions "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "f(x)"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "f(x/s) / s"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " respectively, for some probability density function f. The null hypothesis is that "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "s == 1"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "For multi-dimensional arrays, if the inputs are of shapes "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "(n0, n1, n2, n3)"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "  and "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "(n0, m1, n2, n3)"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", then if "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "axis=1"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", the resulting z and p values will have shape "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "(n0, n2, n3)"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ".  Note that "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "n1"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "m1"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " don't have to be equal, but the other dimensions do."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Beginning in SciPy 1.9, "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.matrix"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " inputs (not recommended for new code) are converted to "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.ndarray"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " before the calculation is performed. In this case, the output will be a scalar or "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.ndarray"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " of appropriate shape rather than a 2D "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.matrix"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ". Similarly, while masked elements of masked arrays are ignored, the output will be a scalar or "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.ndarray"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " rather than a masked array with "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "mask=False"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__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": "mood",
              "reference": {
                "__type": "LocalRef",
                "__tag": 4022,
                "kind": "module",
                "path": "scipy.stats._morestats:mood"
              },
              "kind": "module"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "SCIPY_ARRAY_API=1"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported."
            }
          ]
        },
        {
          "__type": "Code",
          "__tag": 4050,
          "value": "====================  ====================  ====================\nLibrary               CPU                   GPU\n====================  ====================  ====================\nNumPy                 ✅                     n/a                 \nCuPy                  n/a                   ⛔                   \nPyTorch               ✅                     ✅                   \nJAX                   ✅                     ✅                   \nDask                  ⛔                     n/a                 \n====================  ====================  ====================",
          "execution_status": null
        },
        {
          "__type": "Blockquote",
          "__tag": 4059,
          "children": [
            {
              "__type": "Paragraph",
              "__tag": 4045,
              "children": [
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": "See "
                },
                {
                  "__type": "InlineRole",
                  "__tag": 4003,
                  "value": "dev-arrayapi",
                  "domain": null,
                  "role": "ref",
                  "inventory": null
                },
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": " for more information."
                }
              ]
            }
          ]
        }
      ],
      "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": "res",
              "annotation": "SignificanceResult",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "An object containing attributes:"
                    }
                  ]
                },
                {
                  "__type": "DefList",
                  "__tag": 4033,
                  "children": [
                    {
                      "__type": "DefListItem",
                      "__tag": 4037,
                      "dt": {
                        "__type": "Paragraph",
                        "__tag": 4045,
                        "children": [
                          {
                            "__type": "Text",
                            "__tag": 4046,
                            "value": "statistic"
                          }
                        ]
                      },
                      "dd": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": "statistic"
                            }
                          ]
                        }
                      ]
                    },
                    {
                      "__type": "DefListItem",
                      "__tag": 4037,
                      "dt": {
                        "__type": "Paragraph",
                        "__tag": 4045,
                        "children": [
                          {
                            "__type": "Text",
                            "__tag": 4046,
                            "value": "pvalue"
                          }
                        ]
                      },
                      "dd": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": "pvalue"
                            }
                          ]
                        }
                      ]
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Perform Mood's test for equal scale parameters."
            }
          ]
        }
      ],
      "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": "x, y",
              "annotation": "array_like",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Arrays of sample data. There must be at least three observations total."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "axis",
              "annotation": "int or None, default: 0",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "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 "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "None"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ", the input will be raveled before computing the statistic."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "alternative",
              "annotation": "{'two-sided', 'less', 'greater'}, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Defines the alternative hypothesis. Default is 'two-sided'. The following options 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": "'two-sided': the scales of the distributions underlying "
                            },
                            {
                              "__type": "ParamRef",
                              "__tag": 4071,
                              "name": "x"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": " and "
                            },
                            {
                              "__type": "ParamRef",
                              "__tag": 4071,
                              "name": "y"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": "   are different."
                            }
                          ]
                        }
                      ]
                    },
                    {
                      "__type": "ListItem",
                      "__tag": 4054,
                      "children": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": "'less': the scale of the distribution underlying "
                            },
                            {
                              "__type": "ParamRef",
                              "__tag": 4071,
                              "name": "x"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": " is less than   the scale of the distribution underlying "
                            },
                            {
                              "__type": "ParamRef",
                              "__tag": 4071,
                              "name": "y"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": "."
                            }
                          ]
                        }
                      ]
                    },
                    {
                      "__type": "ListItem",
                      "__tag": 4054,
                      "children": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": "'greater': the scale of the distribution underlying "
                            },
                            {
                              "__type": "ParamRef",
                              "__tag": 4071,
                              "name": "x"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": " is greater   than the scale of the distribution underlying "
                            },
                            {
                              "__type": "ParamRef",
                              "__tag": 4071,
                              "name": "y"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": "."
                            }
                          ]
                        }
                      ]
                    }
                  ]
                },
                {
                  "__type": "Admonition",
                  "__tag": 4056,
                  "kind": "versionadded",
                  "base_type": "neutral",
                  "children": [
                    {
                      "__type": "AdmonitionTitle",
                      "__tag": 4055,
                      "children": [
                        {
                          "__type": "Text",
                          "__tag": 4046,
                          "value": "versionadded 1.7.0"
                        }
                      ]
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "nan_policy",
              "annotation": "{'propagate', 'omit', 'raise'}",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Defines how to handle input NaNs."
                    }
                  ]
                },
                {
                  "__type": "BulletList",
                  "__tag": 4053,
                  "ordered": false,
                  "start": 1,
                  "children": [
                    {
                      "__type": "ListItem",
                      "__tag": 4054,
                      "children": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "InlineCode",
                              "__tag": 4051,
                              "value": "propagate"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": ": 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."
                            }
                          ]
                        }
                      ]
                    },
                    {
                      "__type": "ListItem",
                      "__tag": 4054,
                      "children": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "InlineCode",
                              "__tag": 4051,
                              "value": "omit"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": ": 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."
                            }
                          ]
                        }
                      ]
                    },
                    {
                      "__type": "ListItem",
                      "__tag": 4054,
                      "children": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "InlineCode",
                              "__tag": 4051,
                              "value": "raise"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": ": if a NaN is present, a "
                            },
                            {
                              "__type": "InlineCode",
                              "__tag": 4051,
                              "value": "ValueError"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": " will be raised."
                            }
                          ]
                        }
                      ]
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "keepdims",
              "annotation": "bool, default: False",
              "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 input array."
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Extended Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Mood's two-sample test for scale parameters is a non-parametric test for the null hypothesis that two samples are drawn from the same distribution with the same scale parameter."
            }
          ]
        }
      ],
      "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/_morestats.py",
  "item_line": 3565,
  "item_type": "function",
  "aliases": [
    "scipy.stats.mood"
  ],
  "example_section_data": {
    "__type": "Section",
    "__tag": 4015,
    "children": [
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "import numpy as np\nfrom scipy import stats\nrng = np.random.default_rng()\nx2 = rng.standard_normal((2, 45, 6, 7))\nx1 = rng.standard_normal((2, 30, 6, 7))\nres = stats.mood(x1, x2, axis=1)\nres.pvalue.shape\n",
        "execution_status": "success"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nFind the number of points where the difference in scale is not significant:\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "(res.pvalue > 0.1).sum()\n",
        "execution_status": "failure"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nPerform the test with different scales:\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "x1 = rng.standard_normal((2, 30))\nx2 = rng.standard_normal((2, 35)) * 10.0\n",
        "execution_status": "success"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "stats.mood(x1, x2, axis=1)\n",
        "execution_status": "failure"
      }
    ],
    "title": [],
    "level": 0,
    "target": null
  },
  "see_also": [
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "ansari",
        "reference": {
          "__type": "LocalRef",
          "__tag": 4022,
          "kind": "module",
          "path": "scipy.stats._morestats:ansari"
        },
        "kind": "module"
      },
      "descriptions": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "A non-parametric test for the equality of 2 variances"
            }
          ]
        }
      ],
      "type": "func"
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "bartlett",
        "reference": {
          "__type": "RefInfo",
          "__tag": 4000,
          "module": "current-module",
          "version": "current-version",
          "kind": "to-resolve",
          "path": "bartlett"
        },
        "kind": "module"
      },
      "descriptions": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "A parametric test for equality of k variances in normal samples"
            }
          ]
        }
      ],
      "type": "func"
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "fligner",
        "reference": {
          "__type": "LocalRef",
          "__tag": 4022,
          "kind": "module",
          "path": "scipy.stats._morestats:fligner"
        },
        "kind": "module"
      },
      "descriptions": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "A non-parametric test for the equality of k variances"
            }
          ]
        }
      ],
      "type": "func"
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "levene",
        "reference": {
          "__type": "LocalRef",
          "__tag": 4022,
          "kind": "module",
          "path": "scipy.stats._morestats:levene"
        },
        "kind": "module"
      },
      "descriptions": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "A parametric test for equality of k variances"
            }
          ]
        }
      ],
      "type": "func"
    }
  ],
  "signature": {
    "__type": "SignatureNode",
    "__tag": 4029,
    "kind": "function",
    "parameters": [
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "x",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": {
          "__type": "Empty",
          "__tag": 4031
        }
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "y",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": {
          "__type": "Empty",
          "__tag": 4031
        }
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "axis",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "0"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "alternative",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "two-sided"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "nan_policy",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "KEYWORD_ONLY",
        "default": "propagate"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "keepdims",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "KEYWORD_ONLY",
        "default": "False"
      }
    ],
    "return_annotation": {
      "__type": "Empty",
      "__tag": 4031
    },
    "target_name": "mood"
  },
  "references": [
    "[1] Mielke, Paul W. \"Note on Some Squared Rank Tests with Existing Ties.\"",
    "    Technometrics, vol. 9, no. 2, 1967, pp. 312-14. JSTOR,",
    "    https://doi.org/10.2307/1266427. Accessed 18 May 2022."
  ],
  "qa": "scipy.stats._morestats:mood",
  "arbitrary": [],
  "local_refs": [
    "alternative",
    "axis",
    "keepdims",
    "nan_policy",
    "res",
    "x",
    "y"
  ]
}