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scipy.stats.

# trim_mean

scipy.stats.trim_mean(a, proportiontocut, axis=0, *, nan_policy='propagate', keepdims=False)

  Return mean of array after trimming a specified fraction of extreme values.

  Removes the specified proportion of elements from each end of the sorted array, then computes the mean of the remaining elements.

  Parameters:
    a array_like

      Input array.

    proportiontocut float

      Fraction of the most positive and most negative elements to remove. When the specified proportion does not result in an integer number of elements, the number of elements to trim is rounded down.

    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.

    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.

    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:
    trim_mean ndarray

      Mean of trimmed array.

  See also

  `trimboth`

    Remove a proportion of elements from each end of an array.

  `tmean`

    Compute the mean after trimming values outside specified limits.

  Notes

  For 1-D array a, `trim_mean` is approximately equivalent to the following calculation:

      import numpy as np
      a = np.sort(a)
      m = int(proportiontocut * len(a))
      np.mean(a[m: len(a) - m])

  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`.

  Array API Standard Support

  `trim_mean` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ✅
  PyTorch | ✅ | ✅
  JAX | ✅ | ✅
  Dask | ✅ | n/a

  `trim_mean` also accepts MArrays backed by the backends indicated above; masked values will be treated as though they were not present.

  See Support for the array API standard for more information.

  Examples

      >>> import numpy as np
      >>> from scipy import stats
      >>> x = [1, 2, 3, 5]
      >>> stats.trim_mean(x, 0.25)
      2.5

  When the specified proportion does not result in an integer number of elements, the number of elements to trim is rounded down.

      >>> stats.trim_mean(x, 0.24999) == np.mean(x)
      True

  Use axis to specify the axis along which the calculation is performed.

      >>> x2 = [[1, 2, 3, 5],
      ...       [10, 20, 30, 50]]
      >>> stats.trim_mean(x2, 0.25)
      array([ 5.5, 11. , 16.5, 27.5])
      >>> stats.trim_mean(x2, 0.25, axis=1)
      array([ 2.5, 25. ])