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scipy.stats.mstats.

# trimmed_mean_ci

scipy.stats.mstats.trimmed_mean_ci(data, limits=(0.2, 0.2), inclusive=(True, True), alpha=0.05, axis=None)

  Selected confidence interval of the trimmed mean along the given axis.

  Parameters:
    dataarray_like

      Input data.

    limits{None, tuple}, optional

      None or a two item tuple. Tuple of the percentages to cut on each side of the array, with respect to the number of unmasked data, as floats between 0. and 1. If `n` is the number of unmasked data before trimming, then (`n * limits[0]`)th smallest data and (`n * limits[1]`)th largest data are masked. The total number of unmasked data after trimming is `n * (1. - sum(limits))`. The value of one limit can be set to None to indicate an open interval.

      Defaults to (0.2, 0.2).

    inclusive(2,) tuple of boolean, optional

      If relative==False, tuple indicating whether values exactly equal to the absolute limits are allowed. If relative==True, tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False).

      Defaults to (True, True).

    alphafloat, optional

      Confidence level of the intervals.

      Defaults to 0.05.

    axisint, optional

      Axis along which to cut. If None, uses a flattened version of data.

      Defaults to None.

  Returns:
    trimmed_mean_ci(2,) ndarray

      The lower and upper confidence intervals of the trimmed data.