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# scipy.stats.tmin

scipy.stats.tmin(a, lowerlimit=None, axis=0, inclusive=True, nan_policy='propagate', *, keepdims=False)

  Compute the trimmed minimum.

  This function finds the minimum value of an array a along the specified axis, but only considering values greater than a specified lower limit.

  Parameters:
    a array_like

      Array of values.

    lowerlimit None or float, optional

      Values in the input array less than the given limit will be ignored. When lowerlimit is None, then all values are used. The default value is None.

    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.

    inclusive{True, False}, optional

      This flag determines whether values exactly equal to the lower limit are included. The default value is True.

    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:
    tmin float, int or ndarray

      Trimmed minimum.

  Notes

  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`.

  Examples

      >>> import numpy as np
      >>> from scipy import stats
      >>> x = np.arange(20)
      >>> stats.tmin(x)
      0

      >>> stats.tmin(x, 13)
      13

      >>> stats.tmin(x, 13, inclusive=False)
      14