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Module « scipy.stats »
Signature de la fonction tmin
def tmin(a, lowerlimit=None, axis=0, inclusive=True, nan_policy='propagate', *, keepdims=False)
Description
help(scipy.stats.tmin)
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
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