min(a, axis=None, keepdims=False, split_every=None, out=None)

This docstring was copied from numpy.min.

Some inconsistencies with the Dask version may exist.

Notes

NaN values are propagated, that is if at least one item is NaN, the corresponding min value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmin.

Don't use :None:None:`amin` for element-wise comparison of 2 arrays; when a.shape[0] is 2, minimum(a[0], a[1]) is faster than amin(a, axis=0) .

Parameters

a : array_like

Input data.

axis : None or int or tuple of ints, optional

Axis or axes along which to operate. By default, flattened input is used.

versionadded

If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before.

out : ndarray, optional

Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See ufuncs-output-type for more details.

keepdims : bool, optional

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.

If the default value is passed, then keepdims will not be passed through to the :None:None:`amin` method of sub-classes of :None:None:`ndarray`, however any non-default value will be. If the sub-class' method does not implement keepdims any exceptions will be raised.

initial : scalar, optional (Not supported in Dask)

The maximum value of an output element. Must be present to allow computation on empty slice. See :None:None:`~numpy.ufunc.reduce` for details.

versionadded

where : array_like of bool, optional (Not supported in Dask)

Elements to compare for the minimum. See :None:None:`~numpy.ufunc.reduce` for details.

versionadded

Returns

amin : ndarray or scalar

Minimum of a. If :None:None:`axis` is None, the result is a scalar value. If :None:None:`axis` is given, the result is an array of dimension a.ndim - 1 .

Return the minimum of an array or minimum along an axis.

See Also

amax

The maximum value of an array along a given axis, propagating any NaNs.

argmin

Return the indices of the minimum values.

fmax

fmin

Element-wise minimum of two arrays, ignoring any NaNs.

maximum

minimum

Element-wise minimum of two arrays, propagating any NaNs.

nanmax

nanmin

The minimum value of an array along a given axis, ignoring any NaNs.

Examples

This example is valid syntax, but we were not able to check execution

>>> a = np.arange(4).reshape((2,2))  # doctest: +SKIP
... a  # doctest: +SKIP
array([[0, 1],
       [2, 3]])

This example is valid syntax, but we were not able to check execution

>>> np.amin(a)           # Minimum of the flattened array  # doctest: +SKIP
0

This example is valid syntax, but we were not able to check execution

>>> np.amin(a, axis=0)   # Minima along the first axis  # doctest: +SKIP
array([0, 1])

This example is valid syntax, but we were not able to check execution

>>> np.amin(a, axis=1)   # Minima along the second axis  # doctest: +SKIP
array([0, 2])

This example is valid syntax, but we were not able to check execution

>>> np.amin(a, where=[False, True], initial=10, axis=0)  # doctest: +SKIP
array([10,  1])

This example is valid syntax, but we were not able to check execution

>>> b = np.arange(5, dtype=float)  # doctest: +SKIP
... b[2] = np.NaN  # doctest: +SKIP
... np.amin(b)  # doctest: +SKIP
nan

This example is valid syntax, but we were not able to check execution

>>> np.amin(b, where=~np.isnan(b), initial=10)  # doctest: +SKIP
0.0

This example is valid syntax, but we were not able to check execution

>>> np.nanmin(b)  # doctest: +SKIP
0.0

This example is valid syntax, but we were not able to check execution

>>> np.amin([[-50], [10]], axis=-1, initial=0)  # doctest: +SKIP
array([-50,   0])

Notice that the initial value is used as one of the elements for which the minimum is determined, unlike for the default argument Python's max function, which is only used for empty iterables.

Notice that this isn't the same as Python's default argument.

This example is valid syntax, but we were not able to check execution

>>> np.amin([6], initial=5)  # doctest: +SKIP
5

This example is valid syntax, but we were not able to check execution

>>> min([6], default=5)  # doctest: +SKIP
6

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.reductions.min dask.array.reductions.make_arg_reduction.<locals>.wrapped dask.array.reductions.nanmin

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