minimum(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Some inconsistencies with the Dask version may exist.

Element-wise minimum of array elements.

Compare two arrays and returns a new array containing the element-wise minima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated.

Notes

The minimum is equivalent to np.where(x1 <= x2, x1, x2) when neither x1 nor x2 are NaNs, but it is faster and does proper broadcasting.

Parameters

x1, x2 : array_like

The arrays holding the elements to be compared. If x1.shape != x2.shape , they must be broadcastable to a common shape (which becomes the shape of the output).

out : ndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

where : array_like, optional

This condition is broadcast over the input. At locations where the condition is True, the :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`out` array is created via the default out=None , locations within it where the condition is False will remain uninitialized.

**kwargs :

For other keyword-only arguments, see the ufunc docs <ufuncs.kwargs> .

Returns

y : ndarray or scalar

The minimum of :None:None:`x1` and :None:None:`x2`, element-wise. This is a scalar if both :None:None:`x1` and :None:None:`x2` are scalars.

This docstring was copied from numpy.minimum.

See Also

amax

amin

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

fmax

fmin

Element-wise minimum of two arrays, ignores NaNs.

maximum

Element-wise maximum of two arrays, propagates NaNs.

nanmax

nanmin

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

Examples

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

>>> np.minimum([2, 3, 4], [1, 5, 2])  # doctest: +SKIP
array([1, 3, 2])

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

>>> np.minimum(np.eye(2), [0.5, 2]) # broadcasting  # doctest: +SKIP
array([[ 0.5,  0. ],
       [ 0. ,  1. ]])

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

>>> np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan])  # doctest: +SKIP
array([nan, nan, nan])

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

>>> np.minimum(-np.Inf, 1)  # doctest: +SKIP
-inf

See :

Back References

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

dask.array.reductions.nanmax dask.array.ufunc.fmin dask.array.reductions.min dask.array.reductions.max dask.array.ufunc.fmax dask.array.reductions.make_arg_reduction.<locals>.wrapped dask.array.reductions.nanmin dask.array.ufunc.maximum

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: /dask/array/ufunc.py#None
type: <class 'dask.array.ufunc.ufunc'>
Commit: