# xarray.ufuncs.fmin

### xarray.ufuncs.fmin *= <xarray.ufuncs._binary_ufunc object>*

xarray specific variant of `numpy.fmin()`. Handles xarray objects by dispatching to the appropriate function for the underlying array type.

Documentation from numpy:

Element-wise minimum of array elements.

Compare two arrays and return a new array containing the element-wise
minima. If one of the elements being compared is a NaN, then the
non-nan 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 ignored when possible.

* **Parameters:**
  * **x1, x2** ([array_like](https://numpy.org/doc/stable/glossary.html#term-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`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray), [`None`](https://docs.python.org/3/builtins/constants.html#None), or [`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple) of [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) and [`None`](https://docs.python.org/3/builtins/constants.html#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](https://numpy.org/doc/stable/glossary.html#term-array_like), *optional*) – This condition is broadcast over the input. At locations where the
    condition is True, the out array will be set to the ufunc result.
    Elsewhere, the out array will retain its original value.
    Note that if an uninitialized 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](https://numpy.org/doc/stable/reference/ufuncs.html#ufuncs-kwargs).
* **Returns:**
  **y** ([`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) or [scalar](../user-guide/terminology.html.md#term-scalar)) – The minimum of x1 and x2, element-wise.
  This is a scalar if both x1 and x2 are scalars.

#### SEE ALSO
[`fmax`](xarray.ufuncs.fmax.html.md#xarray.ufuncs.fmax)
: Element-wise maximum of two arrays, ignores NaNs.

[`minimum`](xarray.ufuncs.minimum.html.md#xarray.ufuncs.minimum)
: Element-wise minimum of two arrays, propagates NaNs.

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

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

[`maximum`](xarray.ufuncs.maximum.html.md#xarray.ufuncs.maximum), `amax`, `nanmax`

### Notes

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

### Examples

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```pycon
>>> import numpy as np
>>> np.fmin([2, 3, 4], [1, 5, 2])
array([1, 3, 2])
```

```pycon
>>> np.fmin(np.eye(2), [0.5, 2])
array([[ 0.5,  0. ],
       [ 0. ,  1. ]])
```

```pycon
>>> np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan])
array([ 0.,  0., nan])
```

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