nanmin(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)

Notes

NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.

If the input has a integer type the function is equivalent to np.min.

Parameters

a : array_like

Array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted.

axis : {int, tuple of int, None}, optional

Axis or axes along which the minimum is computed. The default is to compute the minimum of the flattened array.

out : ndarray, optional

Alternate output array in which to place the result. The default is None ; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details.

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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 original a.

If the value is anything but the default, then :None:None:`keepdims` will be passed through to the :None:None:`min` method of sub-classes of ndarray . If the sub-classes methods does not implement :None:None:`keepdims` any exceptions will be raised.

versionadded

initial : scalar, optional

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

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where : array_like of bool, optional

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

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Returns

nanmin : ndarray

An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as a is returned.

Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and Nan is returned for that slice.

See Also

amax

amin

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

fmax

fmin

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

isfinite

Shows which elements are neither NaN nor infinity.

isnan

Shows which elements are Not a Number (NaN).

maximum

minimum

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

nanmax

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

Examples

>>> a = np.array([[1, 2], [3, np.nan]])
... np.nanmin(a)
1.0

>>> np.nanmin(a, axis=0)
array([1.,  2.])

>>> np.nanmin(a, axis=1)
array([1.,  3.])

When positive infinity and negative infinity are present:

>>> np.nanmin([1, 2, np.nan, np.inf])
1.0

>>> np.nanmin([1, 2, np.nan, np.NINF])
-inf

See :

Back References

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

numpy.amin dask.array.reductions.min numpy.ma.core.maximum numpy.ma.core.minimum dask.array.reductions.make_arg_reduction.<locals>.wrapped numpy.amax numpy.nanmax numpy.lib.nanfunctions dask.array.reductions.nanmin

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