numpy.nanmin#

numpy.nanmin(a,axis=None,out=None,keepdims=<novalue>,initial=<novalue>,where=<novalue>)[source]#

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

Parameters:
aarray_like

Array containing numbers whose minimum is desired. Ifa is not anarray, a conversion is attempted.

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

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

outndarray, optional

Alternate output array in which to place the result. The defaultisNone; if provided, it must have the same shape as theexpected output, but the type will be cast if necessary. SeeOutput type determination for more details.

keepdimsbool, optional

If this is set to True, the axes which are reduced are leftin the result as dimensions with size one. With this option,the result will broadcast correctly against the originala.

If the value is anything but the default, thenkeepdims will be passed through to themin methodof sub-classes ofndarray. If the sub-classes methodsdoes not implementkeepdims any exceptions will be raised.

initialscalar, optional

The maximum value of an output element. Must be present to allowcomputation on empty slice. Seereduce for details.

New in version 1.22.0.

wherearray_like of bool, optional

Elements to compare for the minimum. Seereducefor details.

New in version 1.22.0.

Returns:
nanminndarray

An array with the same shape asa, with the specified axisremoved. Ifa is a 0-d array, or if axis is None, an ndarrayscalar is returned. The same dtype asa is returned.

See also

nanmax

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

amin

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

fmin

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

minimum

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

isnan

Shows which elements are Not a Number (NaN).

isfinite

Shows which elements are neither NaN nor infinity.

amax,fmax,maximum

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 negativeinfinity is treated as a very small (i.e. negative) number.

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

Examples

>>>importnumpyasnp>>>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.inf])-inf
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