numpy.nanmax#

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

Return the maximum of an array or maximum along an axis, ignoring anyNaNs. When all-NaN slices are encountered aRuntimeWarning israised and NaN is returned for that slice.

Parameters:
aarray_like

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

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

Axis or axes along which the maximum is computed. The default is to computethe maximum 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 themax methodof sub-classes ofndarray. If the sub-classes methodsdoes not implementkeepdims any exceptions will be raised.

initialscalar, optional

The minimum 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 maximum. Seereducefor details.

New in version 1.22.0.

Returns:
nanmaxndarray

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

See also

nanmin

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

amax

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

fmax

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

maximum

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

isnan

Shows which elements are Not a Number (NaN).

isfinite

Shows which elements are neither NaN nor infinity.

amin,fmin,minimum

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.max.

Examples

>>>importnumpyasnp>>>a=np.array([[1,2],[3,np.nan]])>>>np.nanmax(a)3.0>>>np.nanmax(a,axis=0)array([3.,  2.])>>>np.nanmax(a,axis=1)array([2.,  3.])

When positive infinity and negative infinity are present:

>>>np.nanmax([1,2,np.nan,-np.inf])2.0>>>np.nanmax([1,2,np.nan,np.inf])inf
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