nums.numpy.nanmax

nums.numpy.nanmax(a, axis=None, out=None, keepdims=False)[source]

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

This docstring was copied from numpy.nanmax.

Some inconsistencies with the NumS version may exist.

Parameters

  • a (BlockArray) – Array containing numbers whose maximum 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 maximum is computed. The default is to compute the maximum of the flattened array.

  • out (BlockArray, 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.

  • 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 keepdims will be passed through to the max method of sub-classes of BlockArray. If the sub-classes methods does not implement keepdims any exceptions will be raised.

Returns

nanmax – 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 BlockArray scalar is returned. The same dtype as a is returned.

Return type

BlockArray

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

If the input has a integer type the function is equivalent to nps.max.

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array([[1, 2], [3, nps.nan]])  
>>> nps.nanmax(a).get()  
array(3.)
>>> nps.nanmax(a, axis=0).get()  
array([3.,  2.])
>>> nps.nanmax(a, axis=1).get()  
array([2.,  3.])