numpy.ma.max#
- ma.max(obj, axis=None, out=None, fill_value=None, keepdims=<no value>)[source]#
Return the maximum along a given axis.
- Parameters:
- axisNone or int or tuple of ints, optional
Axis along which to operate. By default,
axis
is None and the flattened input is used. .. versionadded:: 1.7.0 If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before.- outarray_like, optional
Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output.
- fill_valuescalar or None, optional
Value used to fill in the masked values. If None, use the output of maximum_fill_value().
- keepdimsbool, 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 array.
- Returns:
- amaxarray_like
New array holding the result. If
out
was specified,out
is returned.
See also
ma.maximum_fill_value
Returns the maximum filling value for a given datatype.
Examples
>>> import numpy.ma as ma >>> x = [[-1., 2.5], [4., -2.], [3., 0.]] >>> mask = [[0, 0], [1, 0], [1, 0]] >>> masked_x = ma.masked_array(x, mask) >>> masked_x masked_array( data=[[-1.0, 2.5], [--, -2.0], [--, 0.0]], mask=[[False, False], [ True, False], [ True, False]], fill_value=1e+20) >>> ma.max(masked_x) 2.5 >>> ma.max(masked_x, axis=0) masked_array(data=[-1.0, 2.5], mask=[False, False], fill_value=1e+20) >>> ma.max(masked_x, axis=1, keepdims=True) masked_array( data=[[2.5], [-2.0], [0.0]], mask=[[False], [False], [False]], fill_value=1e+20) >>> mask = [[1, 1], [1, 1], [1, 1]] >>> masked_x = ma.masked_array(x, mask) >>> ma.max(masked_x, axis=1) masked_array(data=[--, --, --], mask=[ True, True, True], fill_value=1e+20, dtype=float64)