In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The
numpy.ma module provides a convenient way to address this issue, by introducing masked arrays. Masked arrays are arrays that may have missing or invalid entries.
numpy.MaskedArray.all() function returns True if all elements evaluate to True.
MaskedArray.all(axis=None, out=None, keepdims)
axis : [int or None] Axis or axes along which a logical AND reduction is performed.
out : [ndarray, optional] A location into which the result is stored.
-> If provided, it must have a shape that the inputs broadcast to.
-> If not provided or None, a freshly-allocated array is returned.
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 input array.
Return : [ndarray, bool] A new boolean or array is returned unless out is specified, in which case a reference to out is returned.
Code #1 :
Input array : [ 1 2 3 -1 5] Output array : True Masked array : [1 2 -- -1 5] Output array : True
Code #2 :
Input array : [ 1 2 3 -1 5] Masked array : [-- -- -- -- --] Output array : --
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