In numpy, arrays may have a data-types containing fields, analogous to columns in a spreadsheet. An example is
[(a, int), (b, float)], where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as
arr['a'] and arr['b'].
Record arrays allow the fields to be accessed as members of the array, using
arr.a and arr.b.
numpy.recarray.all() function returns True if all elements in record array evaluate to True.
numpy.recarray.all(axis=None, out=None, keepdims=False)
axis : [ None or int or tuple of ints, optional] 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.
If the default value is passed, then keepdims will not be passed through to all method of sub-classes of ndarray, however any non-default value will be. If the sub-classes sum method does not implement keepdims any exceptions will be raised.
Returns : [ndarray, bool] It returns True if all elements evaluate to True.
Code #1 :
Input array : [(5.0, 2) (3.0, 4)] Record array of float: [ 5. 3.] Record array of int: [2 4] Output array: True Output array: True
Code #2 :
If we apply
numpy.recarray.all() to whole record array then it will give
Type error because the array is flexible or mixed type.
TypeError: cannot perform reduce with flexible type
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