In general, arrays can have data types that contain fields similar to columns in a spreadsheet. An example is
[(a, int), (b, float)] , where each entry in the array is a pair (int, float). Typically, these attributes are accessed using dictionary searches such as
arr [& # 39; a & # 39;] and arr [& # 39; b & # 39;] .
Arrays of records allow you to access fields as elements of an array using
arr.a and arr.b
numpy.recarray.any () strong> returns True if any elements in the array of records are True.
numpy.recarray.any (axis = None, out = None, keepdims = False)
axis: [None or int or tuple of ints, optional ] Axis or axes along which a logical OR operation is performed.
out: [ndarray, optional] A location into which the result is stored.
– & gt; If provided, it must have a shape that the inputs broadcast to.
– & gt; 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 any 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 any elements evaluate to True. p>
Code # 1:
Input array: [(5.0, 2) (3.0, 4)] Record array of float: [5. 3.] Output array: True Record array of int: [2 4] Output array: True
Code # 2:
If we use
numpy.recarray.any () to the entire array of records, this will because the array is flexible or mixed.
TypeError: cannot perform reduce with flexible type