Numpy recarray.mean() function | Python
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.mean() function returns the average of the array elements along given axis.
numpy.recarray.mean(axis=None, dtype=None, out=None, keepdims=False)
axis : [None or int or tuple of ints, optional] Axis or axes along which to operate. By default, flattened input is used.
dtype : [data-type, optional] Type we desire while computing mean.
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.
Return : [ndarray or scalar] Arithmetic mean of the array (a scalar value if axis is none) or array with mean values along specified axis.
Code #1 :
Input array : [[( 5., 2) ( 3., 6) ( 6., 10)] [( 9., 1) ( 5., 4) (-12., 7)]] Record array of float: [[ 5. 3. 6.] [ 9. 5. -12.]] Record array of int: [[ 2 6 10] [ 1 4 7]] Mean of float record array, axis = None : 2.6666666666666665 Mean of float record array, axis = 0 : [ 7. 4. -3.] Mean of float record array, axis = 1 : [4.66666667 0.66666667] Mean of int record array, axis = None : 5 Mean of int record array, axis = 0 : [1.5 5. 8.5] Mean of int record array, axis = 1 : [6. 4.]
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