** numpy.recarray.mean() ** returns the average of the array elements along the given axis.

Syntax:`numpy.recarray.mean (axis = None, dtype = None, out = None, keepdims = False)`

Parameters:

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.

- & 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.

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: **

` ` |

** Output: **

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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