# Numpy recarray.mean () function | python

`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:

 ` # Python program explaining ` ` # numpy.recarray.mean () method `   ` # import numy as geek ` ` import ` ` numpy as geek `   ` # create an input array with two different fields ` ` in_arr ` ` = ` ` geek.array ([[(` ` 5.0 ` `, ` ` 2 ` `), (` ` 3.0 ` `, ` ` 6 ` `), (` ` 6.0 ` `, ` ` 10 ` `)] , ` ` [(` ` 9.0 ` `, ` ` 1 ` `), (` ` 5.0 ` `, ` ` 4 ` `), (` ` - ` ` 12.0 ` `, ` ` 7 ` `)]], ` ` dtype ` ` = ` ` [(` ` 'a' ` `, ` ` float ` `), (` ` 'b' ` `, ` ` int ` `)]) `   ` print ` ` (` `" Input array: "` `, in_arr) ` ` `  ` # convert it to an array of posts, ` ` # using arr.view (np.recarray) ` ` rec_arr ` ` = ` ` in_arr.view (geek.recarray) ` ` print ` ` (` ` "Record array of float:" ` `, rec_arr.a) ` ` print ` ` (` ` "Record array of int:" ` `, rec_arr.b) `   ` # applying recarray.mean methods ` ` # place an array of posts along the default axis ` ` # i, e along the flattened array ` ` out_arr1 ` ` = ` ` rec_arr.a.mean () ` ` # Flat array mean ` ` print ` ` (` `" Mean of float record array, axis = None: "` `, out_arr1) `     ` # application methods recarray.mean ` ` # place the array behind letters along the 0 axis ` ` # i, e along the vertical ` ` out_arr2 ` ` = ` ` rec_arr.a.mean (axis ` ` = ` ` 0 ` `) ` ` # Axis 0 average ` ` print ` ` (` ` "Mean of float record array, axis = 0:" ` `, out_arr2) `     ` # using recarray.mean methods ` ` # place an array of records along axis 1 ` ` # i, e along the horizontal ` ` out_arr3 ` ` = ` ` rec_arr.a.mean (axis ` ` = ` ` 1 ` `) ` ` # Axis 0 average ` ` print ` ` (` ` "Mean of float record array, axis = 1:" ` `, out_arr3) `     ` # applying recarray.mean methods ` ` # into an array of int records along the default axis ` ` # i, e along the flattened array ` ` out_arr4 ` ` = ` ` rec_arr.b.mean (dtype ` ` = ` ` 'int' ` `) ` ` # Flat array mean ` ` print ` ` (` ` "Mean of int record array, axis = None:" , out_arr4) ``     # applying recarray.mean methods # into an array of int records along the 0 axis # I, e along the vertical out_arr5 = rec_arr.b.mean (axis = 0 ) # Axis 0 average print ( "Mean of int record array, axis = 0:" , out_arr5)     # applying recarray.mean methods # into an array of int records along the axis 1 # i, e along the horizontal out_arr6 = rec_arr.b.mean (axis = 1 ) # Axis 0 average print ( "Mean of int record array, axis = 1:" , out_arr6) `

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.] `