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# Numpy recarray.mean() function | Python

• Last Updated : 27 Sep, 2019

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.

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.
-> 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 :

 `# Python program explaining``# numpy.recarray.mean() method `` ` `# importing numpy as geek``import` `numpy as geek`` ` `# creating input array with 2 different field ``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 a record array,``# 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``# to float record array along default axis ``# i, e along flattened array``out_arr1 ``=` `rec_arr.a.mean()``# Mean of the flattened array ``print``(``"\nMean of float record array, axis = None : "``, out_arr1) `` ` ` ` `# applying recarray.mean methods``# to float record array along axis 0``# i, e along vertical``out_arr2 ``=` `rec_arr.a.mean(axis ``=` `0``)``# Mean along 0 axis``print``(``"\nMean of float record array, axis = 0 : "``, out_arr2)`` ` ` ` `# applying recarray.mean methods``# to float record array along axis 1``# i, e along horizontal``out_arr3 ``=` `rec_arr.a.mean(axis ``=` `1``)``# Mean along 0 axis``print``(``"\nMean of float record array, axis = 1 : "``, out_arr3)`` ` ` ` `# applying recarray.mean methods``# to int record array along default axis ``# i, e along flattened array``out_arr4 ``=` `rec_arr.b.mean(dtype ``=``'int'``)``# Mean of the flattened array ``print``(``"\nMean of int record array, axis = None : "``, out_arr4) `` ` ` ` `# applying recarray.mean methods``# to int record array along axis 0``# i, e along vertical``out_arr5 ``=` `rec_arr.b.mean(axis ``=` `0``)``# Mean along 0 axis``print``(``"\nMean of int record array, axis = 0 : "``, out_arr5)`` ` ` ` `# applying recarray.mean methods``# to int record array along axis 1``# i, e along horizontal``out_arr6 ``=` `rec_arr.b.mean(axis ``=` `1``)``# Mean along 0 axis``print``(``"\nMean 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.]
```

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