# 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.

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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