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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