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 = )

# 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.] 




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