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) |
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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