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 geekimport 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 arrayout_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 verticalout_arr2 = rec_arr.a.mean(axis = 0)# Mean along 0 axisprint("\nMean of float record array, axis = 0 : ", out_arr2) # applying recarray.mean methods# to float record array along axis 1# i, e along horizontalout_arr3 = rec_arr.a.mean(axis = 1)# Mean along 0 axisprint("\nMean of float record array, axis = 1 : ", out_arr3) # applying recarray.mean methods# to int record array along default axis # i, e along flattened arrayout_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 verticalout_arr5 = rec_arr.b.mean(axis = 0)# Mean along 0 axisprint("\nMean of int record array, axis = 0 : ", out_arr5) # applying recarray.mean methods# to int record array along axis 1# i, e along horizontalout_arr6 = rec_arr.b.mean(axis = 1)# Mean along 0 axisprint("\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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