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.argpartition() function returns the indices that would partition this array.
Syntax :
numpy.recarray.argpartition(kth, axis=-1, kind='introselect', order=None)Parameters:
kth : [int or sequence of ints ] Element index to partition by.
axis : [int or None] Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis.
kind : Selection algorithm. Default is ‘introselect’.
order : [str or list of str] When arr is an array with fields defined, this argument specifies which fields to compare first, second, etc.Return : [index_array, ndarray] Array of indices that partition arr along the specified axis.
Code #1 :
# Python program explaining # numpy.recarray.argpartition() 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, -4), (6.0, 9)], [(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.argpartition methods # to float record array along axis 1 out_arr = geek.recarray.argpartition(rec_arr.a, kth = 1, axis = 1) print ("Output partitioned array indices along axis 1: ", out_arr) # applying recarray.argpartition methods # to int record array along axis 0 out_arr = geek.recarray.argpartition(rec_arr.b, kth = 1, axis = 0) print ("Output partitioned array indices array along axis 0: ", out_arr) |
Input array : [[(5.0, 2) (3.0, -4) (6.0, 9)] [(9.0, 1) (5.0, 4) (-12.0, -7)]] Record array of float: [[ 5. 3. 6.] [ 9. 5. -12.]] Record array of int: [[ 2 -4 9] [ 1 4 -7]] Output partitioned array indices along axis 1: [[1 0 2] [2 1 0]] Output partitioned array indices array along axis 0: [[1 0 1] [0 1 0]]
Code #2 :
We are applying numpy.recarray.argpartition() to whole record array.
# Python program explaining # numpy.recarray.argpartition() 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, 4), (6.0, -7)], [(9.0, 1), (6.0, 4), (-2.0, -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) # applying recarray.argpartition methods to record array out_arr = geek.recarray.argpartition(rec_arr, kth = 2) print ("Output array : ", out_arr) |
Input array : [[(5.0, 2) (3.0, 4) (6.0, -7)] [(9.0, 1) (6.0, 4) (-2.0, -7)]] Output array : [[1 0 2] [2 1 0]]
