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.all() function returns True if all elements in record array evaluate to True.
Syntax :
numpy.recarray.all(axis=None, out=None, keepdims=False)Parameters:
axis : [ None or int or tuple of ints, optional] Axis or axes along which a logical AND reduction is performed.
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. With this option, the result will broadcast correctly against the input array.
If the default value is passed, then keepdims will not be passed through to all method of sub-classes of ndarray, however any non-default value will be. If the sub-classes sum method does not implement keepdims any exceptions will be raised.Returns : [ndarray, bool] It returns True if all elements evaluate to True.
Code #1 :
# Python program explaining # numpy.recarray.all() 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)], 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.all methods to float record array out_arr = geek.recarray.all(rec_arr.a) print ("Output array: ", out_arr) # applying recarray.all methods to int record array out_arr = geek.recarray.all(rec_arr.b) print ("Output array: ", out_arr) |
Input array : [(5.0, 2) (3.0, 4)] Record array of float: [ 5. 3.] Record array of int: [2 4] Output array: True Output array: True
Code #2 :
If we apply numpy.recarray.all() to whole record array then it will give Type error because the array is flexible or mixed type.
# Python program explaining # numpy.recarray.all() 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)], 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 ", rec_arr) # applying recarray.all methods to record array out_arr = geek.recarray.all(rec_arr) print ("Output array: ", out_arr) |
TypeError: cannot perform reduce with flexible type
