Skip to content
Related Articles

Related Articles

Improve Article
Numpy recarray.max() function | Python
  • Last Updated : 27 Sep, 2019

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.max() function returns the maximum of record array or maximum along an axis.

Syntax : numpy.recarray.max(axis=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.
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.

Return : [ndarray or scalar] Maximum of record array. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension arr.ndim – 1.



Code #1 :




# Python program explaining
# numpy.recarray.max() 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, 8)],
                     [(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.max methods
# to float record array along default axis 
# i, e along flattened array
out_arr1 = rec_arr.a.max()
# Maximum of the flattened array 
print("\nMax of float record array, axis = None : ", out_arr1) 
  
  
# applying recarray.max methods
# to float record array along axis 0
# i, e along vertical
out_arr2 = rec_arr.a.max(axis = 0)
# Maximum along 0 axis
print("\nMax of float record array, axis = 0 : ", out_arr2)
  
  
# applying recarray.max methods
# to float record array along axis 1
# i, e along horizontal
out_arr3 = rec_arr.a.max(axis = 1)
# Maximum along 0 axis
print("\nMax of float record array, axis = 1 : ", out_arr3)
  
  
# applying recarray.max methods
# to int record array along default axis 
# i, e along flattened array
out_arr4 = rec_arr.b.max()
# Maximum of the flattened array 
print("\nMax of int record array, axis = None : ", out_arr4) 
  
  
# applying recarray.max methods
# to int record array along axis 0
# i, e along vertical
out_arr5 = rec_arr.b.max(axis = 0)
# Maximum along 0 axis
print("\nMax of int record array, axis = 0 : ", out_arr5)
  
  
# applying recarray.max methods
# to int record array along axis 1
# i, e along horizontal
out_arr6 = rec_arr.b.max(axis = 1)
# Maximum along 0 axis
print("\nMax of int record array, axis = 1 : ", out_arr6)
Output:
Input array :  [[(  5.,  2) (  3., -4) (  6.,  8)]
 [(  9.,  1) (  5.,  4) (-12., -7)]]
Record array of float:  [[  5.   3.   6.]
 [  9.   5. -12.]]
Record array of int:  [[ 2 -4  8]
 [ 1  4 -7]]

Max of float record array, axis = None :  9.0

Max of float record array, axis = 0 :  [9. 5. 6.]

Max of float record array, axis = 1 :  [6. 9.]

Max of int record array, axis = None :  8

Max of int record array, axis = 0 :  [2 4 8]

Max of int record array, axis = 1 :  [8 4]

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course




My Personal Notes arrow_drop_up
Recommended Articles
Page :