Insert a new axis within a NumPy array

Calculate the difference between the maximum and the minimum values of a given NumPy array along the second axis

Last Updated : 02 Sep, 2020

Let’s see how to calculate the difference between the maximum and the minimum values of a given NumPy array along the second axis. Here, the Second axis means row-wise.

So firstly for finding the row-wise maximum and minimum elements in a NumPy array we are using numpy.amax() and numpy.amin() functions of NumPy library respectively. then After that we simply perform subtraction on it.

numpy.amax(): This function returns maximum of an array or maximum along axis(if mentioned).

Syntax: numpy.amax(arr, axis = None, out = None, keepdims = )

numpy.amin(): This function returns minimum of an array or minimum along axis(if mentioned).

Syntax: numpy.amin(arr, axis = None, out = None, keepdims = )

Now, Let’s see an example:

Example 1:

Python3

# import library 
import numpy as np 
  
# create a numpy 2d-array 
x = np.array([[100, 20, 305], 
             [ 200, 40, 300]]) 
  
print("given array:\n", x) 
  
# get maximum element row 
# wise from numpy array 
max1 = np.amax(x ,1) 
  
# get minimum element row 
# wise from numpy array 
min1 = np.amin(x, 1) 
  
# print the row-wise max  
# and min difference 
print("difference:\n", max1 - min1)

Output:

given array:
 [[100  20 305]
 [200  40 300]]
difference:
 [285 260]

Example 2:

Python3

# import library 
import numpy as np 
  
# list 
x = [12, 13, 14, 15, 16] 
y = [17, 18, 19, 20, 21] 
  
# create a numpy 2d-array 
array = np.array([x, y]).reshape((2, 5)) 
  
print("original array:\n", array) 
  
# find max and min elements 
# row-wise 
max1, min1 = np.amax(array, 1), np.amin(array,1) 
  
# print the row-wise max  
# and min difference 
print("Difference:\n", max1 - min1)

Output:

original array:
 [[12 13 14 15 16]
 [17 18 19 20 21]]
Difference:
 [4 4]

Insert a new axis within a NumPy array

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