numpy.ptp() in Python

numpy.ptp()function plays an important role in statistics by finding out Range of given numbers. Range = max value - min value. 
 

Syntax : ndarray.ptp(arr, axis=None, out=None) 
Parameters : 
arr :input array. 
axis :axis along which we want the range value. Otherwise, it will consider arr to be flattened(works on all the axis). axis = 0 means along the column and axis = 1 means working along the row. 
out : [ndarray, optional]Different array in which we want to place the result. The array must have same dimensions as expected output. 
Return : Range of the array (a scalar value if axis is none) or array with range of values along specified axis. 
 

Code #1: Working 

# Python Program illustrating 
# numpy.ptp() method 
  
import numpy as np
  
# 1D array 
arr = [1, 2, 7, 20, np.nan]
print("arr : ", arr) 
print("Range of arr : ", np.ptp(arr))

# 1D array 
arr = [1, 2, 7, 10, 16]
print("arr : ", arr) 
print("Range of arr : ", np.ptp(arr))

Output : 

arr :  [1, 2, 7, 20, nan]
Range of arr :  nan
arr :  [1, 2, 7, 10, 16]
Range of arr :  15

 Code #2 : 

# Python Program illustrating 
# numpy.ptp() method 

import numpy as np

# 3D array 
arr = [[14, 17, 12, 33, 44],  
       [15, 6, 27, 8, 19], 
       [23, 2, 54, 1, 4,]] 
print("\narr : \n", arr) 
   
# Range of the flattened array 
print("\nRange of arr, axis = None : ", np.ptp(arr)) 
   
# Range along the first axis 
# axis 0 means vertical 
print("Range of arr, axis = 0 : ", np.ptp(arr, axis = 0)) 
   
# Range along the second axis 
# axis 1 means horizontal 
print("Min of arr, axis = 1 : ", np.ptp(arr, axis = 1))  

Output : 

arr : 
 [[14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4]]

Range of arr, axis = None :  53
Range of arr, axis = 0 :  [ 9 15 42 32 40]
Min of arr, axis = 1 :  [32 21 53]

 Code #3 : 

# Python Program illustrating 
# numpy.ptp() method 

import numpy as np

arr1 = np.arange(5) 
print("\nInitial arr1 : ", arr1)
 
# using out parameter
np.ptp(arr, axis = 0, out = arr1)
 
print("Changed arr1(having results) : ", arr1)

Output :  

Initial arr1 :  [0 1 2 3 4]
Changed arr1(having results) :  [ 9 15 42 32 40]

mohit gupta_omg :)

Follow

Improve

Article Tags :

• Python
• Python-numpy
• Python numpy-Statistics Functions

Practice Tags :

• python