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numpy.trunc() in Python
  • Last Updated : 04 Dec, 2020

The numpy.trunc() is a mathematical function that returns the truncated value of the elements of array. The trunc of the scalar x is the nearest integer i which, closer to zero than x. This simply means that, the fractional part of the signed number x is discarded by this function.

Syntax : numpy.trunc(x[, out]) = ufunc ‘trunc’)
Parameters :

a : [array_like] Input array

Return :
The truncated of each element, with float data-type

Code #1 : Working






# Python program explaining
# trunc() function
  
import numpy as np
  
in_array = [.5, 1.5, 2.5, 3.5, 4.5, 10.1]
print ("Input array : \n", in_array)
  
truncoff_values = np.trunc(in_array)
print ("\nRounded values : \n", truncoff_values)
  
  
in_array = [.53, 1.54, .71]
print ("\nInput array : \n", in_array)
  
truncoff_values = np.trunc(in_array)
print ("\nRounded values : \n", truncoff_values)
  
in_array = [.5538, 1.33354, .71445]
print ("\nInput array : \n", in_array)
  
truncoff_values = np.trunc(in_array)
print ("\nRounded values : \n", truncoff_values)


Output :

Input array : 
 [0.5, 1.5, 2.5, 3.5, 4.5, 10.1]

Rounded values : 
 [  0.   1.   2.   3.   4.  10.]

Input array : 
 [0.53, 1.54, 0.71]

Rounded values : 
 [ 0.  1.  0.]

Input array : 
 [0.5538, 1.33354, 0.71445]

Rounded values : 
 [ 0.  1.  0.]

 

Code 2 : Working




# Python program explaining
# trunc() function
  
import numpy as np
  
in_array = [1.67, 4.5, 7, 9, 12]
print ("Input array : \n", in_array)
  
truncoff_values = np.trunc(in_array)
print ("\nRounded values : \n", truncoff_values)
  
  
in_array = [133.000, 344.54, 437.56, 44.9, 1.2]
print ("\nInput array : \n", in_array)
  
truncoff_values = np.trunc(in_array)
print ("\nRounded values upto 2: \n", truncoff_values)


Output :

Input array : 
 [1.67, 4.5, 7, 9, 12]

Rounded values : 
 [  1.   4.   7.   9.  12.]

Input array : 
 [133.0, 344.54, 437.56, 44.9, 1.2]

Rounded values upto 2: 
 [ 133.  344.  437.   44.    1.]

 
References : https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.trunc.html#numpy.trunc
.

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