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
.
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.