numpy.trunc() in Python Last Updated : 08 Mar, 2024 Comments Improve Suggest changes Like Article Like Report 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 Python3 # 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 Python3 # 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.] Comment More infoAdvertise with us Next Article numpy.array_str() in Python M mohit gupta_omg :) Follow Improve Article Tags : Python Python-numpy Python numpy-Mathematical Function Practice Tags : python Similar Reads numpy.round_() in Python The round_() function in NumPy rounds the elements of an array to a specified number of decimal places. This function is extremely useful when working with floating-point numbers and when precision is important in scientific computing or data analysis.Syntax: numpy.round_(arr, decimals=0, out=None)P 3 min read trunc() in Python Truncate in Python There are many built-in modules in python. Out of these module there is one interesting module known as math module which have several functions in it like, ceil, floor, truncate, factorial, fabs, etc. Out of these functions there is an interesting function called truncate which b 1 min read numpy.array_str() in Python numpy.array_str()function is used to represent the data of an array as a string. The data in the array is returned as a single string. This function is similar to array_repr, the difference being that array_repr also returns information on the kind of array and its data type. Syntax : numpy.array_st 2 min read numpy.trim_zeros() in Python numpy.trim_zeros() removes the leading and trailing zeros from a 1-D array. It is often used to clean up data by trimming unnecessary zeros from the beginning or end of the array. Example:Pythonimport numpy as np a = np.array([0, 0, 3, 4, 0, 5, 0, 0]) res = np.trim_zeros(a) print(res)Output[3 4 0 5] 2 min read numpy.trim_zeros() in Python numpy.trim_zeros() removes the leading and trailing zeros from a 1-D array. It is often used to clean up data by trimming unnecessary zeros from the beginning or end of the array. Example:Pythonimport numpy as np a = np.array([0, 0, 3, 4, 0, 5, 0, 0]) res = np.trim_zeros(a) print(res)Output[3 4 0 5] 2 min read numpy.trim_zeros() in Python numpy.trim_zeros() removes the leading and trailing zeros from a 1-D array. It is often used to clean up data by trimming unnecessary zeros from the beginning or end of the array. Example:Pythonimport numpy as np a = np.array([0, 0, 3, 4, 0, 5, 0, 0]) res = np.trim_zeros(a) print(res)Output[3 4 0 5] 2 min read Like