numpy.fmax() in Python Last Updated : 28 Nov, 2018 Comments Improve Suggest changes Like Article Like Report numpy.fmax() function is used to compute element-wise maximum of array elements. This function compare two arrays and returns a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first is returned. Syntax : numpy.fmax(arr1, arr2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, ufunc ‘fmax’) Parameters : arr1 : [array_like] The array holding the elements to be compared. arr2 : [array_like] The array holding the elements to be compared. out : [ndarray, optional] A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. **kwargs : Allows to pass keyword variable length of argument to a function. It is used when we want to handle named argument in a function. where : [array_like, optional] True value means to calculate the universal functions(ufunc) at that position, False value means to leave the value in the output alone. Return : [ndarray or scalar] The maximum of arr1 and arr2, element-wise. Returns scalar if both arr1 and arr2 are scalars. Code #1 : Working Python # Python program explaining # fmax() function import numpy as geek in_num1 = 10 in_num2 = 11 print ("Input number1 : ", in_num1) print ("Input number2 : ", in_num2) out_num = geek.fmax(in_num1, in_num2) print ("maximum of 10 and 11 : ", out_num) Output : Input number1 : 10 Input number2 : 11 maximum of 10 and 11 : 11 Code #2 : Python # Python program explaining # fmax() function import numpy as geek in_arr1 = [2, 8, 125, geek.nan] in_arr2 = [geek.nan, 3, 115, geek.nan] print ("Input array1 : ", in_arr1) print ("Input array2 : ", in_arr2) out_arr = geek.fmax(in_arr1, in_arr2) print ("Output array : ", out_arr) Output : Input array1 : [2, 8, 125, nan] Input array2 : [nan, 3, 115, nan] Output array : [ 2. 8. 125. nan] Code #3 : Python # Python program explaining # fmax() function import numpy as geek in_arr1 = [2, 8, 125] in_arr2 = [3, 3, 115] print ("Input array1 : ", in_arr1) print ("Input array2 : ", in_arr2) out_arr = geek.fmax(in_arr1, in_arr2) print ("Output array: ", out_arr) Output : Input array1 : [2, 8, 125] Input array2 : [3, 3, 115] Output array: [ 3 8 125] Comment More infoAdvertise with us Next Article numpy.fmax() in Python J jana_sayantan Follow Improve Article Tags : Python Python-numpy Python numpy-Mathematical Function Practice Tags : python Similar Reads numpy.fabs() in Python numpy.fabs() function is used to compute the absolute values element-wise. This function returns the absolute values (positive magnitude) of the data in arr. It always return absolute values in floats. Syntax : numpy.fabs(arr, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, u 2 min read numpy.argmax() in Python The numpy.argmax() function returns indices of the max element of the array in a particular axis. Syntax : numpy.argmax(array, axis = None, out = None) Parameters : array : Input array to work on axis : [int, optional]Along a specified axis like 0 or 1 out : [array optional]Provides a feature to ins 3 min read numpy.fmin() in Python numpy.fmin() function is used to compute element-wise minimum of array elements. This function compare two arrays and returns a new array containing the element-wise minima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first 2 min read numpy.floor() in Python The numpy.floor() function returns the largest integer less than or equal to each element in the input array. It effectively rounds numbers down to the nearest whole number. Let's understand with an example:Pythonimport numpy as np a = [0.5, 1.5, 2.5, 3, 4.5, 10.1] res = np.floor(a) print("Floored:" 1 min read numpy.mod() in Python numpy.mod() is another function for doing mathematical operations in numpy.It returns element-wise remainder of division between two array arr1 and arr2 i.e. arr1 % arr2 .It returns 0 when arr2 is 0 and both arr1 and arr2 are (arrays of) integers. Syntax : numpy.mod(arr1, arr2, /, out=None, *, where 2 min read Like