NumPy
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Accessing a value in a 2D arrayAccessing columns of a 2D arrayAccessing rows of a 2D arrayCalculating the determinant of a matrixChecking allowed values for a NumPy data typeChecking if a NumPy array is a view or copyChecking the version of NumPyChecking whether a NumPy array contains a given rowComputing Euclidean distance using NumpyConcatenating 1D arraysConverting array to lowercaseConverting type of NumPy array to stringCreating a copy of an arrayDifference between Python List and Numpy arrayDifference between the methods array_equal and array_equivDifference between the methods mod and fmodDifference between the methods power and float_powerFinding the closest value in an arrayFinding the Index of Largest Value in a Numpy ArrayFinding the Index of Smallest Value in a Numpy ArrayFinding the most frequent value in a NumPy arrayFlattening Numpy arraysGetting constant PiGetting elements from a two dimensional array using two dimensional array of indicesGetting indices of N maximum valuesGetting indices of N minimum valuesGetting the number of columns of a 2D arrayGetting the number of non-zero elements in a NumPy arrayGetting the number of rows of a 2D arrayInitializing an array of onesInitializing an array of zerosInitializing an identity matrixLimiting array values to a certain rangePerforming linear regressionPrinting full or truncated NumPy arrayPrinting large Numpy arrays without truncationRemoving rows containing NaN in a NumPy arrayReversing a NumPy arraySaving NumPy array to a fileShape of Numpy ArraysSorting value of one array according to anotherSuppressing scientific notation
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Difference between the methods mod and fmod in NumPy
schedule Aug 12, 2023
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Both mod(~) and fmod(~) methods compute the remainder of given two arrays of dividend and divisor.
What differentiates mod(~) from Numpy's fmod(~) is confusingly not whether or not one is for floating numbers; they are both capable of parsing floating numbers. The defining difference is how they handle negative numbers.
Let's take a look at a simple example.
x = [3, 8.5, -7]np.mod(x, 3)
array([0. , 2.5, 2. ])
Here, mod(-7,3)=2, which is equivalent to Python's % implementation (-7%3=2). Also notice how 8.5, which is a floating number, was parsed correctly.
Now, let's do the same thing using the fmod method:
x = [3, 8.5, -7]np.fmod(x, 3)
array([ 0. , 2.5, -1. ])
Here, we see that fmod(-7,3)=-1, which is a different answer from above. In fact, Numpy's fmod(~) method follows the main C library's fmod(~) implementation.
Related
NumPy | mod method
Numpy's mod(~) method computes the remainder element-wise given two arrays. Numpy's remainder(~) method is equivalent to this method.
NumPy | fmod method
Numpy's fmod(~) method computes the remainder element-wise given two arrays.
Published by Isshin Inada
Edited by 0 others
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