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 power and float_power in NumPy
schedule Aug 11, 2023
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Tags Python●NumPy
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Both methods are used to raise each value in the input array by the specified amount.
The key difference is that Numpy's power(~) method uses the same data-type as the input array to perform the calculation; if your input array only contains integers, then the returned result will also be of type int. On the other hand, float_power(~) always uses float64 for maximum precision.
Here's a simple example:
x = [1,2,3]np.power(x, 2) # Type is int64 since the input is of type int64
array([1, 4, 9])
x = [1,2,3]np.float_power(x, 2) # Type is float64
array([1., 4., 9.])
Published by Isshin Inada
Edited by 0 others
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