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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Checking if a NumPy array is a view or copy
schedule Aug 10, 2023
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To check if a NumPy array is a view or not use the base property. To check if a NumPy array is a copy, we can use np.shares_memory(~) to check whether the two objects share memory or not.
Examples
View or not
Consider the following arrays a and b:
a = np.array([1,2])
To check if b is a view of a:
b.base is a
True
Here, array b shares the same memory address as a, so b.base is a evaluates to True.
Share memory or not
Consider the following arrays a and b:
a = np.array([1,2])b = a.copy()
To check if b shares memory with a:
np.shares_memory(b, a)
False
Given the two arrays do not share memory, we can say that b is a copy of a.
Published by Arthur Yanagisawa
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