Numpy: views vs copy by slicing

The accepted answer by John Zwinck is actually false (I just figured this out the hard way!). The problem in the question is a combination of doing “l-value indexing” with numpy’s fancy indexing. The following doc explains exactly this case https://scipy-cookbook.readthedocs.io/items/ViewsVsCopies.html in the section “But fancy indexing does seem to return views sometimes, doesn’t it?” … Read more

Why use arrays instead of slices?

As said by Akavall, arrays are hashable. That means they can be used as a key to a map. They are also pass by value. Each time you pass it to a function or assign it to another variable it makes a complete copy of it. They can be serialized by encoding/binary. They also can … Read more

Best way to swap variable values in Go?

Yes, it is possible. Assuming a and b have the same type, the example provided will work just fine. For example: a, b := “second”, “first” fmt.Println(a, b) // Prints “second first” b, a = a, b fmt.Println(a, b) // Prints “first second” Run sample on the playground This is both legal and idiomatic, so … Read more

Selecting multiple slices from a numpy array at once

You can use the indexes to select the rows you want into the appropriate shape. For example: data = np.random.normal(size=(100,2,2,2)) # Creating an array of row-indexes indexes = np.array([np.arange(0,5), np.arange(1,6), np.arange(2,7)]) # data[indexes] will return an element of shape (3,5,2,2,2). Converting # to list happens along axis 0 data_extractions = list(data[indexes]) np.all(data_extractions[1] == data[1:6]) True … Read more

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