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Why are new dimensions added to the left in numpy arrays?
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[QUOTE="fog37, post: 6461877, member: 503639"] Thank you. Now I see. It makes sense now. I read about row-major and column-major in relation to how array data elements are stored linearly in memory. Apparently numpy uses row-major (column indices change the fastest) when the array elements are arranged in line, row by row from top to bottom. What is the connection between row-major and the fact that the order is (depth, row, column)? You mention that [I]"...In 2D arrays, you have rows, and under each row, you have columns. So, columns are nested under rows..."[/I] In what sense are columns nested under rows? [/QUOTE]
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Why are new dimensions added to the left in numpy arrays?
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