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From Numpy's tutorial, axis can be indexed with integers, like 0 is for column, 1 is for row, but I don't grasp why they are indexed this way? And How do I figure out each axis' index when coping with multidimensional array?
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NumPy axes are the directions along the rows and columns. Just like coordinate systems, NumPy arrays also have axes. In a 2-dimensional NumPy array, the axes are the directions along the rows and columns.
along an axis. Axes are defined for arrays with more than one dimension. A 2-dimensional array has two corresponding axes: the first running vertically downwards across rows (axis 0), and the second running horizontally across columns (axis 1). Many operation can take place along one of these axes.
Numpy Axis in Python for SumWhen we use the numpy sum() function on a 2-d array with the axis parameter, it collapses the 2-d array down to a 1-d array. It collapses the data and reduces the number of dimensions. But which axis will collapse to return the sum depends on whether we set the axis to 0 or 1.
Array indexing is the same as accessing an array element. You can access an array element by referring to its index number. The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc.
By definition, the axis number of the dimension is the index of that dimension within the array's shape. It is also the position used to access that dimension during indexing.
For example, if a 2D array a has shape (5,6), then you can access a[0,0] up to a[4,5]. Axis 0 is thus the first dimension (the "rows"), and axis 1 is the second dimension (the "columns"). In higher dimensions, where "row" and "column" stop really making sense, try to think of the axes in terms of the shapes and indices involved.
If you do .sum(axis=n), for example, then dimension n is collapsed and deleted, with each value in the new matrix equal to the sum of the corresponding collapsed values. For example, if b has shape (5,6,7,8), and you do c = b.sum(axis=2), then axis 2 (dimension with size 7) is collapsed, and the result has shape (5,6,8). Furthermore, c[x,y,z] is equal to the sum of all elements b[x,y,:,z].
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If at all anyone need this visual description of a shape=(3,5) array:
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