![]() There are two overarching styles that dictate the way data is read in from memory, they are the “C style” and “Fortran style”. The next container is a “row” of values which comprises one smaller container for each “column”. I can enter this for container (“dimension”) and view a matrix. The way I think about this is that the ndarray is a container, in the 3D case I think of a cube made up of stacked matrices. So for a 2D array, the order is (row, column), for a 3D array the order is (depth, row, column), for a 4D array it is (4th dimension, depth, row, column), etc. They are explained in detail in the NumPy documentation and in section 2.3 Memory Layout of ndarray in the book Guide to NumPy by Travis Oliphant, but they are to do with the fundamental implementation of how NumPy reads data from memory.īriefly, NumPy uses “row-major” indexing when reading data from memory which basically means that “grouping” starts from the left most index. ![]() These questions are related and actually much deeper than you might expect. How did NumPy decide to fill each 2D matrix slice with elements first, why not fill along the “depth” dimension first? Why is our array above composed of 3 slices of 3x2 matrices and not 2 slices of 3x3 matrices? You might be wondering two things at this point: We then repeat that one more time for the last “slice”. It's always a good idea to write a docstring for a function, specifying what it does, what arguments it takes, and what it returns. Once we’ve used the first 6 elements, we traverse a dimensional “slice” and use the next 6 element to fill that 2D slice. This question is difficult because: It's not clear what the function countlower does. We use the first 6 elements of our flattened array to fill in the first “slice”, and within that 2D slice, those elements are arranged in rows and columns dictated by the strides (i.e., every 2 elements increment a row and every 1 element increment a column). In the above example, we have three 2D matrix stacked together (I’ll call them “slices”). So in the above case, the strides is (24, 8) meaning 24 bytes (three 8-byte int64 elements) and 8 bytes (one 8-byte int64 element), meaning that every 3 elements we increment our first dimension (i.e., move to the next row) and every 1 element we increment our second dimension (i.e., move to the next column). As you can see in the example, the stride information is particularly important for mapping the chunk of memory back to a n-dimensional array structure. That word strides is the number of bytes you need to step in each dimension when traversing the array. It’s helpful to think of it as a one-dimensional sequence in memory but with “fancy indexing” that can help map an N-dimensional index (for ndarrays) into that one-dimensional representation.Ĭonsider “ Panel a” in the below conceptual figure from the paper Array programming with NumPy, showing how a 2d-array of data-type int64 (8 bytes) is represented in memory as a single chunk: An ndarray is stored as a single “chunk” of memory starting at some location. Now that we’ve covered the basic concepts of ndarrays, we can talk more about how arrays are represented in memory.
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