![]() ![]() The only difference is that using some methods you must carefully check dimension. I checked some of the methods for speed performance and find that there is no difference! …because arrays can be constructed out of a sequence of other arrays, adding a new dimension to the beginning. All the input arrays must have the same length. There is also an analogous function column_stack and shortcut c_, for horizontal (column-wise) stacking, as well as an almost-analogous function hstack-although for some reason the latter is less flexible (it is stricter about input arrays’ dimensionality, and tries to concatenate 1-D arrays end-to-end instead of treating them as columns).įinally, in the specific case of vertical stacking of 1-D arrays, the following also works: numpy.array( LIST ) For linear 1-D arrays, all the arrays are stacked vertically to form a 2-D array. This is good for concatenating a few explicitly-named arrays but is no good for your situation because this syntax will not accept a sequence of arrays, like your LIST. ValueError: all the input arrays must have same number of dimensions This is because np.hstack cannot concatenate two arrays with different numbers of rows. This flexible behavior is also exhibited by the syntactic shortcut numpy.r_ (note the square brackets). Again, you can concatenate a whole list at once without needing to iterate: numpy.vstack( LIST ) Where a new dimension is required, it is added on the left. Vstack (or equivalently row_stack) is often an easier-to-use solution because it will take a sequence of 1- and/or 2-dimensional arrays and expand the dimensionality automatically where necessary and only where necessary, before concatenating the whole list together. pythonnumpya,b densesparse0ValueError: all the input arrays must have same number of dimensionsnp.vstack((a,b))np.rowst. It will only work if all the input arrays have the same shape-even along the axis of concatenation. The arrays must have the same shape along all but the first axis. This takes the complementary approach: it creates a new view of each input array and adds an extra dimension (in this case, on the left, so each n-element 1D array becomes a 1-by- n 2D array) before concatenating. If you want to concatenate 1-dimensional arrays as the rows of a 2-dimensional output, you need to expand their dimensionality.Īs Jorge’s answer points out, there is also the function stack, introduced in numpy 1.10: numpy.stack( LIST, axis= 0 ) In general you can concatenate a whole sequence of arrays along any axis: ncatenate( LIST, axis= 0 )īut you do have to worry about the shape and dimensionality of each array in the list (for a 2-dimensional 3×5 output, you need to ensure that they are all 2-dimensional n-by-5 arrays already). ![]()
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