If you don't know the target length, accumulating values in a list, then using np.array() at the end to convert it to an array is a good option. Using just element. If you know the target length, you can create the array up-front using np.empty() and then filling it in. This is the code I wrote to get the embeddings as numpy arrays: final for element in finalembeddings: tach ().numpy () final.append (element) print (final) This still gives me a list of tensors, not a 2D-numpy array. There are a variety of strategies to build long 1-D arrays quickly. This isn't a matter of whether append() is a function or a method the data model for numpy arrays just doesn't mesh with the over-allocation strategy that makes list.append() "fast". It represents an N-D array, not just a 1-D list, so it can't really over-allocate in all axes. Because the size of the over-allocated memory grows with the size of the list list.append() is roughly constant-time, on average, for building a long list. axis is the axis along which the values are appended. The full syntax of this function is: numpy.append(arr, values, axisNone) Where: arr is the original array to which you are appending values are appended to the copy of arr. It only has to re-allocate memory once those over-allocated slots are used up. There is a function called numpy.append () you can use to append elements to an array. While there are still free slots at the end of the over-allocated memory, list.append() just puts the new item into a free slot. You can use the numpy append () function to append values to a numpy array. This can be useful for updating your data dynamically, concatenating multiple arrays. Example Get your own Python Server import numpy as np arr np. We can create a NumPy ndarray object by using the array () function. The array object in NumPy is called ndarray. The reason that list.append() is "fast" is because list over-allocates memory for the items it contains, roughly 1/8 extra items than there are in the list. Appending means adding new elements to the end/last of an existing array. Previous Next Create a NumPy ndarray Object NumPy is used to work with arrays.
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