![]() I have not benchmarked the numpy.empty + ndarray.resize combo, but both should be rather microsecond than millisecond operations. This discussion has more benchmarking on different options:įastest way to grow a numpy numeric array If you know even the maximum size of the list beforehand, you are probably best off allocating a numpy.array using numpy.empty (not numpy.zeros) with the maximum size and then use ndarray.resize to shrink the array once you have filled in all data.įor some reason numpy.array(l) where l is a list is often slow with large lists, whereas copying even large arrays is quite fast (I just tried to create a copy of a 100 000 000 element array it took less than 0.5 seconds). There is very little you can do to speed things up if you do not know the size of the list beforehand. What this means is that lists are designed to be very efficient with the use of append. ![]() For more information on this, please see: Python actually uses the method #2, the lists are stored as "dynamic arrays". However, it is slower in sequential reading and allocates more memory. The first approach enables all sorts of fancy insertion and deletion options, sorting, etc. If we cannot do it at the same location, we need to allocate a bigger block and move all data. If we get more stuff into the list, we need to try to allocate more memory, preferably at the same location. ![]() This area has to be allocated when the list is created, and it has to be larger than what we initially need. Fastest way to grow a numpy numeric array Ask Question Asked 11 years, 11 months ago Modified 3 months ago Viewed 116k times 102 Requirements: I need to grow an array arbitrarily large from data. Store the data in a contiguous memory area. Use a data structure (linked list, some tree structure, etc.) which makes it possible to allocate memory separately for each new element in a list. There are essentially two different possibilities: Internally, the problem for a list of unknown length is that it needs to fit in memory somehow regardless of its length. A bit of technical explanation for the "why lists" part.
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