I'm trying to optimize the performance of a script, which is full of Numpy's where() after which only the first returned element is actually used. Example: F = np.where(Y>p/100)[0] For the huge data sets that we are processing, it doesn't look like a good solution (both in terms of speed and memory consumption) to create a large array and then discard all but the first element. Is there any way how to skip the overhead, maybe by tweaking the condition? Continue reading...
