efficient, fast numpy histograms
First, fill in your 16 bins without considering date at all.
Then, sort the elements within each bin by date.
Now, you can use binary search to efficiently locate a given year/month/week within each bin.
I have a 2D numpy array consisting of ca. 15'000'000 datapoints. Each datapoint has a timestamp and an integer value (between 40 and 200). I must create histograms of the datapoint distribution (16 bins: 40-49, 50-59, etc.), sorted by year, by month within the current year, by week within the current year, and by day within the current month.
Now, I wonder what might be the most efficient way to accomplish this. Given the size of the array, performance is a conspicuous consideration. I am considering nested "for" loops, breaking down the arrays by year, by month, etc. But I was reading that numpy arrays are highly memory-efficient and have all kinds of tricks up their sleeve for fast processing. So I was wondering if there is a faster way to do that. As you may have realized, I am an amateur programmer (a molecular biologist in "real life") and my questions are probably rather naïve.