> 1. How fast can Python do math calculations compared with other languages > such as Fortran and fast versions of Basic. I would have to believe that it > is much faster than Perl for doing math calculations.
As others have indicated, a lot depends on the form of your calculations. There is a cost to crossing the Python/C boundary, and you may or may not care about the other stuff Python can do on-the-fly, like promote ints to Python longs, check for zero division, etc. If you have floating point code that is array-like, then organizing it to use numpy can be a big win, as you will cross that expensive boundary much less often per underlying operation. (I'm thinking here of more complex operations like trigonometric functions, not simple adds and subtracts, which are handled by the Python virtual machine.) I will relate one anecdote from my job here which highlights the cost of the boundary. I work at a trading firm. I work solely within a Python world, but my stuff is built on top of a lot of C++ code developed by others at the firm. Several years ago, it was decided that we needed a price library because some exchanges (the London Stock Exchange, for example) sets the minimum price change based on the current trading price range. Consider a stock like AAPL, which closed yesterday at 489.56. It trades in pennies on NASDAQ, no matter its price. If it was on the LSE, it might trade in half pennies if it was trading in a $100 range, or in dimes if it was trading near $1000 range. (I'm making this stuff up, just to give you an idea what I'm referring to.) So, a price library was written in C++. You could ask for the next higher or lower valid price. It was all very peppy, because of course, it relied heavily on C++ inline functions for all these simple operations. It was wrapped with Boost::Python and tossed over the fence for us Python peons to use. Guess what? It was unbearably slow, because not only did we were crossing the Python/C boundary to do little more (most of the time) than a single add or subtract. The solution was to write a pure Python version of the parts that mattered most. Moral of the story: consider how your code is structured and whether it makes sense to reorganize it when implementing in Python. Skip -- https://mail.python.org/mailman/listinfo/python-list
