In article <462FC8C6.3030200@gmail.com>,
Robert Kern
Christopher Barker wrote:
I can only help with one:
- Even after reading the book I'm not really clear on why one would use numpy.float_ instead of numpy.float or float
They float and numpy.float are the same, and numpy.float_ is the same as numpy.float64:
import numpy float is numpy.float True numpy.float64 is numpy.float64 True
float was added to the numpy namespace so that we could write consistent code like:
a = array(object, numpy.float32) b = array(object, numpy.float)
i.e. have it all in the same namespace.
I'm not sure why float_ is an alias for float64, though I'm guessing it's possible that on some platforms they are not the same.
Rather, numpy.float used to be an alias for numpy.float64; however, it overrode the builtin float() when "from numpy import *" was used at the interactive prompt. Consequently, we renamed it numpy.float_ and specifically imported the builtin float as numpy.float such that we didn't break code that had already started using "numpy.float".
But I still don't understand why one shouldn't just use dtype=float or numpy.float. Does that result in an array with a different type of float than numpy.float_ (float64)? Or does it just somehow speed up numpy because it doesn't have to convert the python type into a numpy dtype. Anyway, thank you all for the helpful answers! I'm glad numpy throws the standard MemoryError since I'm already testing for that. -- Russell