I used to use numpy.arange but had some complications controlling the number of elements it returns, due to floating point errors. So now I use linspace, e.g.:

>>> import numpy
>>> numpy.linspace(0, 10, num=4)
array([  0.        ,   3.33333333,   6.66666667,  10.        ])

You can use numpy.iinfo(arg).max to find the max value for integer types of arg, and numpy.finfo(arg).max to find the max value for float types of arg.

>>> numpy.iinfo(numpy.uint64).min
0
>>> numpy.iinfo(numpy.uint64).max
18446744073709551615L
>>> numpy.finfo(numpy.float64).max
1.7976931348623157e+308
>>> numpy.finfo(numpy.float64).min
-1.7976931348623157e+308

iinfo only offers min and max, but finfo also offers useful values such as eps (the smallest number > 0 representable) and resolution (the approximate decimal number resolution of the type of arg).

float32 is a 32 bit number - float64 uses 64 bits.

That means that float64’s take up twice as much memory - and doing operations on them may be a lot slower in some machine architectures.

However, float64’s can represent numbers much more accurately than 32 bit floats.

They also allow much larger numbers to be stored.

For your Python-Numpy project I'm sure you know the input variables and their nature.

To make a decision we as programmers need to ask ourselves

1. What kind of precision does my output need?
2. Is speed not an issue at all?
3. what precision is needed in parts per million?

A naive example would be if I store weather data of my city as [12.3, 14.5, 11.1, 9.9, 12.2, 8.2]

Next day Predicted Output could be of 11.5 or 11.5164374

do your think storing float 32 or float 64 would be necessary?