[Numpy-discussion] Efficient way to handle None->nan?

[Sasha]

>>> from numpy.core.ma import masked_values
>>> from numpy import nan
>>> masked_values([1.0,None,2.0],None).filled(nan).astype(float)
array([ 1.        ,         nan,  2.        ])

On 1/18/06, Russell E. Owen <rowen at cesmail.net> wrote:
> We're getting numeric data from a (MySQL) database. We'd like to use
> numarray or NumPy on the resulting data, but some values may be None. Is
> there a fast, efficient way to replace None with NaN? I'd hate to use a
> list comprehension on each data tuple before turning it into an array,
> but I haven't thought of anything else.
>
> numarray.array and numarray.where are both intolerant of None in the
> input data.
>
> -- Russell
>
>
>
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