Hi,
Should numpy.mean() (and similar functions) maintain the original dtype
or the dtype used for the dtype option?
I do understand the numerical issues involved but my question relates to
whether this should be a bug or needs clarification in the documentation.
According to the help, the dtype parameter to numpy.mean() says:
dtype : dtype, optional
Type to use in computing the mean. For integer inputs, the default
is float64; for floating point, inputs it is the same as the input
dtype.
I interpret this to indicate the type used in the internal calculations
and technically does not say what the output dtype should be. But the
dtype option does change the output dtype as the simple example below
shows.
With Python 2.6 and numpy '1.4.0.dev7282' on Linux 64-bit Fedora 11
>>> import numpy as np
>>> a=np.array([1,2,3])
>>> a.dtype
dtype('int64')
>>> a.mean().dtype
dtype('float64')
>>> a.mean(dtype=np.float32).dtype
dtype('float64')
>>> a.mean(dtype=np.float64).dtype
dtype('float64')
>>> a.mean(dtype=np.float128).dtype
dtype('float128')
>>> a.mean(dtype=np.int).dtype
dtype('float64')
Clearly the output dtype is float64 or higher as determined by the input
dtype or dtype parameter.
Bruce
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