On Fri, Jan 26, 2018 at 5:48 PM, Chris Barker email@example.com wrote:
On Fri, Jan 26, 2018 at 2:35 PM, Allan Haldane firstname.lastname@example.org wrote:
As I remember, numpy has some fairly convoluted code for array creation which tries to make sense of various nested lists/tuples/ndarray combinations. It makes a difference for structured arrays and object arrays. I don't remember the details right now, but I know in some cases the rule is "If it's a Python list, recurse, otherwise assume it is an object array".
that's at least explainable, and the "try to figure out what the user means" array cratinon is pretty much an impossible problem, so what we've got is probably about as good as it can get.
These points make me think that instead of a `.totuple` method, this might be more suitable as a new function in np.lib.recfunctions.
I don't seem to have that module -- and I'm running 1.14.0 -- is this a new idea?
Sorry, I didn't specify it correctly. It is "numpy.lib.recfunctions".
thanks -- found it.
Also, the functions in that module encourage "pandas-like" use of structured arrays, but I'm not sure they should be used that way. I've been thinking they should be primarily used for binary interfaces with/to numpy, eg to talk to C programs or to read complicated binary files.
that's my use-case. And I agree -- if you really want to do that kind of thing, pandas is the way to go.
I thought recarrays were pretty cool back in the day, but pandas is a much better option.
So I pretty much only use structured arrays for data exchange with C code....
My impression is that this turns into a deprecate recarrays and supporting recfunction issue.
recfunctions and the associated function from matplotlib.mlab where explicitly designed for using structured dtypes as dataframe_like.
(old question: does numpy have a sort_rows function now without detouring to structured dtype views?)
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