[Numpy-discussion] Simple financial functions for NumPy
Sat Apr 5 05:12:08 CDT 2008
I'm just a numpy user, but for what it's worth, I would much prefer to
have a single numpy namespace with a small as possible number of
objects inside that namespace. To me, 'as small as possible' means
that it only includes the array and associated array manipulation
functions (searchsorted, where, and the record array functions), and
the various functions that operate on arrays (exp, log, sin, cos, abs,
Having a small number of objects in a single namespace means that
numpy is much easier to learn for beginners, as it's easier to find
the appropriate thing for what you want to do (this is also helped by
reducing duplication *shakes fist at .compress* and good
documentation). It's also much easier to explore with dir() to jog
your memory as to what function you need for a task.
If I felt I contributed enough to this list to have a '1', I would be
-1 on adding financial functions to numpy.
> On Fri, Apr 4, 2008 at 3:31 PM, Anne Archibald <email@example.com>
> > On 04/04/2008, Alan G Isaac <firstname.lastname@example.org> wrote:
> > It seems to me that there are two separate issues people are talking
> > about when they talk about packaging:
> > * How should functions be arranged in the namespaces? numpy.foo(),
> > scipy.foo(), numpy.lib.financial.foo(), scikits.foo(),
> > numkitfull.foo()?
> > * Which code should be distributed together? Should scipy require
> > separate downloading and compilation from numpy?
> > The two questions are not completely independent - it would be
> > horribly confusing to have the set of functions available in a given
> > namespace depend on which packages you had installed - but for the
> > most part it's not a problem to have several toplevel namespaces in
> > one package (python's library is the biggest example of this I know
> > of).
> > For the first question, there's definitely a question about how much
> > should be done with namespaces and how much with documentation. The
> > second is a different story.
> > Personally, I would prefer if numpy and scipy were distributed
> > together, preferably with matplotlib. Then anybody who used numpy
> > would have available all the scpy tools and all the plotting tools; I
> > think it would cut down on wheel reinvention and make application
> > development easier. Teachers would not need to restrict themselves to
> > using only functions built into numpy for fear that their students
> > might not have scipy installed - how many students have learned to
> > save their arrays in unportable binary formats because their teacher
> > didn't want them to have to install scipy?
> > I realize that this poses technical problems. For me installing scipy
> > is just a matter of clicking on a checkbox and installing a 30 MB
> > package, but I realize that some platforms make this much more
> > difficult. If we can't just bundle the two, fine. But I think it is
> > mad to consider subdividing further if we don't have to.
> If these were tightly tied together, for instance in one big dll , this
> would be unpleasant for me. I still have people downloading stuff over 56k
> modems and adding an extra 30 MB to the already somewhat bloated numpy
> distribution would make there lives more tedious than they already are.
> I think python's success is due in part to its "batteries included"
> > library. The fact that you can just write a short python script with
> > no extra dependencies that can download files from the Web, parse XML,
> > manage subprocesses, and save persistent objects makes development
> > much faster than if you had to forever decide between adding
> > dependencies and reinventing the wheel. I think numpy and scipy should
> > follow the same principle, of coming "batteries included".
> One thing they try to do in Python proper is think a lot more before adding
> stuff to the standard library. Generally packages need to exist separately
> for some period of time to prove there general utility and to stabilize
> before they get accepted. Particularly in the core, but in the library as
> well, they make an effort to chose a compact set of primitive operations
> without a lot of duplication (the old "There should be one-- and preferably
> only one --obvious way to do it."). The numpy community has, particularly of
> late, been rather quick to add things that seem like they *might *be useful.
> One of the advantages of having multiple namespaces would have been to
> enforce a certain amount of discipline on what went into numpy, since it
> would've been easier to look at and evaluate a few dozen functions that
> might have comprised some subpackage rather than, let's say, five hundred or
> I suspect it's too late now; numpy has chosen the path of matlab and the
> other array packages and is slowly accumulating nearly everything in one big
> flat namespace. I don't like it, but it seems pointless to fight it at this
> So in this specific case, yes, I think the financial functions should
> > absolutely be included; whether they should be included in scipy or
> > numpy is less important to me because I think everyone should install
> > both packages.
> Personally I think it's a bad idea to add stuff that, as far as I can tell,
> no has even asked for yet. Put them in the sandbox. Advertise them. If
> people use them, figure out what needs to be changed. Then add them to SciPy
> once they've stabilized, if they actually get used.
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