Data Frames in D - let's not wait for linear algebra; useful today in finance and Internet of Things

[Laeeth Isharc via Digitalmars-d-learn]

Russell:
"I think we are agreeing. Very lightweight editor and executor of 
code
fragments is as good, if not better, that the one line REPL."

Yes - the key for me is that the absence of a shell is by no 
means a reason to say that D is not suited to this task.  One may 
wish to refine what exists, but that is another question entirely.

"Part of the problem here is tribalism. Most data science people 
want to
use the same tools that other data science people use, even 
though the
issue is to differentiate themselves."

Yes - we are answering two different questions.  I could not care 
less about persuading anyone en masse in a broad sector, those 
who think of themselves as being 'data scientists' included.  
It's silly, in my view, to think of it as an established field 
very distinct from others, and with a fixed way of doing things.  
If for no other reason that things are in flux and the sector is 
growing quickly, which means that there is room for many 
different approaches, and it is premature to think the popularity 
of approach X or Y today means that approach 'D' can't be 
productive tomorrow.

But as I said, I am less convinced in persuading anyone, and 
rather more concerned with getting a basic data frame in D up and 
running because I could certainly use it, and the hard work has 
been done already.  The basics should be an evening's work for an 
advanced D hacker, but it will probably take me longer than that. 
  In any case, since nobody else has come forward, I will keep 
working away at it.

"A BLAS library is certainly a precusor, as is very good data
visualization tools, graphs, diagrams etc."

Perhaps a prerequisite to D being seen as a contender, but I 
don't see how it's a prerequisite just to have a dataframe, which 
is really a very simple yet incredibly useful thing.

"Go has masses of people putting a lot of effort into Web. It's 
not the ideas, it's the number of people getting on board and 
doing things".

Also about the quality of the people.  (I have no view about Go, 
but have a very positive view on D).  When things get big there 
is a danger they get cluttered.  That's one blessing for D.

"To get some traction in any of these areas, finance data 
analysis and
model building, or systems activity, it is all about people doing 
it,
publicizing it and making things available for others to use".

Yes - so do you have any thoughts on what a data frame structure 
should look like?  I am trying to do and after that will make 
available.

"But it needs to be better than Julia in some way that makes
others sit up and take notice. There has to be the ability to 
create
some hype."

Don't care ;)  This concept of "what is your edge" is not my cup 
of tea because I do not see the world in those terms.  Something 
of high quality that's highly productive will over time stand a 
decent chance of becoming more widely adopted, whereas trying to 
force it into some kind of marketing framework can prove 
counterproductive.

Right now, the main thing I care about is solving the problem at 
hand, because if it solves my problem well then I am pretty sure 
it will be useful to others too, and be so better than if one had 
adopted a more consciously 'commercial/marketing' mindset.

I would post the dataframe skeleton here, but it's too 
embarassing right now and want to read the std.variant library to 
see what tricks I can learn.  (A data series seems kind of like a 
variant, but with every cell the same type).  Obviously in some 
cases the data frame type is defined at compile time, like a 
struct, and that's easy.  But if you are loading from a file you 
need to be able to have dynamic typing for the column.

"> I don't believe I agree that we need a perfect 
multi-dimensional
> rectangular array library to serve as a backend before thinking 
> and doing much on data frames (although it will certainly be 
> very useful when ready).

Also, if there is a ready made C or C++ library that can be made 
use of,
do it."

Well, the hard parts of arrays themselves (and it's not that 
fiendishly hard, I would think) seem to need to be tightly 
integrated with the language, so I don't see how a C/C++ library 
will help so much.  For the linear algebra, yes...
hyping it up.

"I recently discovered a number of hedge funds work solely on 
moving
average based algorithmic trading. NumPy, SciPy and Pandas all 
have
variations on this basic algorithm."

Well, having worked for more or less quanty hedge funds since 98, 
I would think it unlikely that anyone depends only on moving 
averages although basic old-school trend-following certainly does 
work - it is just a hard sell to herding institutional investors, 
and does not fit very well with the concept of a 'career'.  (You 
have to be able to see the five years of subdued returns since 
2009 as just part of the cycle, which indeed may be the correct 
view when one sees markets as a natural phenomenon, but is not 
the view of asset allocators, or talented people one may want to 
hire in other areas).


"Perceived to be fast. In fact it isn't anything like as fast as 
it
should be. NumPy (which underpins Pandas and provides all the data
structures and basic algorithms), is actually quite slow."

Yes - was tired when I wrote, and meant to say Pandas is fast for 
key things such as parsing large data files eg CSVs - 
significantly faster than Julia, from what I have seen.  And yes 
- I agree about Numpy, and don't need to be persuaded of the 
benefits of moving to something else if one can make it slightly 
less inconvenient.  Which is how this conversation started - you 
really don't need a perfect BLAS implementation/wrapper to start 
to benefit from a dataframe.

"Guido though is I/O bound rather than CPU bound in his work and 
doesn't see a need for anything other than multiprocessing for 
accessing parallelism in Python. Sadly, it can be shown that 
multiprocessing is slow and inefficient at what it does and it 
needs replacing".

I cannot claim deep expertise here, but this was one of the 
things that got me looking at D originally.  Just too frustrating 
trying to fit with the restrictions to write nogil Cython code, 
knowing that one might need to rewrite when one has mentally long 
moved on.  Ie I feel like I am short options building my platform 
that way, and I don't like being short options when they don't 
cost much to buy.  Hence D.  It also struck me that there was a 
degree of complacency amongst some Python people, whereas hunger 
and insecurity may be a spur to greater and more creative efforts.

"In principle this is fertile territory for a new language to 
take the
stage. Hence Julia." I fear D has missed the boat of this 
opportunity
now."
I really don't see why one can't just take the next boat arriving 
in fifteen minutes.  Or establish a new boat service going 
somewhere better that hooks up with the existing network.  
Conditions are changing so quickly, and the gap between the talk 
about big data etc and what people have actually done so far so 
large that to me the field seems wide open.  I don't see an 
alternative acceptable way to do what I would like, so D it will 
be.  And if I think that way today, probably others will have the 
same thoughts in coming years.  (Perhaps not).

"This is worth hyping up, it should be front and centre on teh 
dlang
pages along with Facebook funding bug fixes."

I agree.  Also in a few lines a punchier summary of why 
Sociomantic use D, what the benefits have been, and how they deal 
with the standards sorts of hurdles that might have been 
objections in a more mature and conventional company ("how are 
you going to hire experienced D programmers").

"But if all the libraries are C , C++ and Fortran, is there any 
value add
role for D?"

I guess we vote with our feet/fingers.  Sounds like you don't 
find D especially useful (since you don't use it much currently), 
whereas I do.  De gustibus non est disputandum, particularly when 
tastes reflect being in different situations.

"Lots of C++ system embed Python or Lua for dynamic scripting 
capability,
lots of Python and R system call out to C. This seems a well 
established
milieu. Is there a good way for D to, in an evolutionary way 
establish a
permanent foothold. Certainly it cannot be a revolutionary one."

You write as if Christensen's book "The Innovator's Dilemma" had 
never been written, and nor had it been a standard textbook in 
business schools for some years.  You may have good arguments as 
to why he is wrong, or why it doesn't apply to D, but you haven't 
set them out, as far as I am aware.

Not Russell
"There will sure be some algorithms where numba/cython would do
better (especially if they cannot be easily vectorized), but
that's not the point. The thing about numpy is that it provides a
unified accepted interface (plus a reasonable set of reasonably
fast tools and algorithms) for arrays and buffers for a multitude
of scientific libraries (scipy, pytables, h5py, pandas, scikit-*,
just to name a few), which then makes it much easier to use them
together and write your own ones."

Yes.  But one has to start somewhere (if not happy with the 
python route), and we start to have equivalents of 
scipy,pytables/h5py.  So why not pandas?


"Splunk stuff is just an example of using dataflow networks for
processing data rather than using SQL. The "Big Data using JVM"
community are already on this road, cf. various proprietary 
frameworks
running over Hadoop and Spark."

Yes - technically, it may well be "nothing more than".  But many 
of the practical problems which have a high commercial return to 
solving are "nothing more than" quite simple things technically.  
One doesn't need to be a technical genius to make valuable 
commercial contributions.  And maybe Hadoop and Spark are just 
the perfect solution for most people (maybe not!), but that 
certainly leaves some room for others.

So... data frames!?