What D Needs…

[Russel Winder via Digitalmars-d]

Contributing to the "What D needs to get traction" debate ongoing in
various threads, a bit of feedback from the PyData London 2015 day
yesterday (I couldn't get there Friday or today).

Data science folk use Python because of NumPy/SciPy/Matplotlib/Pandas.
And IPython (soon to be Jupyter). Julia is on the radar, but…

NumPy is actually relatively easy to crack (it is just an n-dimensional
array type with algorithms), which means most of SciPy is
straightforward (it just adds stuff on NumPy). Matplotlib cannot be
competed against so D needs to ensure it can very trivially interwork
with Python and Matplotlib. C-linkage and CFFI attacks much of this,
PyD attack much of the rest. This leaves Pandas (which is about time
series and n-dimensional equivalents) and Jupyter (which is about
creating Markdown or LaTeX documents with embedded executable code
fragments).

If D had a library that attacked the capabilities offered by Pandas and
could be a language usable in Jupyter, there is an angle for serious
usage as long as D performs orders of magnitude faster than NumPy and
faster than Cython code.

At the heart of all this is a review of std.parallelism to make sure we
can get better performance than we currently do.
 
-- 
Russel.
=============================================================================
Dr Russel Winder      t: +44 20 7585 2200   voip: sip:russel.winder at ekiga.net
41 Buckmaster Road    m: +44 7770 465 077   xmpp: russel at winder.org.uk
London SW11 1EN, UK   w: www.russel.org.uk  skype: russel_winder
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