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:[email protected] 41 Buckmaster Road m: +44 7770 465 077 xmpp: [email protected] London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
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