Re:Welcome to the Python Engineering blog
by toyg on Sunday February 21, 2016 @04:26PM
(#11146625)
A lot of those actually started as wrappers to existing C/C++/other libs, something that is very easy to achieve in Python and not so much with other runtimes.
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To be sure, NumPy is the third implementation (Numeric, numarray, NumPy) of the basic array data type, so getting it right isn't as easy to achieve even in Python.
Re:Welcome to the Python Engineering blog
by Scarblac on Monday February 22, 2016 @03:27AM
(#11149382)
And when library X and unrelated library Y are wrapped, they often both use Numpy arrays, making them interoperable.
E.g. if you do geographical work with raster data, there is GDAL, a raster library with Python wrappers. Because it exposes the raster data as Numpy array, you can easily make images from them to overlay on maps with matplotlib, or analyze them using scipy. In C/C++ it would probably be a lot more hassle to combine them like that.