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Python's Bright Future in Science

Project: CPython, Cython, NumPy, SciPy, Numba, scikit-image

Over the past five years, Python has skyrocketed in popularity in the scientific world, pushing out proprietary languages such as IDL and Matlab. This rise was powered by simple syntax and efficient numerical libraries. But many operations in Python are still slow, and upstart languages, such as Julia and Go, promise simplicity *and* speed. Can Python cement its place in scientific computing?

Juan Nunez-Iglesias

Juan Nunez-Iglesias is a research scientist at the Victorian Life Sciences Computation Initiative, in the University of Melbourne. He uses image analysis and computer vision to study neural networks (the squishy ones, not the Googly ones), Malaria parasites, and other biological things. This work led him to the scikit-image library, for which he is now a core developer. He has taught at the SciPy conference, EuroSciPy, the ASPP summer school, and Software Carpentry workshops. He is co-author of the O'Reilly book, Elegant SciPy.