To make PyLab an easy to use, well packaged, well integrated, and well documented, numeric computation environment so compelling that instead of having people go to Python and discovering that it is suitable for numeric computation, they will find PyLab first and then fall in love with Python. The philosophy behind this vision is to consider Rails and Ruby; while Ruby was somewhat popular beforehand, it was Rails which propelled it to the forefront. At the moment, the current combination of Python, NumPy, SciPy, Matplotlib, and IPython provide a compelling environment for numerical analysis and computation. Unfortunately, for those who are not already familiar with Python and the intricacies of how to build your own Python environment, or for those not familiar with the details of how there are conflicting names exported by different modules, or how the best list of NumPy examples is found on the wiki in a non-obvious place (and that the docstrings are not the best documentation), or that the speed of linear algebra operators is dependent on a carefully compiled combination of LAPACK, ATLAS, and Goto BLAS, or a host of other reasons (some outlined below), the picture is not nearly so rosy.

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References in zbMATH (referenced in 3 articles )

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  1. Rieck, Konrad; Wressnegger, Christian: Harry: a tool for measuring string similarity (2016)
  2. Guttag, John V.: Introduction to computation and programming using Python (2013)
  3. Rieck, Konrad; Wressnegger, Christian; Bikadorov, Alexander: Sally: a tool for embedding strings in vector spaces (2012) ioport