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|rss_image|  **2018-08 - 1/1** :ref:`Blog <ap-main-0>` :ref:`C++ (3) <ap-cat-C++-0>`

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    :target: ../_downloads/rss.xml
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.. index:: 2018-08


.. _ap-month-2018-08-0:

2018-08 - 1/1
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.. blogpostagg::
    :title: Cython, Pythran, nuitka, numba
    :date: 2018-08-05
    :keywords: Cython,Pythran,nuitka,numba
    :categories: C++
    :rawfile: 2018/2018-08-05_pythran.rst

    I discovered than :epkg:`Pythran` was now a possible
    backend for :epkg:`Cython`:
    `Pythran as a Numpy backend
    <https://cython.readthedocs.io/en/latest/src/userguide/numpy_pythran.html>`_.
    On one benchmark, it increases the speed by 2. I recommend the reading of
    the following article:
    `Optimizing your code with NumPy, Cython, pythran and numba
    <https://flothesof.github.io/optimizing-python-code-numpy-cython-pythran-numba.html>`_
    which instigates the performance brought by the four
    following tool and gives some hints on how to write
    efficient code with these tools:
    

    ...




.. blogpostagg::
    :title: Exploration with pybind11 and ExtensionArray
    :date: 2018-08-03
    :keywords: pybind11,C++,pandas
    :categories: C++
    :rawfile: 2018/2018-08-03_pybind11.rst

    I tried the version of :epkg:`pybind11`
    to expose a dummy C++ object
    :class:`WeightedDouble <cpyquickhelper.numbers.weighted.number.WeightedDouble>`,
    to implement a couple of operators and to see
    how it behaves into a dataframe.
    

    ...





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|rss_image|  **2018-08 - 1/1** :ref:`2018-08 (2) <ap-month-2018-08-0>` :ref:`2019-02 (1) <ap-month-2019-02-0>` :ref:`2019-03 (1) <ap-month-2019-03-0>` :ref:`2019-09 (1) <ap-month-2019-09-0>` :ref:`2021-01 (1) <ap-month-2021-01-0>`