在Python中我们可以使用Jupyter Notebook 直接看到结果,例如: l = [1,2] l
直接输出: [1,2]
那当使用C++的时候,例如: map<string, int> mp{ {'one', 1}, {'two', 2}, {'three', 3}, {'four', 4} };
如果要输出,就得循环遍历,可否直接输出结果呢? so easy!!! Jupyter Notebook 可以解决一切问题,哈哈~ 看下图: 如何在Jupyter中玩C++?在github上有一个仓库,如下所示: https://github.com/QuantStack/xeus-cling
xeus-cling 是一个用于C++的Jupyter内核,基于C++解释器和Jupyter协议xeus的原生实现。
目前,支持Mac与Linux,但不支持Windows。 安装也是非常简单,首先安装好Anaconda,在里面创建一个虚拟环境: conda create -n cling
切换进去: conda activate cling
给新环境安装jupyter 和notebook conda install jupyter notebook
使用conda-forge 安装xeus-cling conda install xeus-cling -c conda-forge
为了加速安装,请记得给Anaconda配置源! 检查是否安装好了内核(kernel): jupyter kernelspec list
输出: python3 /home/xxx/anaconda3/envs/cling/share/jupyter/kernels/python3 xcpp11 /home/xxx/anaconda3/envs/cling/share/jupyter/kernels/xcpp11 xcpp14 /home/xxx/anaconda3/envs/cling/share/jupyter/kernels/xcpp14 xcpp17 /home/xxx/anaconda3/envs/cling/share/jupyter/kernels/xcpp17
打开Jupyter Notebook ,就可以看到看到kernel了。 启动Jupyter Notebook : jupyter-notebook
(其中多了一个C,是因为也装了C kernel,看后面) 示例测试: 如何在Jupyter中玩C?只需要安装c kernel即可! 可以直接在当前环境中创建c kernel,也可以新开一个环境安装,下面是在当前环境中直接安装。 pip install jupyter-c-kernel install_c_kernel jupyter kernelspec list
此时,就输出: c /home/light/anaconda3/envs/cling/share/jupyter/kernels/c python3 /home/light/anaconda3/envs/cling/share/jupyter/kernels/python3 xcpp11 /home/light/anaconda3/envs/cling/share/jupyter/kernels/xcpp11 xcpp14 /home/light/anaconda3/envs/cling/share/jupyter/kernels/xcpp14 xcpp17 /home/light/anaconda3/envs/cling/share/jupyter/kernels/xcpp17
启动Jupyter Notebook : jupyter-notebook
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