我也遇到过类似的问题，我找到了一个适用于Mac、Windows和Linux的解决方案。它需要上面答案中的几个关键成分:

为了能够看到conda env在Jupyter笔记本，你需要:

the following package in you base env: conda install nb_conda the following package in each env you create: conda install ipykernel check the configurationn of jupyter_notebook_config.py first check if you have a jupyter_notebook_config.py in one of the location given by jupyter --paths if it doesn't exist, create it by running jupyter notebook --generate-config add or be sure you have the following: c.NotebookApp.kernel_spec_manager_class='nb_conda_kernels.manager.CondaKernelSpecManager'

您可以在终端看到的环境:

在Jupyter实验室，你可以看到相同的env上面的笔记本和控制台:

当你打开笔记本时，你可以选择你的环境:

安全的方法是创建一个特定的env，从中运行envjupyter lab命令的示例。激活你的环境。然后添加jupyter实验室扩展示例jupyter实验室扩展。然后你就可以运行木星实验室了

可能的特定渠道问题

我有这个问题(再次)，原来我从conda-forge频道安装;将其移除并从蟒蛇通道重新安装，而不是为我修复它。

更新:我在一个新的env中再次遇到了同样的问题，这次我确实从anaconda通道安装了nb_conda_kernels，但我的jupyter_client来自conda forge通道。卸载nb_conda_kernels并重新安装会将其更新到更高优先级的通道。

所以请确保你从正确的渠道安装:)

nb_conda_kernels包是在conda中使用jupyter的最佳方式。通过最小的依赖关系和配置，它允许您使用运行在不同环境中的jupyter笔记本上的其他conda环境。引用其文件:

Installation This package is designed to be managed solely using conda. It should be installed in the environment from which you run Jupyter Notebook or JupyterLab. This might be your base conda environment, but it need not be. For instance, if the environment notebook_env contains the notebook package, then you would run conda install -n notebook_env nb_conda_kernels Any other environments you wish to access in your notebooks must have an appropriate kernel package installed. For instance, to access a Python environment, it must have the ipykernel package; e.g. conda install -n python_env ipykernel To utilize an R environment, it must have the r-irkernel package; e.g. conda install -n r_env r-irkernel For other languages, their corresponding kernels must be installed.

然后你需要做的就是启动jupyter笔记本服务器:

conda activate notebook_env  # only needed if you are not using the base environment for the server
# conda install jupyter # in case you have not installed it already
jupyter

尽管有太多的答案，@merv也在努力改进，但仍然很难找到一个好的答案。我做了这个CW，所以请投票给它的顶部或改进它!

This has been so frustrating, My problem was that within a newly constructed conda python36 environment, jupyter refused to load “seaborn” - even though seaborn was installed within that environment. It seemed to be able to import plenty of other files from the same environment — for example numpy and pandas but just not seaborn. I tried many of the fixes suggested here and on other threads without success. Until I realised that Jupyter was not running kernel python from within that environment but running the system python as kernel. Even though a decent looking kernel and kernel.json were already present in the environment. It was only after reading this part of the ipython documentation: https://ipython.readthedocs.io/en/latest/install/kernel_install.html#kernels-for-different-environments and using these commands:

source activate other-env
python -m ipykernel install --user --name other-env --display-name "Python (other-env)"

我能让一切顺利进行。(我实际上没有使用-user变量)。

我还没有想到的一件事是如何将默认的python设置为“python (other-env)”。目前，从主屏幕打开的现有.ipynb文件将使用系统python。我必须使用内核菜单“更改内核”来选择环境python。

我不认为其他答案是工作了，因为conda停止自动设置环境作为jupyter内核。您需要手动为每个环境添加内核，方法如下:

source activate myenv
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

如下所示:http://ipython.readthedocs.io/en/stable/install/kernel_install.html#kernels-for-different-environments 请参见本期。

附录: 您应该能够使用conda install nb_conda_kernels安装nb_conda_kernels包来自动添加所有环境，请参阅https://github.com/Anaconda-Platform/nb_conda_kernels

对于conda 4.5.12，适用于我的是(我的虚拟环境被称为nwt)

conda create --name nwt python=3

之后，我需要激活虚拟环境并安装ipykernel

activate nwt
pip install ipykernel

那么对我有效的方法是:

python -m ipykernel install --user --name env_name --display-name "name of your choosing."

例如，我使用'nwt'作为虚拟env的显示名称。在运行上面的命令之后。再次在Anaconda Prompt中运行“jupyter notebook”。我得到的是: