65 lines
1.9 KiB
Markdown
65 lines
1.9 KiB
Markdown
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# Jupyter
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Despite being one of the most prolific modern languages, python suffers from a
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dangerous amount of not-invented-here syndrome. Prepare to fight on all fronts
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while python reinvents the wheels, though makes it square, just since a
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text-based terminal is too intimidating for new cs students
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Firstly you'll want to install python and all the other required packages:
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```
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# pacman -S python python-pip
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# pacman -S jupyter_console python-qtconsole python-ipython-genutils
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# pacman -S python-pynvim # For neovim only
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```
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Next you'll want to generate the configs required for Vim to connect with
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Jupyter. Follow this section of the
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[readme](https://github.com/jupyter-vim/jupyter-vim#jupyter-configuration).
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Jupyter does not come with manpages, though it does give useful support by using
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`-h` for any of its subcommands
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Now we need to make a virtual environment, otherwise pip will pollute package
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dependencies all over the system. Create a new environment directory:
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```
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$ python3 -m venv new_env_dir
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$ cd new_env_dir
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$ . bin/activate
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```
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Now you can install packages with pip, so long as the prompt stays "activated".
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If you aren't sure, open another pane and reactivate from there
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```
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$ python -m pip install ipykernel
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$ python -m pip install numpy matplotlib networkx
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```
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Trying to use Jupyter now still won't work. Since virtual environments are very
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thin layer that just modifies some environment variables, Jupyter will still run
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as if it's being run from a normal shell. We'll need to attach this venv as a
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separate kernel on the system Jupyter installation. For example:
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```
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$ python3 -m ipykernel install --user --name=my_kernel_name
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```
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Now Jupyter should see it. You can use any shell from here on, not just the
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venv-activated one
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```
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$ jupyter kernelspec list
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```
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We can connect to this kernel with an option
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```
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$ jupyter console --kernel=my_kernel_name
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```
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Removing the kernel later is easy too
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```
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$ jupyter kernelspec remove my_kernel_name
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```
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