Jupyter notebooks, jupyter kernels, virtual environment created in Anaconda...

[bytecrunch]

However, without creating a jupyter kernel specific for that environment via:

You don't install a jupyter kernel as mention this is done automatic when install JupyterLab
Here how i like to make a environment,make sure that use channel conda-forge and install a new Python 3.11.4 to this environment.
Make sure all is up to date before making environment conda update --all

conda create --name home_env -c conda-forge spyder jupyterlab pandas python=3.11.4

Now if activate home_env and test:

C:\anaconda3
(base) λ activate home_env

C:\anaconda3
(home_env) λ jupyter --version
Selected Jupyter core packages...
IPython          : 8.15.0
ipykernel        : 6.25.0
ipywidgets       : not installed
jupyter_client   : 7.4.9
jupyter_core     : 5.3.0
jupyter_server   : 1.23.4
jupyterlab       : 3.6.3
nbclient         : 0.5.13
nbconvert        : 6.5.4
nbformat         : 5.9.2
notebook         : 6.5.4
qtconsole        : 5.4.2
traitlets        : 5.7.1

See that ipykernel : 6.25.0 is installed.
If have trouble i would suggest to remove Anaconda and install the new version that are Python 3.11.

Hello snippsat,

I was re-reading your replies. thank you. Let me see if I understood correctly.

ISSUE: my issue was that, after creating a new virtual environment (ven1) and launching jupyter notebook from it (from the CLI of the activated environment), I notices that the notebook did NOT have access to the libraries/modules of (ven1) as I would have liked...

SOLUTIONS
There are two possible solutions:

1) Let's assume we already have Jupyter Notebook installed on our computer. We then create a virtual environment called (ven1). If we launch jupyter notebook from the command line of the activated environment, jupyter notebook will open but the notebook will NOT use the libraries and modules of (ven1), as we would instead like, just because the we opened the app from (ven1).
To solve that, we could install a new jupyter notebook inside (ven1) itself. At that point, when we type "jupyter notebook", the notebook will have access to (ven1) resources...That is redundant: having multiple jupyter notebooks, one for each environment, does not seem efficient.

2) The other and more efficient solution is to:
a) only keep a single Jupyter notebook installation
b) create the new virtual environment (ven1)
c) create a new jupyter kernel inside (ven1) with the steps described above (ipykernel, etc.).
d) after typing "jupyter notebook" at the CLI of activated (ven1), the notebook opens and we simply select the new jupyter kernel specific to (ven1) that we created for the notebook to have access to (ven1) resources...

Did I understand correctly? Thank YOU for your patience!