conda environment broken after updating conda - pip

Question

after using conda extensively for a while, I was asked to update it yesterday and now things look broken. I have to admit that I am not an expert of what goes on behind the scenes so bear with me :)
After installing conda I used pip to install the various packages.
Yesterday I started working on some code from a tutorial on git, which suggested to create an ad-hoc environment to work with:
conda env create -f binder/environment.yml
At this step I was suggested to update conda, since I was using an old version. I can't find in my terminal that specific message (i.e. I can't find what version of conda I was using before), but here is what I get now when I check the version of conda:
(base) francesco ~$ conda list conda
# packages in environment at /Users/francesco/anaconda3:
#
# Name Version Build Channel
_anaconda_depends 2019.03 py36_0
anaconda custom py36_1
anaconda-client 1.7.2 py36_0
anaconda-navigator 1.8.7 py36_0
anaconda-project 0.8.4 py_0
conda 4.8.3 py36_0
conda-build 3.10.5 py36_0
conda-env 2.6.0 h36134e3_0
conda-package-handling 1.6.0 py36h1de35cc_0
conda-verify 3.4.2 py_1
One of the things I noticed after the update is that in order to activate/deactivate the environment I had to use conda activate/deactivate <env> instead of source activate/deactivate <env>.
After that I worked with the code in the new environment without any problem.
Today I tried to activate the main environment I work with, but I was asked to "init" my shell first with:
conda init bash
After that I activated my "usual" environment:
conda activate testenv
and I tried to start Jupyter Lab, but I got this error:
(testenv) francesco ~$ jupyter lab
Error executing Jupyter command 'lab': [Errno 2] No such file or directory
What is happening? Why is Jupyter Lab not working anymore in my usual environment?
I checked the installation in testenv and things look ok:
(testenv) francesco ~$ conda list | grep jup
jupyter 1.0.0 py37_7
jupyter_client 5.2.4 py37_0
jupyter_console 6.0.0 py37_0
jupyter_core 4.4.0 py37_0
(testenv) francesco ~$ pip list | grep jup
jupyter 1.0.0
jupyter-client 5.2.4
jupyter-console 6.0.0
jupyter-core 4.4.0
(testenv) francesco ~$
Does anyone know what is going on? This is a huge problem for me as conda/jupyter lab are the main tools I use for work :\

Answer 1

The common practice is to only install Jupyter in a single Conda environment (typically your base env if an Anaconda user), and always launch Jupyter from there. To use other Python envs in Jupyter, you need to install ipykernel in those envs, e.g.,
conda install -n testenv ipykernel
And, to avoid having to register your additional envs, it is recommended to install nb_conda_kernels in the env with Jupyter, e.g.,
conda install -n base nb_conda_kernels
As a side note, installing things with Pip can make environments unstable. I strongly recommend learning and adhering to the documented best practices.

Answer 2

#FrancescoLS It seems that you had perhaps an older version of Conda installed(?) as the CHANGELOG indicates that source activate was deprecated in favor of conda activate in Conda v4.4.0 (as is also noted in this "How to Get Ready for the Release of conda 4.4" post from Anaconda).
This really isn't an "answer" in any actionable way, but it seems that you are not alone in Conda update breaking people's environments.
superuser: Conda update conda does extra stuff and destroy my anaconda install
Conda Issue #8842: Conda Deletes All Modules During Update
there are lots more. :/
I think it is safe to say that keeping virtual environments safely intact during an upgrade is hard to do, and that when doing a major upgrade across virtual environment maintainers (Conda) it is even harder. This is one of the reasons that I personally try to have all of my project virtual environments be maintained either in native Python 3 venv virtual environments with pip or through Poetry (as they are (or at least used to be) way faster to restore than having to go through Conda's slower solver) and then only resorting to using Conda when I need to bring in multiple external binary applications.
Were you able to make a new Conda environment for your work that effectively restores it (perhaps from your own environment.yml file for that environment)?