Setting up Conda Virtual Env, Jupyer Notebooks and IPython

Creating Virtual Environments with Anaconda

Out of all the ways of accessing Python, Jupyter notebooks and virtual environments, using Anaconda Distribution is arguably the easiest.

1. Install Anaconda as you would any other Mac software (currently for Python 3.9 MacOS)
2. In Terminal:
3. check version $ conda --version
4. create environment variable $ conda create -n myenv where myenv is any environment name.
5. confirm environment location, e.g.: /Users/username/opt/anaconda3/envs/defi
6. activate environment: $ conda activate myenv
7. de-activate environment: $ conda deactivate

Note: in terminal you’ll notice differences between (base) and (myenv)

8. list out all available environments: $ conda env list

NOTE: Different virtual environments may have different version of python, a module, package or, in some cases, not have those things so they need to be re-installed within that virtual environment.

For example, opening IPython in base may be easier than in a virtual env:

$ python3 -m IPython  

vs

$ IPython

Add Virtual Environment to Jupyter Notebook

1. activate environment: $ conda activate myenv
2. install ipykernel: $ pip3 install --user ipykernel
3. install environment: $ python3 -m ipykernel install --user --name=myenv
4. confirm location: Installed kernelspec myenv in /Users/username/Library/Jupyter/kernels/myenv

Note: ipykernel is key

5. important: When loading Anaconda Navigator, select Environments, and find myenv you should see list of “installed packages”, including: pandas, numpy, ipython and jupyter among others.

6. Launch: a new Jupyter notebook from Python 3 (ipykernel)

Ipython

NOTE: This is from chapter 2 of Joel Grus’ ‘Data Science from Scratch’.

Joel’s a known opponent of notebooks and recommends operating in IPython instead.

I was pleasantly surprised that the process of setting up a virtual environment and IPython was relatively painless. Here’s my process, taken from the book with some tweaks:

# create a Python 3.6 environment named 'dsfs'
conda create -n dsfs python=3.6

# update conda to latest version (4.9.0)
conda update -n base -c defaults conda

# to activate virtual environment (named it 'dsfs' to keep it simple)
source activate dsfs

# install pip (note: currently using Python 3.8.5)
python3 get-pip.py

# install IPython 
python3 -m pip install ipython

# save IPython session
# save lines 1-21 in session to file initial_ipython_session.py
%save initial_ipython_session 1-21

# exit IPython
ctrl + D

# exit conda virtual environment
conda deactivate

Pulling up a saved IPython session in VSCode

note: I am using VSCode as my main python IDE outside of jupyter notebooks.

After you’ve saved an IPython session (see above), you may want to pull up the .py file for further edits at a later time. To do this, you’ll need to ensure that the code command for VSCode is installed.

Assuming you’re already in VSCode, press (I’m using macOS):

Command + Shift + P

Then select Shell Command: Install code in PATH. That’s it.

To open a previously saved IPython session in VSCode from the VSCode terminal, type:

% code name_of_file.py

Note that this can be done from (base) or from a previously configured virtual environment session, for example:

(base) paulapivat@Pauls-MacBook dsfs % code function_session.py
(base) paulapivat@Pauls-MacBook dsfs % source activate dsfs
(dsfs) paulapivat@Pauls-MacBook dsfs % code function_session.py