Introduction to Python environments

Installing Python is the single most important thing about learning Python. Yet, it is also one of the most complicated. There’s nothing too intrinsically difficult here; it’s just that there’s tons of options, they are all evolving, and they can interfere with each other in weird ways. This guide is somewhat opinionated, and will only cover one Python installation in detail (miniforge). We will, however, try to describe the principles involved, so that you can diagnose issues with other Python installations, and understand when things go wrong.

Why are there lots of ways to get Python?

A lot of newcomers are confused when starting out with Python, because there isn’t just Python — there’s Anaconda, miniconda, Python(x,y), — nowadays you can even get Python from the Windows App Store!

Plus, on most Linux distributions, there is a python command built-in. Is that all you need?

All of this happens because of Python’s great strength: it’s open source. This means that anyone can package their own Python and distribute it as an app, as long as they think they can do it better, in some way, than what’s out there. (And even if they don’t! You can distribute your own version of Python just for fun!)

Let’s untangle it all.

It’s best to think of Python not as a program, but as email. Just like you can send and receive email using a variety of programs, such as Gmail, Outlook, or Apple Mail, so too can all these different versions of Python run Python programs — but each with different features, advantages, disadvantages.

And, just like you can have many different email programs installed on your phone or on your computer, so too can you have lots of different Python versions on your computer. Probably 95% of installation problems have to do with people not knowing which Python they are using out of the multiple versions on their computer.

To help you preempt these issues, here we show you the single most important diagnostic when troubleshooting your Python installation problems. In your Python program, whether that program is commands entered into the Python prompt, a .py file, or a Jupyter notebook, type:

import sys

The output will look something like this:

>>> import sys
>>> print(sys.executable)
>>> print(sys.path)
['', '/home/jni/miniconda3/envs/upfs/lib/', '/home/jni/miniconda3/envs/upfs/lib/python3.9', '/home/jni/miniconda3/envs/upfs/lib/python3.9/lib-dynload', '/home/jni/miniconda3/envs/upfs/lib/python3.9/site-packages']

The first print function call prints the location of the interpreter running your code — in the email analogy, this is the same as the email program with which you are reading or sending email. Most usually, when you install a Python package but can’t access it, it’s because you installed it for a different Python interpreter.

Similarly, sys.path tells you the folders within your computer where Python is looking for programs or libraries to import. If you install a library with conda or pip, it will end up in one of these folders. If it’s not there, or it’s a different version from the one you expect, it usually means, again, that you’ve installed it into a different Python installation.

Together, these two variables make up an environment. (Well, to a first approximation; in fact, other information is needed, such as where to look for C libraries that Python depends on. But in most cases, you only need to care about which interpreter and which Python libraries you’re using.)

Managing environments

Hopefully, the above ideas are enough to keep you oriented in the face of different versions of Python and different Python environments within your machine. When in doubt, don’t panic and print sys.executable! Now that you have a compass, though, let’s see how you can create and navigate environments. Once you can create, modify, and destroy environments at will, you’ll be able to marshall the entire Scientific Python ecosystem!

In Python, there are two main tools to manage environments: virtualenv, and conda. Various other tools, such as pipenv and mamba, give you options to manipulate either of those two types of environments. The concepts for all these tools are the same, so we will focus on conda and leave you, brave reader, to figure out the other tools when necessary, armed with the concepts from this chapter. Indeed, a good exercise when you are done with this chapter is to uninstall everything, then start from scratch with mamba.

The first thing to do is to get conda. Confusingly, even now we have choices to make: we can either install miniconda or Anaconda. Miniconda is just conda, and what we will install going forward. Anaconda includes miniconda as well as a whole bunch of Scientific Python packages hand-compiled by the engineers at Anaconda, Inc, all neatly bundled into a single, massive environment. Although this can be convenient, in our case we want to create, modify, and destroy environments at will, so we will skip the One Huge Environment To Rule Them All.

In fact, we will not download miniconda itself, but rather miniforge, a different version of miniconda that searches for packages in the conda-forge open repository, rather than the main Anaconda channel, which is proprietary. As of this writing, you can download miniforge at, but if that changes, simply search the web for “download miniforge”. Follow the installation instructions on the site, and launch a command line terminal. This will be different depending on your operating system. On Mac and Linux, you will have an application called Terminal. (Though on Mac we recommend you download and install the excellent iTerm, which fulfills the same function, but better.) On Windows, the picture is more complicated, so much so that we have given it its own chapter. See “Navigating Windows Terminals” for details [Editorial note: this chapter has not yet been written.]. We recommend using Anaconda Prompt in this chapter.

If you’ve correctly installed miniconda, you shoud be able to launch a terminal and type:

conda --help

and get a message about conda usage.

The Prime Directive of conda environments

In a rather meta turn, conda itself lives in its own little conda environment, called base. In what I consider to be a grave mistake, conda allows you to install other packages into this environment, and generally wreak all sorts of havoc with it.

I instead recommend that you treat this environment as untouchable, and always work in other environments. To reiterate: never install packages into the base conda environment! All other environments are disposable, but the base one is harder to replace, so you should only change it to update conda itself, using conda update --name base conda.

Your first environment

Ok, with that out of the way, let’s create our first conda environment. We do this with the conda create command. Let’s make a Python 3.9 environment:

conda create -n py39 python=3.9

Once that’s done, you can activate the environment, which means setting that specific installation of Python as the one that subsequent commands will use. This setting will be active for that specific terminal session until it’s closed or you conda deactivate, or you conda activate a different environment. Do this with:

conda activate py39


if the above command fails with an error message similar to “conda command not found”, you need to set up your terminal to correctly find conda. See the “terminals and shells” chapter for more.

Exercise: switching environments

You now have at least two environments: Your base environment, and the py39 environment. Try to get comfortable with switching between them, and understanding how they cause you to invoke different versions of Python. Try typing in the following commands one by one:

conda deactivate
which python  # or "where python" in some Windows shells
python -c "import sys; print(sys.executable)"
conda activate py39
which python
python -c "import sys; print(sys.executable)"
which pip
pip install numpy
python -c "import numpy; print(numpy)"
conda deactivate
python -c "import numpy; print(numpy)"

The last command should crash with an import error, because we have not installed NumPy in the base environment, only in the py39 environment.

You now have a Python 3.9 environment, and you have NumPy installed within it, and you can run Python programs that use NumPy within it.

Now, let’s try to create some other environments, and run Python in different ways. Try the following commands:

conda create -y -n jupy38 python=3.8
conda activate jupy38
pip install jupyter notebook
jupyter notebook

The last command should open a browser to the launch page of Jupyter Notebook.

[Editorial note: insert screenshot]

Try creating a new notebook. Will you be able to import numpy within it? If you type the following code into a cell and run it, what is the output?

import sys

Working with IDEs

Integrated Development Environments, or IDEs, are text editors that come with additional features to help you quickly evaluate and run your code. Examples of IDEs include Visual Studio Code, PyCharm, and Spyder.

In order to help you run your code, IDEs must use a Python executable. But we’ve just seen that you can have many executables on your computer, and that they won’t all have the same libraries and features installed. In fact, without knowing which Python executable you are working with, the IDEs can’t tell you whether your code is correct, because different Python versions have different features. The code print(f'{5+8}') is valid in Python 3.6 or later, but won’t work in Python 3.5 or earlier.

The solution, as you might expect, is to tell the IDE which actual Python interpreter you want to work with. All IDEs have a facility to do this. The purpose of this section is just to tell you about it, so you can code more effectively with IDEs!

Exercise: using the correct interpreter

Download and install Visual Studio Code, an open source IDE, and install the Python extension. (It will recommend it to you on first launch.)

Start a new project with Visual Studio Code. Go to the settings, and set the interpreter to be the Python returned by sys.executable in the jupyter exercise.

Now, create a new file in your project, and start with:

import numpy as np

VSCode should complain that you do not have pylint installed. pylint is a linter, a computer program that analyses other computer programs to check them for errors. When you ask VSCode to install it, it will then highlight the import numpy line, because we did not install NumPy in the jupy38 environment! This is the power of linters: They can tell us our programs are wrong even befdore we run them. Working with a linter can make you much more productive.

At this point, we have two options: we can either install NumPy in the jupy38 environment (and indeed VSCode will suggest this), or we can change the interpreter to the py39 environment, which does have NumPy. (Though we will then need to install pylint again.) We leave the choice to you.

The nuclear option

We warned earlier that it’s dangerous to install packages to your system Python. Why wouldn’t it be dangerous in an environment? The answer is, it is, but environments are disposable, and you should see them as such. Once you do, dealing with environments becomes very freeing, as opposed to another complicated step to get Python to work.

Installing new libraries to an environment can often lead to strange failures, because libraries often depend on other libraries in version-specific ways, and they don’t always correctly declare this version specificity. For example, scikit-image 0.14 depended on NumPy 1.10 and higher, but when NumPy 1.15 was released, it inadvertently broke some functionality in scikit-image. Therefore, if you installed scikit-image to an environment before NumPy 1.15 was released, and then subsequently updated NumPy, you would have ended up with a broken environment for image analysis. Even if you don’t consciously update packages, you might decide to install some other package that depends on NumPy 1.15 or higher. Since scikit-image did not know ahead of time that a later NumPy release would break it (though in this particular instance they should have, because they were using a private NumPy function), there was nothing to prevent this from happening — package dependency metadata is fixed at release time.

Sometimes, these failures are easy to detect and fix. Sometimes, it is very difficult or impossible. In many cases, the failures are caused by a strange convergence of events, caused by the exact order and versions of packages installed.

This works to our advantage. When an environment breaks, and we can’t readily fix it, just nuke it and start again! Let’s try this out:

conda create -y -n borked python=3.7
conda activate borked
pip install scikit-image==0.14.0 numpy==1.14.3

Now, check that this works:

python -c "from skimage import util"

Now, suppose you want to visualize some 3D image data in your project, so you now want to install napari. Let’s try it:

pip install napari[qt]

Notice how it upgraded NumPy, but not scikit-image. Now, we try the same code:

python -c "from skimage import util"

Boom. Our environment broke. Now, in this very specific case, upgrading scikit-image would fix things. But things aren’t always that easy, or obvious. So, an easy way to proceed is to start again!

Exercise: nuke, rinse, repeat

Delete the borked environment, and create a new one with current versions of NumPy, scikit-image, and napari.


That’s it! We hope this chapter has made you a bit more confident to try out different versions of Python, different library versions, everything! The fastest way to learn is to try lots of things, so by making it easier for you to try things, environments are a key to help you learn Python quickly.

If you’ve gone through this whole chapter and are still feeling tentative, remember the nuclear option, and try again! As a final exercise, why don’t you try:

  • remove all your conda environments, and try to repeat this whole chapter using mambaforge, a from-scratch implementation of miniforge.

  • replace all the pip install commands with mamba install commands

  • repeat the chapter using to download Python, and virtualenv to create environments

  • if you have access to another computer, try everything again on that other computer.

Once you have messed with environments enough times, all of this becomes easier to deal with. And remember: even the experts regularly end up with broken environments and have to start from scratch! If your environment is broken, don’t panic, conda env remove, and carry on!