Intro to development

In this section you will:

Create an isolated software area (“virtual environment”) to work in.
Open a code editor.

Development environment

If you want to work on Python software, you should always have a virtual environment, an area to install the software that is isolated from other programs running on your system. A user may not always have one, but a developer always should.

Why? You do not want to risk interfering with your main system environment, you want full control over versions of libraries, and you want to avoid letting your main environment “leak” in, causing you to not notice when you have extra dependencies. Virtual environments are disposable—it’s no big deal to just delete one and start over—while it is very hard to clean up or update a main system environment.

There are many (arguably too many) ways to do this in Python. Any modern tool should be fine, but here we recommend two options popular in the scientific Python community. If you already use a different way, that’s fine, use that.

Option 1: Using pip

The most basic option is to use venv, which comes by default with Python.

python3 -m venv .venv

This creates a new virtual environment in a local folder, named .venv. You only need to do this step once, per project, per machine you work on. Later, if something about your software installation breaks and you want to start fresh, you can simply delete this directory to remove all trace of the virtual environment.

To activate the virtual environment, type:

. .venv/bin/activate

You need to do this step every time you open a new shell (i.e. Terminal, Command Prompt) to work on your project. The . is short for source, which runs the script activate in your current shell. This causes pip and python to work within this new environment, isolated from system-wide packages. It adds a bit of text to your prompt so you don’t forget that you are in an environment.

The activation script installs a function deactivate; type that at any time to leave the environment (or just close your shell).

Optional Alternative

Alternatively, you can use the virtualenv package, which has the same syntax as venv and is a little faster. Unlike venv, it is not built in to Python; it has to be installed as pip install virtualenv.

For Advanced Users Using Custom Shells

If you like a different shell, like fish, there are several activate scripts; the default one expects a bash-like shell.

Fast alternative

You can also consider the uv package, which is much, much faster version of pip and venv implemented in Rust. Just put uv in front of the commands you’d normally use; as long as you use venvs, it should be nearly the same. You can use --system to mimic pip, otherwise it uses the active virtualenv or a .venv folder. uv venv will default to a .venv folder.

This also supports --exclude-newer DATE, which allows you to resolve as if you were at some past point in time.

Option 2: Using conda

You can also develop in conda. This is an especially good option if:

You want an easy way to choose a specific version of Python
Your project will depend on complex software libraries that are not readily pip-installable

The creation of an environment looks like this:

conda create -n env_name python=3.11

You can use -n name or -p path to specify the environment by name or location. The above assume you used a name, but just replace names with paths if you choose a path.

To activate an environment:

conda activate env_name

To deactivate, use conda deactivate, or leave your shell.

Modern faster alternative

You can also consider the pixi package, which is much, much faster version of conda implemented in Rust. It has been completely redesigned to work around projects with a pixi.toml file instead of global environments. It’s a new way to work (more similar to pdm/hatch/poetry from Python), but very powerful if you learn it.

Choosing an Editor

Any plain text editor will serve our purposes for this guide. Bare bones editors like Notepad or nano will do the job. More feature-rich Integrated Development Environments (IDEs) such as PyCharm or Visual Studio Code can provide useful additions, such as syntax highlighting, tab completion, and more. Classic text editors like vim and emacs can do the same with configuration, and are often readily available on shared institutional systems.

You can instruct an IDE to use the virtual environment (pip or conda) that you set up. Setting up an IDE takes extra time but often provides tools (like smart renaming) that are useful, and if you use type hints, will probably pay for the setup time quite quickly when developing.