Package managers#

Package managers help you install packages. Some help you install virtual environments as well. Better known python package managers include conda, pip, poetry

conda

pip

poetry

audience

research

all

developers

manage python packages

✅

✅

✅

manage non-python packages

✅

❌

❌

choose python version

✅

❌

❌

manage virtual envs

✅

❌

✅

easy interface

❌

✅

❌

fast

❌

✅

✅

Rules for choosing a package manager#

  1. Choose one

  2. Stick with it

Virtual Environments#

Python applications will often use packages and modules that don’t come as part of the standard library. Applications will sometimes need a specific version of a library, because the application may require that a particular bug has been fixed or the application may be written using an obsolete version of the library’s interface.

This means it may not be possible for one Python installation to meet the requirements of every application. If application A needs version 1.0 of a particular module but application B needs version 2.0, then the requirements are in conflict and installing either version 1.0 or 2.0 will leave one application unable to run.

The solution for this problem is to create a virtual environment, a self-contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages.

Different applications can then use different virtual environments. To resolve the earlier example of conflicting requirements, application A can have its own virtual environment with version 1.0 installed while application B has another virtual environment with version 2.0. If application B requires a library be upgraded to version 3.0, this will not affect application A’s environment.

- The Python Tutorial

Environment: Conda example#

There are several ways to create/manage python environments. We chose conda because it is the de facto standard in science, and because it can natively install libraries such as fftw, vtk, or even Python, R, and Julia themselves.

Create environment from a yml file#

Step 1: environment.yml#

The environment.yml file specifies the dependencies that will be installed in your environment.

An example of content for this file type is:

name: course
dependencies:
  - python>=3.8
  - numpy=1.13
  - matplotlib=3.*
  - pandas

  • The first line of the yml file sets the new environment’s name.

  • By defining dependencies you can:

    • Define the version number by fixing the major and minor version numbers.

    • Use the wildcard * to allow the patch version vary.

    • Use >= to set minimum version.

    • Do not specify version.

See also

For more details see Creating an environment file manually.

Step 2: create a new virtual environment#

conda env create -f environment.yml

Tip

The option -f is short for --file.

Other options includes:

  • -n or --name

  • -e or --envs

  • -h or --help

Warning

it is important to note that creating the environment does not automatically activate it.

Step 3: activate the environment#

conda activate course

Tip

Replace course with the environment name or directory path.

Note

You can also deactivate the environment at any time: just run conda deactivate.

Warning

conda activate and conda deactivate only work on conda 4.6 and later versions (use conda info to check conda version). For conda versions prior to 4.6, run:

  • Windows: activate or deactivate

  • Linux and macOS: source activate or source deactivate

Managing packages#

Search package#

Search for a package to see if it is available to conda install

conda search package-name

Install a new package#

To install a conda package in the current environment:

conda install package-name

Update an existing package#

To update a package in the current environment:

conda update package-name

Remove an existing package#

To remove a package from the current environment:

conda remove package-name

Note

If you are not in the environment, you still can perform the above procedures. To do so, it will be necessary to indicate the desired environment in the command, for example:

conda install -n ENVNAME package-name

List of packages#

To view a list of packages in active environment:

conda list

Managing environments#

List available environments#

To list all available environments:

conda env list

Remove an environment#

conda remove -n ENVNAME --all

Share an environment#

Create the latest yml environment file

conda env export --name ENVNAME > file-name.yml

Share the .yml file.

Tip

Note the difference between the environment.yml file created before with this latest file.

To create a clean yml environment file:

conda env export --name ENVNAME --no-builds | grep -v "prefix" > file-name.yml

Clone an environment#

Make an exact copy of an environment

conda create --clone ENVNAME --name NEWENV

See also

Check this Conda Cheat Sheet!