Environments#

An environment is a directory that contains a specific collection of packages that you have installed. For example, you may have one environment with NumPy 1.7 and its dependencies, and another environment with NumPy 1.6 for legacy testing. If you change one environment, your other environments are not affected. You can easily activate or deactivate environments, which is how you switch between them. You can also share your environment with someone by giving them a copy of your environment.yaml file. For more information, see Managing environments.

Conda directory structure#

ROOT_DIR#

The directory where the conda distribution was installed into.

EXAMPLES:

/opt/Anaconda  #Linux
C:\Anaconda    #Windows

/pkgs#

Also referred to as PKGS_DIR. This directory contains decompressed packages, ready to be linked in conda environments. Each package resides in a subdirectory corresponding to its canonical name.

/envs#

The system location for additional conda environments to be created.

The following subdirectories comprise the default Anaconda environment:

/bin
/include
/lib
/share

Other conda environments usually contain the same subdirectories as the default environment.

Virtual environments#

A virtual environment is a tool that helps to keep dependencies required by different projects separate by creating isolated spaces for them that contain per-project dependencies for them.

Users can create virtual environments using one of several tools such as Pipenv or Poetry, or a conda virtual environment. Pipenv and Poetry are based around Python's built-in venv library, whereas conda has its own notion of virtual environments that is lower-level (Python itself is a dependency provided in conda environments).

Scroll to the right in the table below.

Some other traits are:

Python virtual environment

Conda virtual environment

Libraries

Statically link, vendor libraries in wheels, or use apt/yum/brew/etc.

Install system-level libraries as conda dependencies.

System

Depend on base system install of Python.

Python is independent from system.

Extending environment

Extend environment with pip.

Extended environment with conda or pip.

Non-Python dependencies

Manages non-Python dependencies (R, Perl, arbitrary executables).

Tracking dependencies

Tracks binary dependencies explicitly.

Why use venv-based virtual environments#

  • You prefer their workflow or spec formats.

  • You prefer to use the system Python and libraries.

  • Your project maintainers only publish to PyPI, and you prefer packages that come more directly from the project maintainers, rather than someone else providing builds based on the same code.

Why use conda virtual environments?#

  • You want control over binary compatibility choices.

  • You want to utilize newer language standards, such as C++ 17.

  • You need libraries beyond what the system Python offers.

  • You want to manage packages from languages other than Python in the same space.

Workflow differentiators#

Some questions to consider as you determine your preferred workflow and virtual environment:

  • Is your environment shared across multiple code projects?

  • Does your environment live alongside your code or in a separate place?

  • Do your install steps involve installing any external libraries?

  • Do you want to ship your environment as an archive of some sort containing the actual files of the environment?

Package system differentiators#

There are potential benefits for choosing PyPI or conda.

PyPI has one global namespace and distributed ownership of that namespace. Because of this, it is easier within PyPI to have single sources for a package directly from package maintainers.

Conda has unlimited namespaces (channels) and distributed ownership of a given channel. As such, it is easier to ensure binary compatibility within a channel using conda.