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Frequently asked questions

What is a conda channel?

Conda channels are the locations where conda packages are stored. By default, packages are automatically downloaded and updated from the default channel, but other channels (i.e., conda-forge) can be specified using the --channel flag, as shown in the example below:

conda install rust --channel conda-forge

For more information, please check out the conda documentation page on channels.

What is conda-forge?

Conda-forge is a community-led GitHub organization that provides access to thousands of conda package recipes. All of these recipes are open source and can be installed with the conda package manager by specifying conda-forge as the channel.

What is a feedstock?

A feedstock is a conda package repository.

How can I search for packages?

There are many ways to search for packages, either in your browser or in the command line. Many package organizations keep lists of their packages on their websites, so you can either use a search engine or go directly to package organization websites like and Anaconda also provides package hosting on

If you want to use conda to search for packages, use the command conda search. Enter conda search -h for more information.

What is the difference between conda create and conda env create?

conda create is a command that creates a conda environment with a custom name (listed after the -n flag) or full path to environment location, also known as the prefix (indicated by the -p flag). This command can also specify packages to install into that environment at the same time as creating it. Below is an example of this command being used to create an environment named new-env, installing Python 3.9 along with package_name1 and package_name2:

$ conda create -n new-env python==3.9 package_name1 package_name2

conda env create is a command that creates a conda environment based on an environment definition file. Typically, the environment name is stated in the first line of the environment.yml file (this is the default name of an environment definition file), but it can be named other things as long as you specify the file name in the command using the -f flag. For example, the following command will enable you to create a conda environment that is defined in a file called my-exported-env.yml:

$ conda env create -f my-exported-env.yml

Why should I use conda and not just install everything with pip?

Pip can only install Python packages and (unlike conda) cannot account for the dependency graphs connected to each package that it installs, which can break global system dependencies and/or the user's dependency stacks. Even when using pip with a tool like virtualenv, which creates isolated Python environments, it can still inadvertently install Python packages to the wrong places.

On the other hand, conda is a powerful package and environment manager that can install much more than just Python libraries. With conda, users can install entire software stacks (while remaining assured that all dependencies are accounted for and resolved), as well as R programs and libraries, Node.js, Java programs, C++ programs and libraries, Perl programs, and more. Conda has an environment management system that allows users to have all of these installed across multiple different environments; it also enables installation of complex software stacks on a system without needing root privileges, due to it being able to do all of these software and package installations in an isolated, userspace manner.

The Python packaging system is prone to develop incompatibilities over time; the more packages you install into one conda environment, the more complex the dependency graph gets, which makes the default base environment prone to problems and breakage each time another package is installed.

For this reason, it is highly recommended to utilize separate conda environments for each project/purpose in order to mitigate the dependency management issues of the Python packaging system and to keep project dependencies as separate and simple as possible.

What is a dependency graph?

A dependency graph is a tree-like data structure where each node points to all of the things that it depends on. Then, each of those dependency nodes point at all of their particular dependencies, and so on. Simply put, it's a graph that represents how objects depend on each other.

Each separate conda environment would have its own dependency graph. The items in a dependency graph would be the packages that conda manages and what each of those packages require as a prerequisite to function properly.

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