How to list package versions available with conda

To search for a specific package, use: conda search -f <package_name>. For example, based on the question, to search all versions for “jupyter” package, you’ll do: conda search -f jupyter. This will only return information about packages named “jupyter” exactly.

Source: https://docs.conda.io/projects/conda/en/latest/commands/search.html

As an addendum, you can use the output of conda search to fine-tune the version of the package you need installed. E.g. in the list from the ‘nasica88’, there are three albaster 0.7.7 versions available with with different python versions. If you require e.g. albaster 0.7.7 with python 3.4, you install it as following:

$> conda install albaster=0.7.7=py34_0

So, the second = sign is your friend here.

To list packages that are installed on your anaconda machine

conda list

This is to list all packages available for anaconda

conda search

To trim down the long and slowly loading conda search output to just the (latest) version(s) appropriate for your environment, you can use MatchSpec filters, as documented here in conda Github repo

For example:

$ conda search "conda-forge::*[name=scikit-learn, subdir=linux-64, build=*py37*]" | tail -n5
scikit-learn                  0.21.2  py37h627018c_0  conda-forge
scikit-learn                  0.21.2  py37hcdab131_1  conda-forge
scikit-learn                  0.21.3  py37hcdab131_0  conda-forge
scikit-learn                    0.22  py37hcdab131_0  conda-forge
scikit-learn                    0.22  py37hcdab131_1  conda-forge

Note that the most recent version is placed at the bottom of the list (they are sorted in ascending chronological order), so it can be found using tail -n1, e.g.:

$ conda search "conda-forge::*[name=scikit-learn, subdir=linux-64, build=*py38*]" | tail -n1 | awk {'print $2'}
$ 0.23.2

Cautions:

• using version for narrowing down major and/or minor version is risky, because version=1.*.* would miss versions such as 1.1 or 1,

• setting architecture (using subdir key) to linux-64 can miss some useful linux 64-bit packages, if they are stored in the noarch folder instead of linux-64

If you know the name of the package you want to install search for all available versions of it. eg. for package pandas you will do the following

conda search pandas

and then install the version you want using

conda install pandas=1.0.2

To get the version of certain package you can filter it by grep Like:

$ conda list | grep tensorflow

Result:

tensorflow                2.2.0           mkl_py36h5a57954_0  
tensorflow-base           2.2.0           mkl_py36hd506778_0  
tensorflow-estimator      2.2.0              pyh208ff02_0  

I installed pip in conda, so pip list also works

To control specific channels, use -c option. For example:

conda search -c conda-forge jupyterlab

The above will also search in the channels listed in .condarc, so to avoid that (and get results faster) one can use --override-channels:

conda search -c conda-forge --override-channels jupyterlab

To only show versions above a specific release, use "{package}>={release}". For example:

conda search -c conda-forge "jupyterlab>=3.5"

Note that some shells (esp. Windows) do not like single quotes, so using double quotes is safer.

Finally, if you intend to use the output in a program, to avoid parsing the results one can use --json:

conda search -c conda-forge --override-channels --json "jupyterlab>=3.6"

This will return:

{
  "jupyterlab": [
    {
      "arch": null,
      "build": "pyhd8ed1ab_0",
      "build_number": 0,
      "channel": "https://conda.anaconda.org/conda-forge/noarch",
      "constrains": [],
      "depends": [
        "ipython",
        "jinja2 >=2.1",
        "jupyter_core",
        "jupyter_server >=1.16.0,<3",
        "jupyter_server_ydoc >=0.6.0,<0.7.0",
        "jupyter_ydoc >=0.2.2,<0.3",
        "jupyterlab_server >=2.19,<3",
        "nbclassic",
        "notebook <7",
        "packaging",
        "python >=3.7",
        "tomli",
        "tornado >=6.1.0"
      ],
      "fn": "jupyterlab-3.6.0-pyhd8ed1ab_0.conda",
      "license": "BSD-3-Clause",
      "license_family": "BSD",
      "md5": "1a9cd36192678fc2175145c9103b95ff",
      "name": "jupyterlab",
      "noarch": "python",
      "package_type": "noarch_python",
      "platform": null,
      "sha256": "66da471830af4f5a7baa6229240c9dfe0fcc43bf20cc576067dab742bf5ec02e",
      "size": 5827178,
      "subdir": "noarch",
      "timestamp": 1675350928375,
      "url": "https://conda.anaconda.org/conda-forge/noarch/jupyterlab-3.6.0-pyhd8ed1ab_0.conda",
      "version": "3.6.0"
    },
    {
      "arch": null,
      "build": "pyhd8ed1ab_0",
      "build_number": 0,
      "channel": "https://conda.anaconda.org/conda-forge/noarch",
      "constrains": [],
      "depends": [
        "ipython",
        "jinja2 >=2.1",
        "jupyter_core",
        "jupyter_server >=1.16.0,<3",
        "jupyter_server_ydoc >=0.6.0,<0.7.0",
        "jupyter_ydoc >=0.2.2,<0.3",
        "jupyterlab_server >=2.19,<3",
        "nbclassic",
        "notebook <7",
        "packaging",
        "python >=3.7",
        "tomli",
        "tornado >=6.1.0"
      ],
      "fn": "jupyterlab-3.6.1-pyhd8ed1ab_0.conda",
      "license": "BSD-3-Clause",
      "license_family": "BSD",
      "md5": "c7de31a5b57a9fc1aa4d3fb9993819c6",
      "name": "jupyterlab",
      "noarch": "python",
      "package_type": "noarch_python",
      "platform": null,
      "sha256": "8f7d234af44356633f8d418ed3001e814215ff09cedbec9583e3fb10fb7cc5e2",
      "size": 5354015,
      "subdir": "noarch",
      "timestamp": 1675434565845,
      "url": "https://conda.anaconda.org/conda-forge/noarch/jupyterlab-3.6.1-pyhd8ed1ab_0.conda",
      "version": "3.6.1"
    }
  ]
}