Managing packages#


There are many options available for the commands described on this page. For details, see commands.

Searching for packages#

Use the terminal for the following steps.

To see if a specific package, such as SciPy, is available for installation:

conda search scipy

To see if a specific package, such as SciPy, is available for installation from

conda search --override-channels --channel defaults scipy

To see if a specific package, such as iminuit, exists in a specific channel, such as, and is available for installation:

conda search --override-channels --channel iminuit

Installing packages#

Use the terminal for the following steps.

To install a specific package such as SciPy into an existing environment "myenv":

conda install --name myenv scipy

If you do not specify the environment name, which in this example is done by --name myenv, the package installs into the current environment:

conda install scipy

To install a specific version of a package such as SciPy:

conda install scipy=0.15.0

To install multiple packages at once, such as SciPy and cURL:

conda install scipy curl


It is best to install all packages at once, so that all of the dependencies are installed at the same time.

To install multiple packages at once and specify the version of the package:

conda install scipy=0.15.0 curl=7.26.0

To install a package for a specific Python version:

conda install scipy=0.15.0 curl=7.26.0 -n py34_env

If you want to use a specific Python version, it is best to use an environment with that version. For more information, see Troubleshooting.

Installing similar packages#

Installing packages that have similar filenames and serve similar purposes may return unexpected results. The package last installed will likely determine the outcome, which may be undesirable. If the two packages have different names, or if you're building variants of packages and need to line up other software in the stack, we recommend using Mutex metapackages.

Installing packages from

Packages that are not available using conda install can be obtained from, a package management service for both public and private package repositories. is an Anaconda product, just like Anaconda and Miniconda.

To install a package from

  1. In a browser, go to

  2. To find the package named bottleneck, type bottleneck in the top-left box named Search Packages.

  3. Find the package that you want and click it to go to the detail page.

    The detail page displays the name of the channel. In this example it is the "pandas" channel.

  4. Now that you know the channel name, use the conda install command to install the package. In your terminal window, run:

    conda install -c pandas bottleneck

    This command tells conda to install the bottleneck package from the pandas channel on

  5. To check that the package is installed, in your terminal window, run:

    conda list

    A list of packages appears, including bottleneck.


For information on installing packages from multiple channels, see Managing channels.

Installing non-conda packages#

If a package is not available from conda or, you may be able to find and install the package via conda-forge or with another package manager like pip.

Pip packages do not have all the features of conda packages and we recommend first trying to install any package with conda. If the package is unavailable through conda, try finding and installing it with conda-forge.

If you still cannot install the package, you can try installing it with pip. The differences between pip and conda packages cause certain unavoidable limits in compatibility but conda works hard to be as compatible with pip as possible.


Both pip and conda are included in Anaconda and Miniconda, so you do not need to install them separately.

Conda environments replace virtualenv, so there is no need to activate a virtualenv before using pip.

It is possible to have pip installed outside a conda environment or inside a conda environment.

To gain the benefits of conda integration, be sure to install pip inside the currently active conda environment and then install packages with that instance of pip. The command conda list shows packages installed this way, with a label showing that they were installed with pip.

You can install pip in the current conda environment with the command conda install pip, as discussed in Using pip in an environment.

If there are instances of pip installed both inside and outside the current conda environment, the instance of pip installed inside the current conda environment is used.

To install a non-conda package:

  1. Activate the environment where you want to put the program:

    • In your terminal window, run conda activate myenv.

  2. To use pip to install a program such as See, in your terminal window, run:

    pip install see
  3. To verify the package was installed, in your terminal window, run:

    conda list

    If the package is not shown, install pip as described in Using pip in an environment and try these commands again.

Installing commercial packages#

Installing a commercial package such as IOPro is the same as installing any other package. In your terminal window, run:

conda install --name myenv iopro

This command installs a free trial of one of Anaconda's commercial packages called IOPro, which can speed up your Python processing. Except for academic use, this free trial expires after 30 days.

Viewing a list of installed packages#

Use the terminal for the following steps.

To list all of the packages in the active environment:

conda list

To list all of the packages in a deactivated environment:

conda list -n myenv

Listing package dependencies#

To find what packages are depending on a specific package in your environment, there is not one specific conda command. It requires a series of steps:

  1. List the dependencies that a specific package requires to run: conda search package_name --info

  2. Find your installation’s package cache directory: conda info

  3. Find package dependencies. By default, Anaconda/Miniconda stores packages in ~/anaconda/pkgs/ (or ~/opt/pkgs/ on macOS Catalina). Each package has an index.json file which lists the package’s dependencies. This file resides in ~anaconda/pkgs/package_name/info/index.json.

  4. Now you can find what packages depend on a specific package. Use grep to search all index.json files as follows: grep package_name ~/anaconda/pkgs/*/info/index.json

The result will be the full package path and version of anything containing the <package_name>.

Example: grep numpy ~/anaconda3/pkgs/*/info/index.json

Output from the above command:

/Users/testuser/anaconda3/pkgs/anaconda-4.3.0-np111py36_0/info/index.json: numpy 1.11.3 py36_0
/Users/testuser/anaconda3/pkgs/anaconda-4.3.0-np111py36_0/info/index.json: numpydoc 0.6.0 py36_0
/Users/testuser/anaconda3/pkgs/anaconda-4.3.0-np111py36_0/info/index.json: numpy 1.11.3 py36_0

Note this also returned “numpydoc” as it contains the string “numpy”. To get a more specific result set you can add < and >.

Updating packages#

Use conda update command to check to see if a new update is available. If conda tells you an update is available, you can then choose whether or not to install it.

Use the terminal for the following steps.

  • To update a specific package:

    conda update biopython
  • To update Python:

    conda update python
  • To update conda itself:

    conda update conda


Conda updates to the highest version in its series, so Python 3.8 updates to the highest available in the 3.x series.

To update the Anaconda metapackage:

conda update conda
conda update anaconda

Regardless of what package you are updating, conda compares versions and then reports what is available to install. If no updates are available, conda reports "All requested packages are already installed."

If a newer version of your package is available and you wish to update it, type y to update:

Proceed ([y]/n)? y

Preventing packages from updating (pinning)#

Pinning a package specification in an environment prevents packages listed in the pinned file from being updated.

In the environment's conda-meta directory, add a file named pinned that includes a list of the packages that you do not want updated.

EXAMPLE: The file below forces NumPy to stay on the 1.7 series, which is any version that starts with 1.7. This also forces SciPy to stay at exactly version 0.14.2:

numpy 1.7.*
scipy ==0.14.2

With this pinned file, conda update numpy keeps NumPy at 1.7.1, and conda install scipy=0.15.0 causes an error.

Use the --no-pin flag to override the update restriction on a package. In the terminal, run:

conda update numpy --no-pin

Because the pinned specs are included with each conda install, subsequent conda update commands without --no-pin will revert NumPy back to the 1.7 series.

Adding default packages to new environments automatically#

To automatically add default packages to each new environment that you create:

  1. Open a terminal window and run: conda config --add create_default_packages PACKAGENAME1 PACKAGENAME2

  2. Now, you can create new environments and the default packages will be installed in all of them.

You can also edit the .condarc file with a list of packages to create by default.

You can override this option at the command prompt with the --no-default-packages flag.

Removing packages#

Use the terminal for the following steps.

  • To remove a package such as SciPy in an environment such as myenv:

    conda remove -n myenv scipy
  • To remove a package such as SciPy in the current environment:

    conda remove scipy
  • To remove multiple packages at once, such as SciPy and cURL:

    conda remove scipy curl
  • To confirm that a package has been removed:

    conda list