Finding and installing packages — Anaconda documentation (2024)

FAQs

How do I get a list of packages installed in Anaconda? ›

In most Linux distributions, use Ctrl+Alt+T to open a terminal application. Run one of the following commands to verify your installation was successful: conda list - If Anaconda Distribution is installed successfully, this displays a list of packages installed in your active environment and their versions.

This may be because your system lacks the sufficient disk space or memory to complete the process. Verify that you have enough disk space and memory on your system to install and use Anaconda packages. The minimum system requirements for Miniconda and Anaconda installers can be found in the conda user guide.

The main issue is due to the base environment having many packages. This is very often the cause of long solves as the problem space grows incredibly challenging and why we strongly encourage people to create environments instead of installing normal packages into the base env.

To test your installation, in your Terminal window or Anaconda Prompt, run the command conda list . For a successful installation, a list of installed packages appears.

In conda-forge, some packages are available with GPU support. These packages not only take significantly longer to compile and build, but they also result in rather large binaries that users then download.

We can list installed packages using pip by using the list command. Just open the terminal or command prompt and run the following command and you will see the list of all installed packages. This command will create a file named installed_packages. txt and save the list of packages in it.

With pip, the Python package installer, you can list installed packages using the pip list and pip freeze commands. pip list can filter and display packages by conditions like latest, outdated versions, and those not required by others. pip freeze is useful for creating requirements.

Installing Python Packages with Setup.py

To install a package that includes a setup.py file, open a command or terminal window and: cd into the root directory where setup.py is located. Enter: python setup.py install.

Anaconda comes with its own set of pre-installed data science packages and tools, making it convenient for beginners to set up their environment quickly. Python, on the other hand, requires users to manually install the required libraries and tools.

Conda installs packages into the anaconda/pkgs directory.

How can I make conda install faster? ›

One option for speeding up conda is to reduce the index. The index is reduced by conda based upon the user's input specs. It's likely that your repodata contains package data that is not used in the solving stage. Filtering out these unnecessary packages before solving can save time.

While it is mostly unattended, the download and installation of Anaconda can take a fair amount of time (approximately 10 to 15 minutes, depending on the speed of your system), so it is highly recommended to do this before you arrive at the tutorial session (and when you have access to a decent internet connection).

conda is an open source package and environment manager that comes with Anaconda. As a package manager, you can use conda to install, update, and remove packages and their "dependencies" (the packages they depend upon): If Anaconda doesn't include a package that you need, you use conda to download and install it.

Open the Anaconda Prompt: Click on the Windows Start menu, type “Anaconda Prompt” and press Enter. This will open the Anaconda command line interface. This command will display the version of Anaconda installed on your system. This command will display the Python version installed with your Anaconda distribution.

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.

Miniconda is a smaller, “minimal” version of Anaconda. It includes only Conda and Python, without any additional packages pre-installed. Miniconda is lightweight and quick to install, making it a great choice if you're working with limited disk space or only need specific packages.

Conda installs binary packages, which means the packages include compiled code. This can make the installation process faster and more reliable, especially for packages with complex dependencies. Pip, by contrast, often installs packages from source, which means the code is compiled during the installation process.

It's common that installing packages using Conda is slow or fails because Conda is unable to resolve dependencies. To get around this, we suggest the use of Mamba. Mamba is a drop-in replacement for Conda that is generally faster and better at resolving dependencies.

To check all the installed Python modules, we can use the following two commands with the 'pip': Using 'pip freeze' command. Using 'pip list command.

To list npm user-installed packages, you can use the npm list command in the terminal. By default, this command will show a tree-like structure of all installed packages for the current project. If you want to see a list of globally installed packages (user-installed), you can add the -g flag.