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[Tut] A Guide of All pip Commands

A Guide of All pip Commands

When working with Python, a programmer often encounters situations where she needs to install packages not contained in the Standard Library. In such situations, she must install modules from online repositories using packager installers.

The goal of this article is to help beginners develop a working knowledge of pip (acronym for “PIP Installs Packages”) as quickly as possible while defining all the prerequisite jargon along the way. In particular, this article aims to make the content of the pip documentation as accessible as possible for beginners by using easier words and emphasizing practical examples.

What is pip?

PIP (“Pip Installs Packages”) is the official package managing software for Python which installs packages from PyPI (“Python Package Index”). PyPI contains over 300,000 packages as of November 2021 which is much larger than similar package repositories for Python. PIP allows users to install and uninstall packages, manage dependencies, keep archives of wheel files, amongst many other things.

The purpose of this article is to develop a “working knowledge” of PIP that may become useful while working on Python projects at a basic to intermediate level. In particular, we will talk about the most useful parts of the PIP documentation and provide explanations so to make the system more accessible to the beginner. The article will assume that the user is working on MacOS, but the commands for Windows can be obtained through minor modifications.

Note on Pip vs. Conda

A popular alternative to PIP is Conda, which is a package managing software aimed for data analysis. We will highlight three key differences to give you a sense of which you may prefer to use. For a more extensive discussion, see the official Anaconda blog page or StackOverflow.

1) Virtual Environments. Virtual environments are isolated Python environments used for Python projects. Because many Python projects depend on having specific versions of packages installed in the environment, projects may be broken when globally installed packages are updated. To prevent this, virtual environments are created so that projects can be run in the same environment, every time they need to be implemented.

PIP has several virtual environment builders such as virtualenv and venv. (See Chris’s article for a more detailed discussion.) In contrast, Conda has a built-in virtual environment manager. (This can be managed through a GUI if you install Anaconda Navigator.) In this respect, Conda may be easier to use for beginning coders.

2) Availability of Packages. As noted before, PyPI boasts over 300,000 packages in contrast to around 7000 packages in the Anaconda repository. Although PyPI packages can be installed through Conda, they often lead to complications, and mixing the two should generally be avoided. (For more details, see the official Anaconda blog page). Many popular Python packages (numpy, matplotlib, and pandas to name a few) are available through Conda, but when working on Python projects, it is not uncommon for developers to come across packages that are only available through PyPI.

3) Languages. While PIP only deals with Python packages, Conda can install packages written in other languages such as R or C. This is because Conda is aimed toward data science tasks.

Part I: How to install packages using pip?

In this section, we will look at how to install packages and manage dependencies using pip.

pip install

To install packages on pip from PyPI, open up your terminal and use the command:

pip install matplotlib

🛑 Note: pip is replaced with python -m pip in the PIP documentations. -m flag searches the sys.path for the pip module and executes it as an executable file. Some systems require that one uses python -m pip. For this article, we will just use pip.

The install command installs all of the package’s dependencies, which is to say it installs all the necessary packages for the desired package to install properly. For instance, matplotlib requires numpy, packaging, pyparsing, cycler, amongst many others whereas NumPy has none. Dependency resolution is a major topic in using pip.

There are various other sources from which one can install packages.

Requirement Files. Requirement files are .txt files that allow users to install packages in bulk, possibly with specifications such as package versions. (See the “Example” in the PyPI documentation to get a sense of what the contents of the file should look like.) Many of the pip commands have options that make outputs suitable for requirement files.

One can use the pip install command to install from requirement files. To do this, navigate to the appropriate directory on the terminal (using the terminal command cd). Then use the following PIP command:

pip install -r requirements.txt

Instead of navigating to the directory on terminal, one could use the absolute path of the file:

pip3 install -r /Users/username/Desktop/requirements.txt

VCS Projects. Many Python packages are available through VCS repositories (such as GitHub) as well. The following example is if you wanted to install Django from their GitHub repository:

pip install git+

Wheel and Tarball File. The pip install command can also be used to install from local wheel (.whl) and tarball (.tar.gz) files. (Read this Medium article and StackOverflow post on their differences.)

The syntax is similar to before. Navigate to the directory where the files are located using the change directory (cd) command on terminal. For example, to install the tea package from a whl file, use:

pip install tea-0.1.6-py3-none-any.whl

To install the tea package using tarball, use:

pip install tea-0.1.6.tar

pip uninstall

The uninstall command is fairly self-explanatory. It allows users to uninstall packages. For instance, if one were to uninstall the tea package using pip, then use:

pip uninstall -y tea

One can (optionally) add -y as above to prevent the program from asking for confirmation.

To uninstall multiple packages at once, one can list the packages in a requirements.txt file (much like we did for pip install), and use the following command:

pip uninstall -r requirements.txt

pip check

The check command allows users to check for any broken dependencies, i.e. if there are any packages that depend on other packages that are not installed in the environment. The syntax is as follows:

pip check

pip show

The show command lists all the relevant information for a particular package. For instance, if you want to know where Django is installed on your device or if you want to know its package dependencies, one can use:

pip show django

For which one can get the output:

Name: Django
Version: 3.0
Summary: A high-level Python Web framework that encourages rapid development and clean, pragmatic design.
Author: Django Software Foundation
License: BSD
Location: /Users/user_name/Library/Python/3.8/lib/python/site-packages
Requires: pytz, sqlparse

pip list

To list all the packages available in one’s environment, use the pip list command:

pip list

For which one may get the output:

Package Version
-------------- -------
pip 19.2.3 setuptools 41.2.0 setuptools-scm 6.3.2 six 1.15.0 sqlparse 0.4.2 tea 0.1.6 tomli 1.2.2 tzlocal 3.0 wheel 0.33.1

What if the user wanted to uninstall all the packages except the bare essentials? One can obtain a list of packages that are not dependencies of installed packages using:

pip3 list --format freeze --not-required

The option “--format freeze” puts the list in a format compatible with a requirements.txt file:


Now the user can copy the above into a requirements.txt file, delete the names of files that the user wants to keep, and use

pip uninstall -r requirements.txt

to uninstall all the rest.

pip freeze

The freeze command outputs a list of packages installed in the environment in a package suitable for requirement files. The syntax is as follows:

pip freeze 

The freeze command is useful for copying all the packages from environment A to environment B. First run freeze in environment A, and store the contents in a requirements.txt file:

pip freeze > requirements.txt

The file gets stored in the current directory (which one can check using pwd command on terminal). Then go to environment B. (If A and B are virtual environments, deactivate A and activate B on terminal using commands from whichever virtual environment manager is being used.) Then install the packages in the requirements file using install:

pip install -r requirements.txt

Part II: Distribution Files

In this section, we will discuss how to download and manage distribution files for Python packages.

Distribution files are compressed files containing various files necessary to implement the Python library. See this medium article on an extensive discussion on the different types of distribution files. We just need to know the following in order to understand the rest of this section:

Wheels. (.whl) Wheel files are essentially zip files containing everything necessary to install packages in your local environment. They are generally faster to download and install compared to tarballs. For more details, see this article from and this article from

A “built” distribution file is in a format that is ready to install, thereby making the whole installation process faster.

Tarballs. (.tar.gz) Tarballs are types of source distributions that contain both python codes and codes for any extension modules for the package.

Wheel files are the preferred format for installations using pip. See this stackoverflow post on a discussion on wheels versus tarballs.

pip download

Like the pip install command, the pip download command downloads the necessary distribution files from repositories (e.g. for offline installation), but does not install the packages from the downloaded files. As such, the command supports many of the options that the install command does.

For instance, if one were to download the distribution file, we would use the following syntax:

pip download numpy

pip wheel

As we described before, wheel files are easier to use than tarball files. However, there are situations where only tarball files are available.

The wheel command allows users to build wheel files locally.

To build a wheel file for NumPy, use:

pip wheel -w numpy

Much like the pip install and pip download commands, pip wheel also supports requirement files:

pip wheel -r requirements.txt

pip cache

pip has a built-in cache system for keeping distribution files downloaded from repositories. Whenever pip is used to install a package, the wheel files in the cache are preferred over downloading new distribution files from the repository. This helps the whole installation process faster as well as reduces traffic to repositories.

The pip cache command allows users to interact with pip’s wheel cache. There are several things you can do with it:

Show file path to the directory of all cache files:

pip cache dir

Show various information regarding the cache, such as the number of files and size of the cache:

pip cache info

List the file names in a pip cache:

pip cache list

To see a list of file paths for wheel files of specific packages, use:

pip cache list numpy --format==abspath

To remove specific packages from the cache, use:

pip cache remove numpy

Finally, to clear the whole cache:

pip cache purge

pip hash

A hash value is a value assigned to a file that changes if the file is altered. Since anyone can upload packages to pypa, there may be tampered packages in the repository, at least in principle. Hash values allow users to check whether files have been tampered with or not.

To generate a hash value for a wheel file, use:

python -m pip hash tea-0.1.7-py3-none-any.whl

There are different algorithms for computing hash values. On pip, you can choose from sha256, sha384, and sha512:

python -m pip hash -a 'sha256' tea-0.1.7-py3-none-any.whl

Running this, the output is:


We can compare this to the hash code available on PyPI to confirm that it is indeed the correct file.


Here are some other commands listed in the pip documentation.

pip config

The pip config command allows users to interact with the configuration file (pip.conf) from terminal. The configuration files are located in standardized locations depending on the platform (see “Location” in the documentation), and most of what can be done by the config command can be done by opening the configuration file in a text editor and editing its contents.

Configuration File. The configuration files determine the default behavior of pip in the command line (ranging from the address for the proxy server used to install packages to the timeout when running the freeze command). There are three levels to configuration files. The global files determine pip‘s behavior throughout the system, the user files determine the behavior by user, and finally, the site file determines the behavior depending on the virtual environment.

For a list of all the possible options available to the config command, use:

pip help config

pip debug

The debug command outputs information about the system that may be useful for debugging, such as the versions for pip and python, where the executable is located etc:

pip debug

The pip search command allowed users to search for PyPI packages using a query. However, the command has been permanently disabled as of March 2021.


Finally, note that much of the content in the documentation and this blog article is available through the pip help command. For instance, if the user forgets the syntax for config, then use:

pip help config

This command provides the syntax for the config command as well as all the possible options associated with the command.

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