add pip and conda comparison

source/discussions/index.rst

@@ -11,6 +11,7 @@ specific topic. If you're just trying to get stuff done, see
    versioning
    deploying-python-applications
    pip-vs-easy-install
+   pip-and-conda-comparison
    install-requires-vs-requirements
    distribution-package-vs-import-package
    package-formats

source/discussions/pip-and-conda-comparison.rst

@@ -0,0 +1,62 @@
+.. _`Pip and Conda Comparison`:
+
+========================
+Pip and Conda Comparison
+========================
+
+Pip is a package manager for Python, while Conda is a package manager and an
+environment manager. Conda packages are not only for Python but also for other
+systems. With Pip, you can only install Python packages, whereas with Conda, you
+can install Python packages as well as packages for Rust, C++, and other
+languages.
+
+Pip is heavily used in the Python ecosystem and in any type of Python project.
+Conda, on the other hand, is mainly used in data science, AI, and related
+projects.
+
+Type, Source, and Other Info of Packages
+========================================
+
+As Pip is a package manager specific to Python, it can only install Python
+packages. It can install, uninstall, and list Python packages, among other
+things. It can install packages from PyPI, VCS (GitHub), distributions (sdist,
+wheels), and local projects. Pip is installed by default if you install Python
+through the source or the official installer from python.org. Python packages
+can be in source form (sdist) or binary form (wheel). Generally, installing
+wheels is faster because there is no need for compilation.
+
+Conda, on the other hand, can install not only Python packages but also packages
+and libraries for other languages, such as Rust and C++. Additionally, it can
+install Python and Pip. Conda installs and manages packages from the Anaconda
+repository and Anaconda Cloud. Conda packages are binary, so there is no need
+for compilation, making them faster for packages that would otherwise require
+compilation.
+
+Virtual Environment
+===================
+
+Pip is not a virtual environment manager. Therefore, ``venv`` or ``virtualenv`` is
+used to create isolated environments when working with Pip.
+
+Conda, however, is a virtual environment manager too. It can create different
+isolated environments for different projects without any external tools.
+
+Dependency Resolution
+=====================
+
+While both Pip and Conda manage dependencies, Conda does a better job at
+dependency resolution by using a satisfiability solver.
+
+With Pip, it is possible to have a broken environment because the version of a
+dependency needed by one package does not match the version of the same
+dependency needed by a different package.
+
+Availability of Packages
+========================
+
+The Conda repository has a limited number of Python packages compared to PyPI,
+which is no surprise since PyPI is focused only on Python. As a result, many
+Python packages can only be installed via Pip using the PyPI index.
+
+However, you can use Conda and Pip together, such as using Conda as a virtual
+environment manager and Pip as a Python package manager.

source/guides/tool-recommendations.rst

@@ -42,7 +42,13 @@ making use of the same Python interpreter (especially on Linux).
 
 For scientific software specifically, consider :ref:`Conda` or :ref:`Spack`.
 
-.. todo:: Write a "pip vs. Conda" comparison, here or in a new discussion.
+.. note::
+
+  Pip manages Python packages, while Conda manages packages for multiple
+  languages and environments. Conda excels at dependency management and creating
+  isolated environments, but has limited Python packages. Pip is widely used,
+  but struggles with dependencies and virtual environments. Using both together
+  can leverage their strengths. See (:ref:`Pip and Conda Comparison`).
 
 Do **not** use ``easy_install`` (part of :ref:`setuptools`), which is deprecated
 in favor of pip (see :ref:`pip vs easy_install` for details). Likewise, do