My main problem with conda is its performance issues. After several years of using conda, here are few of my observations on conda as a package and dependency management: Performance issues Name : post channels : - default - conda-forge dependencies : - python=3.8 - pandas=1.1.0 - pip=20.3.3 - pip : - requests=2.25.0īy now, you may say, great, conda does everything, so, let’s use conda packages in conda environments and let conda resolve any dependency issues.

You can install a fresh conda environment by running the following command Besides, conda can install PyPI packages by using pip in an active conda environment. Not only that, but it is language-agnostic too. Unlike conda, both virtualenv and Pipenv are Python environments only.Īs you may note from the introduction, conda manages the environment and the packages, and the dependencies.I want to have the flexibility to install conda packages.However, the main reason I will not consider virtualenv nor the Pipenv as the environment managers are: Pipenv was created to address many shortcomings of virtualenv. You can install conda packages by running conda install package_name in your conda environment. Python libraries can also be packaged using conda, and a popular host for conda packages is Anaconda. You can install packages from PyPI by running pip install package_name. The most popular Python package repository is the Python Package Index (PyPI), a public repository for many Python libraries. Let’s first list different groups of technologies and highlight few tools In this post, library and package are used interchangeably, and they both refer to the Python package. Then, we will go over an ideal setup (of course, in my opinion 🙂) suitable for most Python projects using conda and Poetry. This post discusses different available technologies for Python packaging, environment, and dependencies management systems. There are various tools for creating an isolated environment and install the libraries you need for your project.

If you work on multiple Python projects at different development stages, you probably have different environments on your system. 👉 This article is also published on Towards Data Science blog.

Creating conda environment