![]() ![]() If you are a beginner, before moving towards to more advanced options, such as #3, #4 and #5 listed above, I would recommend you to try both options #1 and #2.ĭo not underestimate option #1. In the long run, working with Python virtual environments is the right thing to do. No matter what options you will try or have tried previously, you would eventually find yourself ending up with managing projects with virtual environments and associating each project with one Python virtual environment. These options may easily confuse someone who just begins. Within Anaconda, the dependency management tool Conda creates virtual environments as well as activating, deactivating and deleting the environments.Ĭomplete option #2 and install a virtual environment management tool, such as virtualenv which creates isolated Python environments and pyenv package for isolating Python versions.Īccess Python on cloud computing platforms: AWS, Microsoft Azure and GCP. This would be the best option if you do Python for data science, machine learning and AI. Install an environment manager such as Anaconda, the most popular Python data science platform, which comes with a bundled Python. The following options support virtual environments. The virtual environments are separated and updating an individual environment will not interference with others. With per-project virtual envronments, the projects are isolated from each other with respect to their dependencies, including the Python version as well as the packages. The first two options above are straightforward and simle but when you want to switch between multiple versions and build a project upon a specific version and package denpendencies, you should create multiple virtual environments, and associate each project with a single environment.
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