Over time i have learned that most users:
-pip combined with a virtual environment is a practical and widely supported approach
-conda is ideal if you’re in the data science ecosystem or need to manage dependencies across multiple languages.
-pipenv offers a more integrated experience but might be overkill for simpler projects.
Another approach is using conda. There is a free comunity version called miniforge which can be used. Conda provides a huge repository of all packages, handles dependencies and provides also additional packages like intel-mkl libraries for various operating systems.
Available on github: https://github.com/conda-forge/miniforge
Haider Hadi Abbas
1. Using pip: pip installs packages from the Python Package Index (PyPI) and is included by default with Python 3.4 and above.
2. Using conda: conda is a package manager that can install Python libraries
3. Using Virtual Environments
Always use virtual environments to avoid dependency conflicts.
Regularly update your libraries to keep your environment secure and compatible with the latest versions.
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