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Conda vs miniconda vs anaconda8/13/2023 ![]() _libgcc_mutex conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge Of course, there is also Miniconda, which uses Conda underneath too without all these additional packages, but… Unnecessary packages added during installationįor example, if we ran the command conda install -c conda-forge numpy=1.22.3, we would see the following in the terminal: The following NEW packages will be INSTALLED: Having all these unnecessary packages will only take up extra disk space and memory, which could be better used for something more important. However, when it comes to an actual project in production, we typically do not want to have so many packages that we do not actually need. This is useful for a beginner who has just started learning how to use Python to do data science related work, and might not want to spend too much time trying to figure out how to get the dependencies they need. # packages in environment at /home/user/anaconda3: If we ran the command conda list in the base environment, we might see something like this in the terminal: (base) user:~$ conda list I know, “ditch xx totally” is a pretty strong phrase right there, but I have my reasons for saying this. Ditch Conda totally if you plan to do Python programming for production Now this is where I try to convince you that Poetry is the best choice out of the 3 dependency management tools I described earlier. More information about Poetry can be found in its documentation. The use of pyproject.toml and poetry.lock files make it similar to the way the Node Package Manager (npm) for Node.js works.
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