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Currently pip is the package manager in place within the Designer. Unfortunately this is something that doesn't fit our requirements as Data Scientists. We prefer using conda due to the following reasons:
condamanages also non-Python library dependencies. This waycondacan beused to manage R packages as well which comes in quite handy (even tough not all packages fromCRANRepository are available)
condaprovides a very simple way of creating conda envs (similar to virtualenv but with conda one can also install and manage pip packages --> virtualenv cannot install conda packages!)toisolate required packages (with specific versions) used in a workflow (e.g. for a Python Tool in Designer).
So I would like to havecondainstead or additionally to pip and would like to createmy condaenvswhere I install the packages I need for a specific task within my workflow. Moreover, if you think about to feature an R jupyternotebook capability (like the Python Tool) it could be beneficial to change from pip tocondafor managing packages in both worlds.