Jupyter is an amazing presentation tool for analytical work where you can present code in “blocks,” combines with rich text descriptions between blocks, and the inclusion of formatted output from the blocks, and graphs generated in a well-designed matter by way of another block’s code. It’s a bit similar to Docker, but restricted to the Python ecosystem. Jupyter tries to solve the issue of reproducibility in the analysis by enabling an iterative and hands-on approach to explaining and visualizing code by using rich text documentation combined with visual representations, in a single solution.Īnaconda is similar to pyenv, venv and minconda it’s meant to achieve a python environment that’s 100% reproducible on another environment, independent of whatever other versions of a project’s dependencies are available. Jupyter is a presentation layer.Īnaconda tries to solve the dependency hell in python-where different projects have different dependency versions-so as to not make different project dependencies require different versions, which may interfere with each other. We will quote the difference in one line:Īnaconda is package manager. Whenever I try to discuss Anaconda with people who are beginners with Python and Data Science, they get confused between Anaconda and Jupyter Notebooks.
#Install jupyter notebook to anaconda enviornment install
It will automatically install Python on your machine so you don’t have to install it separately.
To show in brief what Anaconda is, here are some quick points:
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