![]() ![]() To summarize, both of these tools are incredibly useful for data scientists. ![]() More developers are probably used to working with this tool, so in terms of collation, this tool is more accessible between departments or roles of engineeringĪs you can see for these benefits, a lot of them coincide with the more software engineering aspect of data science, or just straight-up software engineering.With that being said, lets highlight the benefits of P圜harm: Code is for the long-haul ( not like Jupyter, which is trial and error focused)Īs you can see, the main differences are in that P圜harm is used for the code that is usually the final product, whereas Jupyter is more for research-based coding and visualizing.Use in production (not usually research).To make these points more clear, here is when you can and should use P圜harm: So, pretending that we are using the community version ( the free one) of P圜harm, we will highlight that product instead of the integrated one with Jupyter Notebook. With that being said, if you are using that version then a lot of the benefits and when to use, would also apply to P圜harm - however, I still think it is easier to have them separated - as the UI gets a little wonky when it is shown in P圜harm. It even has Jupyter Notebook support - however, it is just available in the paid, professional version. ![]() P圜harm is a similar tool that organizes code and helps to run the data science process.
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