![]() This because Jupyter default working directory is C:\Users\MYUSER - where MYUSER is your Windows user (in our case is “telec”).īut you can change this to another location. When you installed Jupyter, you saw that the default folders are presented as your computer folders – Documents, Downloads, etc. Changing Jupyter working directoryīut first, let’s see one tip that may be useful. But it’s not that simple, so let’s look at two ways to do that. Then the question arises: if I am using an IDE (like P圜harm), can I open an “ipynb” file of Jupyter normally? Most Python code editors save the file in the same format, with the “.py” extension.īut when we installed Jupyter, we saw that it has another extension, the “ipynb” – as we saw in the Jupyter installation tutorial (formerly known as IPython Notebook). (But you can use others, no issues).īelow we’ll see some tips for you who are starting to use Jupyter and/or P圜harm be able to easily follow the upcoming tutorials. Developers found it much easier to create, run, and debug source code while examining its outputs simultaneously in a single place.Īnd that’s where IDE come in, and as we’ve seen, P圜harm is the one we’re going to use – because it’s one of the most powerful. ![]() The editing options of markup cells are endless, allowing you to use imagination to work with text formats, images and even mathematical equations and diagrams.Īnd that’s one of the reasons that has made Jupyter one of the mandatory programs for those who want to work with Data Science.īut it doesn’t have as many features as professional programmers do. ![]() We have code cells - of course - but we also have markup cells where it is easy to type descriptions of code, meaning or results. If you’re starting out, give a lot of importance to this topic – and we’ll help you by always reinforcing it.Īnd it’s exactly this idea that we have with Jupyter: it presents us with the code in a simple way to understand and at the same time complete, with a multitude of tools available.Īs we have seen, notebooks (files in which the code is written in Jupyter) have the ability to separate code into “cells”, which facilitate differentiation between specific functionalities. But for some reason – perhaps the rush – they don’t, end up, after some time afterwards, don’t knowing what the code does anymore, how it works. Tip: Not following this idea is one of the big problems, even of professional programmers – who know that they should write a well-commented text, for example. This standard form of programming (that we in the telecomHall community are also adept) was defined by Donald Knuth, a famous Computer Scientist at Stanford. In other words, give form to the logical units of the code, and also their meanings and of course, the results. We’re really looking forward to start learning Python and Data Science here in telecomHall Community – and yes, we’ll do it soon, together.īut before we go any further, we just need to make a few observations about the programs we just installed – and if you haven’t already installed, don’t leave it for later – if you postpone it, you won’t be able to do it all at once – and you certainly won’t be able to learn together.Īnd although the title of this article is “Jupyter versus Pycharm”, let’s not here make a comparison between both, but rather see some concepts related to them and also adjustments in both, which we will do before we start.Ĭodes must be written in such a way as to ensure that they are read and interpreted with ease by us human beings.
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