jupyter notebook in pycharm community version

Jupyter notebook in pycharm community version code#

Jupyter notebook in pycharm community version code#

I used to code in Pascal, C, and little of Fortran that were the main scientific languages used in the Universities when I graduated (I know, it was a long time ago). When I started to develop my research at the university, I was at least 10 years apart from any coding and I barely know about the existence of Python. To answer this question I will tell a little story. If it is so good (and a gamechanger), why do I have to take care of its use? And this can be confirmed by the increasing number of available courses and jobs positions that require these skills.īut now, you may be asking yourself. You can just declare your variable on demand, and focus on the results.īottom line: Python and Jupyter became a standard for data scientists. Depending on the objective, you don’t even need to bother with declaring functions or designing classes. By using interactive notebooks, it is no more necessary to write a long script, filled with dozens (or hundreds) lines of code, prone to errors, to display the results just at the end of the processing. The possibility to experiment with data and see the code’s results for each typed command makes it ideal for data scientists and researchers, where the focus is on data analysis and not on developing. The second reason is the interactive nature of Jupyter Notebooks. It combines code with natural language texts. Literate programming was first introduced by Knuth at Stanford University, with the objective to bring the program logic closer to the human language. This notebook feature made it possible, and simple, to implement the literate programming paradigm, first proposed by Donald Knuth in 1984. The first reason is the simplicity to connect to different programming languages (kernels) and combine text with code snippets and outputs such as tables and graphs, and maps into a single page. It’s no doubt that the launch of the project Jupyter, and its notebooks, back in 2015, changed the relation between scientific programmers and their code. Photo by Elisa Ventur on Unsplash Introduction