The SciView panel contains the Plots tab, which displays all the visualizations that have been generated by a Python program, and the Data tab, which can be used to inspect the values of data-related variables.
All of the visualizations that are generated by a Python program are included in the Plot viewer of the SciView panel, where a user can navigate through them with a straightforward graphical interface. Since all of the visualizations are temporarily saved to the panel, the whole program can run in one go. This also allows us to avoid clicking through the Matplotlib plots in order to proceed with the execution, which is the case when executing a Python program from, say, the Terminal.
The data viewer of the SciView panel supports pandas DataFrames and NumPy arrays.
Iterative development is done when a given program is split into different sections so that the logic and execution of that program can be considered in an incremental way. This idea is specifically appropriate for data science and scientific computing tasks, where you need to consider the characteristics of a dataset before applying different processing methods and algorithms.
Jupyter notebooks consist of multiple code cells, each containing only a block of code that achieves a specific goal. The output of a code cell is displayed immediately after that code cell when it is executed, making the process of debugging easier than in traditional programs.
Markdown is a markup language that's typically used for writing README files in GitHub repositories. LaTeX is a word processing system that is popular among members of the academic community, who use LaTeX to write scientific research papers.
The ability to combine LaTeX and general Markdown text with live code makes Jupyter notebooks a flexible tool in data science projects. Being able to display the code between text explanations of a data analysis process can help readers follow what is being done to that data much more easily.
The beginning of a code cell is marked with the #%%symbol, while that of a Markdown cell is marked with#%% md.
Writing Jupyter notebooks in the PyCharm editor addresses the lack of code-writing support in traditional, web-based Jupyter notebooks. In other words, when using PyCharm to write Jupyter notebooks, we get the best of both worlds: powerful, intelligent support from PyCharm and iterative development style from Jupyter.
