Jupyter notebooks are arguably the most-used tool in Python scientific computing and data science projects. In this section, we will briefly discuss the basics of Jupyter notebooks as well as the reasons why it is a great tool for data analysis purposes. Then, we will consider the way PyCharm supports the usage of these notebooks.
We will be working with the code examples from the Chapter12/JupyterNotebooks folder of this book's code repository. In its requirements.txt file, we have Pandas, NumPy, Matplotlib, and Jupyter as the external libraries that need to be installed. Whether you are creating a new project or importing the folder into your PyCharm, go ahead and install those libraries in your environment.
Even though we will be writing code in Jupyter notebooks, it is beneficial to first consider a bare-bones program in a traditional Python...
