{"id":184693,"date":"2023-02-07T15:19:00","date_gmt":"2023-02-07T20:19:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/traders-insight\/jupyter-notebook-tutorial-installation-components-and-magic-commands-part-ii\/"},"modified":"2023-02-13T15:16:38","modified_gmt":"2023-02-13T20:16:38","slug":"jupyter-notebook-tutorial-installation-components-and-magic-commands-part-ii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/jupyter-notebook-tutorial-installation-components-and-magic-commands-part-ii\/","title":{"rendered":"Jupyter Notebook Tutorial: Installation, Components and Magic Commands \u2013 Part II"},"content":{"rendered":"\n

See Part I<\/a> for an overview of Jupyter Notebook.<\/em><\/p>\n\n\n\n

What are the different components of Jupyter notebooks?<\/h3>\n\n\n\n

I assume that you are following the chronological order of this article. If so, then you have learned what Jupyter notebooks are, how to install it, and how to run and open it. If not, then I would recommend you to go through them to get an overall picture. Nevertheless, if you are already familiar with those parts, go on and keep learning.<\/p>\n\n\n\n

In this section, I will explain the various components of Jupyter software. When you start Jupyter Notebook application, and you’ll be presented with the homepage. Let’s start exploring it. Below shown is the snapshot of the homepage that you’d seen earlier, but with numbers assigned to each component to make our learning easier.<\/p>\n\n\n\n

\"Jupyter<\/figure>\n\n\n\n

Here’s the description of each numbered point shown in the snapshot.<\/p>\n\n\n\n

    \n
  1. It is the URL<\/em> on which Jupyter server is running. If you are running the Jupyter on localhost, this would be the same URL shown in the console when you started Jupyter software.<\/li>\n\n\n\n
  2. The Files<\/em> tab lists the directories and files in the home folder, which usually is the home directory of the user logged in to the computer.<\/li>\n\n\n\n
  3. The Running<\/em> tab shows you a list of all open notebooks. When you start a new notebook or open an existing notebook, a kernel will get attached to it. All such running kernels will be listed under this tab.<\/li>\n\n\n\n
  4. If you want to open an existing Jupyter notebook(that ends with .ipynb extension), it needs to be listed in the Files tab. If it is not listed, you need to upload it using the Upload<\/em> button which will open a file browser for you to load the file.<\/li>\n\n\n\n
  5. Quit<\/em> and Logout<\/em> buttons allow you to logout and shutdown the server. When you quit, all the opened notebooks will be closed, and the server will get shutdown.<\/li>\n\n\n\n
  6. The New<\/em> button allows you to create a new notebook, text file, folder, or terminal. The snapshot shown below shows that you can create a notebook in Python, Julia, and R language. The notebook that you create will be associated with a respective kernel. When you install Jupyter environment as shown in the previous section, it is very likely that you will have only one option for the kernel, that is, Python. In order to add new languages, you can refer to this link<\/a>.<\/li>\n<\/ol>\n\n\n\n
    \"=\"\"<\/figure>\n\n\n\n

    You can create a new notebook by clicking on the respective language name. Regardless of what language you choose, the new notebook that you create will have the same appearance. The difference would be in terms of the kernel attached to it. If I click on Python 3<\/em> on the dropdown opened, a new notebook with Python kernel attached to it will be created. The empty Jupyter notebook is shown in the snapshot below. (Obviously without numbers :p)<\/em><\/p>\n\n\n\n

    \"<\/figure>\n\n\n\n

    The newly created notebook has various components which are explained below:<\/p>\n\n\n\n

      \n
    1. The title<\/em> is the name of the notebook. The title you set becomes the file name for the notebook, and it will have the extension as .ipynb<\/em> which stands for IPython NoteBook<\/em>.<\/li>\n\n\n\n
    2. The checkpoint<\/em> shows you the time when your notebook was saved last.<\/li>\n\n\n\n
    3. The menu bar<\/em> lists various menus that allow you to download the notebook(in multiple formats), open a new notebook, edit the notebook, customise the headers, manipulate cells, nudge the kernel, access help and so on.<\/li>\n\n\n\n
    4. The shortcut bar<\/em> lists commonly used shortcuts such as save<\/em> to save the notebook, add a cell<\/em>, cut<\/em>, copy<\/em> to manipulate cells, up<\/em> and down<\/em> to navigate between cells, run<\/em> to execute the cell, and so on. Any extension that you add to Jupyter will have its shortcut on this bar. We will learn what extensions are in the latter part of this article.<\/li>\n\n\n\n
    5. The Kernel<\/em> shows you the current kernel associated with the notebook. In our case, the kernel is Python 3<\/em>. The circle beside the kernel shows the status of the kernel. The hollow circle represents that it is ready to take input and run a cell. When a kernel is executing code or processing anything, it changes to solid.<\/li>\n\n\n\n
    6. A cell<\/em> is the part of a notebook where all the magic happens. Cells are explained in detail in the following section.<\/li>\n<\/ol>\n\n\n\n

      What are cells in a Jupyter Notebook?<\/h3>\n\n\n\n

      Any text or code that you write goes in the cell. Cells are the building block of any Jupyter notebook. Cells operate in two modes: command<\/em> and edit<\/em> mode, and they are of mainly three types: code<\/em>, markdown<\/em>, and Raw NBConvert<\/em>.<\/p>\n\n\n\n

      The command<\/em> mode allows you to manipulate cells. That is, the action you perform has to do with the cell as a whole. The command mode is represented by a grey border around the cell with a blue indication, as shown in the below snapshot.<\/p>\n\n\n\n

      \"<\/figure>\n\n\n\n

      Some of the operations (along with their shortcuts) that you can perform when a cell is in the command mode are as follows:<\/p>\n\n\n\n