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Creating a Pandas DataFrame
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How To Use Jupyter Notebook - An Ultimate Guide

Last Updated : 17 Mar, 2025
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The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter has support for over 40 different programming languages and Python is one of them.

Installation of Jupyter Notebook

Using Anaconda

To install Jupyter Notebook using Anaconda: Download and install the latest Python 3 version of Anaconda. It includes Jupyter Notebook, Python, and other essential packages by default, making it an easy and recommended option for beginners.

Using pip

Alternatively you can install Jupyter Notebook using pip

python3 -m pip install --upgrade pip

python3 -m pip install jupyter

Starting Jupyter Notebook

To launch Jupyter Notebook enter the following command in the terminal:

jupyter notebook

This will print some information about the notebook server in your terminal, including the URL of the web application (by default, http://localhost:8888) and then open your default web browser to this URL. jupyter-notebook-python After opened, you'll see the Notebook Dashboard which lists all available notebooks, files and subdirectories. You should start the notebook server in a directory containing your notebooks typically your home directory

jupyter-notebook-dashboard

Creating a Notebook

To create a new notebook click on the new button at the top right corner. Click it to open a drop-down list and then if you'll click on Python3 it will open a new notebook. jupyter-notebook-new-file The web page should look like this: jupyter-notebook-notebook

Writing and Running Code in Jupyter Notebook

After successfully installing and creating a notebook in Jupyter Notebook let's see how to write code in it. Jupyter notebooks consist of cells where you can write and execute code. For example, if you created a Python3 notebook then you can write Python3 code in the cell. Now, let's add the following code - 

Python 
print("Hello, World!")

To run a cell either click the run button or press shift ⇧ + enter ⏎ after selecting the cell you want to execute. After writing the above code in the jupyter notebook the output was: jupyter-notebook-hello-world

Cells in Jupyter Notebook

Cells can be considered as the body of the Jupyter. In the above screenshot the box with the green outline is a cell. There are 3 types of cell:

  • Code
  • Markup
  • Raw NBConverter

Code

This is where the code is typed and when executed the code will display the output below the cell. The type of code depends on the type of the notebook you have created. Consider the below example where a simple code of the Fibonacci series is created and this code also takes input from the user. jypter-code-cell The text bar in the above code is prompted for taking input from the user. The output of the above code is as follows:

Output: jupyter-code-cell

Markdown

Markdown is a popular markup language that is the superset of the HTML. Jupyter Notebook also supports markdown. The cell type can be changed to markdown using the cell menu. jupyter-notebook-cell-menu Adding Headers: Heading can be added by prefixing any line by single or multiple '#' followed by space. jupyter-notebook-headers-1 Output: jupyter-notebook-headers-2- Adding List: Adding List is really simple in Jupyter Notebook. The list can be added by using '*' sign. And the Nested list can be created by using indentation. Example: jupyter-notebook-lists-1 Output: jupyter-notebook-lists-2 Adding Latex Equations: Latex expressions can be added by surrounding the latex code by '$' and for writing the expressions in the middle, surrounds the latex code by '$$'. Example: jupyter-notebook-latex-1 Output: jupyter-notebook-latex-2 Adding Table: A table can be added by writing the content in the following format. jupyter-notebook-table-1 Output: jupyter-notebook-table-2

Raw NBConverter

Raw cells are provided to write the output directly. This cell is not evaluated by Jupyter notebook. After passing through nbconvert the raw cells arrives in the destination folder without any modification. For example one can write full Python into a raw cell that can only be rendered by Python only after conversion by nbconvert.

Understanding Jupyter Notebook Kernels

A kernel runs behind every Jupyter notebook, executing code and storing variables. The kernel remains active throughout the notebook session. For example, if a module is imported in one cell then that module will be available for the whole document. See the below example for better understanding. jupyter-notebook-for-kernelOptions for kernels: Jupyter Notebook provides various options for kernels. This can be useful if you want to reset things. The options are:

  • Restart: This will restart the kernels i.e. clearing all the variables that were defined, clearing the modules that were imported, etc.
  • Restart and Clear Output: This will do the same as above but will also clear all the output that was displayed below the cell.
  • Restart and Run All: This is also the same as above but will also run all the cells in the top-down order.
  • Interrupt: This option will interrupt the kernel execution. It can be useful in the case where the programs continue for execution or the kernel is stuck over some computation.

Naming and Saving Notebooks

By default a new notebook is named Untitled. To rename it: Click the notebook title and then enter a new name and confirm. Jupyter automatically saves notebooks periodically but you can manually save them by clicking File > Save and Checkpoint (Ctrl + S).

jupyter-notebook-rename

Extending Jupyter Notebook with Extensions

New functionality can be added to Jupyter through extensions. Extensions are javascript module. You can even write your own extension that can access the page's DOM and the Jupyter Javascript API. Jupyter supports four types of extensions.

  • Kernel
  • IPyhton Kernel
  • Notebook
  • Notebook server

Installing Extensions

Most of the extensions can be installed using Python's pip tool. If an extension can not be installed using pip then install the extension using the below command.

jupyter nbextension install extension_name

The above only installs the extension but does not enables it. To enable it type the below command in the terminal.

jupyter nbextension enable extension_name


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Creating a Pandas DataFrame

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