Tutorial Playlist

Python Tutorial for Beginners


The Best Tips for Learning Python

Lesson - 1

How to Install Python on Windows?

Lesson - 2

Top 10 Python IDEs in 2022: Choosing The Best One

Lesson - 3

A Beginner’s Guide To Python Variables

Lesson - 4

Understanding Python If-Else Statement

Lesson - 5

Python Numbers: Integers, Floats, Complex Numbers

Lesson - 6

Python Strings | Simplilearn Python Tutorial

Lesson - 7

The Basics of Python Loops

Lesson - 8

Python For Loops Explained With Examples

Lesson - 9

Introduction to Python While Loop

Lesson - 10

Everything You Need to Know About Python Arrays

Lesson - 11

All You Need To Know About Python List

Lesson - 12

How to Easily Implement Python Sets and Dictionaries

Lesson - 13

A Handy Guide to Python Tuples

Lesson - 14

Everything You Need to Know About Python Slicing

Lesson - 15

Python Regular Expression (RegEX)

Lesson - 16

Learn A to Z About Python Functions

Lesson - 17

Objects and Classes in Python: Create, Modify and Delete

Lesson - 18

Python OOPs Concept: Here's What You Need to Know

Lesson - 19

An Introduction to Python Threading

Lesson - 20

Getting Started With Jupyter Network

Lesson - 21

PyCharm Tutorial: Getting Started with PyCharm

Lesson - 22

The Best NumPy Tutorial for Beginners

Lesson - 23

The Best Python Pandas Tutorial

Lesson - 24

An Introduction to Matplotlib for Beginners

Lesson - 25

The Best Guide to Time Series Analysis In Python

Lesson - 26

An Introduction to Scikit-Learn: Machine Learning in Python

Lesson - 27

A Beginner's Guide To Web Scraping With Python

Lesson - 28

Python Django Tutorial: The Best Guide on Django Framework

Lesson - 29

Top 10 Reason Why You Should Learn Python

Lesson - 30

10 Cool Python Project Ideas For Beginners in 2021

Lesson - 31

The Best Ideas for Python Automation Projects

Lesson - 32

12 Tips On How To Become a Python Developer

Lesson - 33

The Best Guide for RPA Using Python

Lesson - 34

Comprehending Web Development With PHP vs. Python

Lesson - 35

The Best Tips for Learning Python - REMOVE

Lesson - 36

The Best Way to Learn About Box and Whisker Plot

Lesson - 37

An Interesting Guide to Visualizing Data Using Python Seaborn

Lesson - 38

The Complete Guide to Data Visualization in Python

Lesson - 39

Everything You Need to Know About Game Designing With Pygame in Python

Lesson - 40

The Complete Simplified Guide to Python Bokeh

Lesson - 41

Top 150 Python Interview Questions and Answers for 2022

Lesson - 42

The Supreme Guide to Understand the Workings of CPython

Lesson - 43
Getting Started With Jupyter Network

Have you ever wondered why Python has become the most preferred language in multiple industries and educational institutions? It is because Python is simple and more straightforward to use than other object-oriented programming languages. Python also comes with a lot of in-built libraries that make data analysis and machine learning easy to implement.

This blog will help you understand Python with Jupyter Notebook and will help you learn the various features it provides to make programming simple and easy to learn. 

To read the official Jupyter Notebook document, visit the website


Click on Install to learn the steps for installing the Jupyter Notebook.


You will then be prompted to install Jupyter Notebook using Anaconda.

To download the Anaconda Distribution of Python, visit the website and click on the Download tab.


Next, download the latest version of Python for Windows Installer and choose either 64-Bit/32-Bit Graphic Installer based on your system’s configuration.


After the download is complete, install Anaconda to an appropriate location. 

Once the installation is complete, you can search for Anaconda in the Start menu of Windows and go to the Anaconda Prompt. 

In the Anaconda Prompt, type “jupyter notebook.” This will open the Jupyter homepage in a web browser.


You can also search for Anaconda Navigator and check the things it has got to offer.


As you can see, Ananconda provides a lot of other software, such as Sypder, Orange, RStudio, Visual Studio Code, etc.

To start the Jupyter Notebook, click on Launch, which will take you to the Jupyter homepage. You will be able to see all the files and folders present in the current working directory. You can change the working directory of the Jupyter Notebook any time you want.

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Any program written in Jupyter is saved as a .ipynb file, which stands for “interactive Python notebook.” Click on New and select Python3.


It will open a new Jupyter Notebook in a new tab.


Click on the first cell, and type print(“Hello World”). To run the cell, use Shift+Enter.


You can rename the Jupyter Notebook by clicking on Untitled. As shown in the example below, it is named JupyterNotebookBasics”.


To save the Jupyter Notebook file, click on the save icon. Now, you can close the file and open it any time you want from the Jupyter Notebook homepage.

Jupyter Notebooks have kernels, which are programs that run and introspect the user’s code.


Using a kernel, you can interrupt a program while it is running. You can restart a kernel, clear the output of the cells, run all the cells simultaneously, shut down a notebook, or change the kernel if required.

In the below example, you will see that if a kernel is restarted, the previous variables won’t be stored anymore.

hello simplilearn

Now, if you restart the kernel and run hello, you’ll receive an error message.


To run a particular cell, you can either select the Run button or hit Shift+Enter. This will create a new cell and move the pointer to the new cell once the execution of the current cell is complete.

To clear all the outputs, you can select Restart & Clear Output from the kernel.


You can also run all the cells at a time by selecting Restart & Run All from the kernel.

Similar actions can be performed using the Cell option.


 In Jupyter, the output of the code is generated right below the cell that you are running. This makes your code more readable and informative.

The asterisk symbol at the beginning of the cell indicates that the cell is still running.


Once you enter a name, the cell will complete its execution and generate the output.


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Jupyter allows you to give titles to any project you are working with. This is possible using the Markdown cell type. Click on Cell and select the Cell Type as Markdown. To give a more prominent size title, use a hashtag (#), followed by a space followed by the name of the title.

Jupyter Notebooks are useful for visualizing any amount of data. It creates the plot using the matplotlib visualization library right below the code. The following is an example of plotting a linear graph:


A Jupyter Notebook can be downloaded in multiple formats. Select File and click on Download as. This will show you the various formats in which you can save a Jupyter Notebook.


This is what the .html format of Jupyter Notebook looks like:


It will open in a web browser, and each line of code can be easily copied on to a Jupyter Notebook.

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To get more hands-on experience working with Jupyter, google jupyter notebook repository on git. This provides a lot of resources for practicing code with Jupyter.

We hope that having read this blog, you've understood the Anaconda Distribution of Python, the interface of the Jupyter Notebook, and how to write a Python program in Jupyter, run the cells, rename a notebook, and add a title to the code. You would have also gained knowledge of plotting a simple linear graph using the matplotlib library. Finally, you would have understood how to download a notebook in various formats, such as .ipynb, .py, .html, .pdf, etc.

To get more in-depth training, sign up for Simplilearn’s Python Training course today!

About the Author

Avijeet BiswalAvijeet Biswal

Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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