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What Is Jupyter Notebook: An In-Depth Introduction

Jupyter Notebook is one of the most popular open-source web-based interactive computing platforms used by over 2 million users across various fields like data science, machine learning, and scientific computing.

This comprehensive guide provides a deep dive into Jupyter Notebook and how it enables interactive exploratory computing.

What Exactly Is Jupyter Notebook?

Jupyter Notebook is a web-based interactive computing environment that allows users to author documents that combine live code with narrative text, visualizations, interactive widgets and other rich output.

Some key components that make up Jupyter Notebook:

  • Web Application: Jupyter Notebook is a client-server web application that provides access to notebook documents via a modern web browser.
  • Notebook Documents: Self-contained documents with live code, text, visualizations and more. Stored as JSON files with .ipynb extension.
  • Kernels: Background processes that run and introspect different programming languages like Python, R, Julia etc.
  • Notebook Dashboard: Landing page interface to manage your notebook files and kernels.

Under the hood, Jupyter Notebook is powered by:

  • Jupyter Server: Responsible for managing kernels and providing APIs for front-end access. Written in Python.
  • IPython Kernel: Default Python kernel with magics and helper functions.
  • Tornado Web Framework: Provides the Python web server used by Jupyter notebook.
  • Notebook UI: Front-end interface written in JavaScript, HTML and CSS. Interacts with server using AJAX.

This architecture allows Jupyter Notebook to provide interactive computing experience in modern web browsers. The document interface combines inputs and outputs of many languages side-by-side.

What is Jupyter Notebook Used For?

Jupyter notebooks provide an incredibly useful tool across many different use cases:

1. Data Cleaning and Transformation

The interactive notebook environment enables iterative data cleaning and munging. Code can be rerun with changes as data issues are discovered during exploration.

# Load pandas and numpy
import pandas as pd
import numpy as np

# Import dataset 
data = pd.read_csv(‘data.csv‘)

# Inspect data

# Drop missing values
data = data.dropna()

# Data cleaning and filtering
data = data[data[‘Column‘] > 100]

2. Data Visualization and Exploration

Jupyter seamlessly integrates code execution for data visualization using libraries like Matplotlib, Seaborn, Plotly etc.

# Plot histogram 


Rich interactive widgets can be used to build powerful dashboards for data exploration.

3. Machine Learning Model Building

Notebook allows iteratively developing and evaluating ML models locally or integrating with platforms like AWS SageMaker:

# Load data
X_train, X_test, y_train, y_test = train_test_split(data)

# Train model
model = LogisticRegression(), y_train)

# Evaluate model
print("Accuracy:", model.score(X_test, y_test))

Built models can also be exported and deployed to production.

4. Model Deployment

Models built in Notebook can be exported to formats like ONNX and deployed to cloud platforms:

# Export trained model
onnx_model = onnxmltools.convert_sklearn(model)
onnxmltools.utils.save_model(onnx_model, ‘model.onnx‘)

5. Developing Applications

Notebook supports developing full applications with Jupyter widgets, Voila etc:

# Slider widget
slider = widgets.IntSlider(min=0, max=100) 

# Display widget
widgets.interact(f, x=slider)

Jupyter Widget

6. Big Data Analysis

Jupyter Notebook can connect to big data engines like PySpark for large scale data processing:

# PySpark session
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName(‘App‘).getOrCreate()

# Read CSV
df =‘data.csv‘)

# Display DataFrame

7. Scientific Computing

Notebook supports many popular numeric and scientific computing libraries like NumPy, SciPy, AstroPy, Chaospy, SymPy etc.

# Polynomial curve fitting
import numpy as np
from scipy import optimize

def error(coeffs, x, y):
    return y - np.polyval(coeffs, x)

coeffs = optimize.leastsq(error, [0, 0, 0], args=(x, y))[0]

This allows researchers and scientists to leverage Notebook for their applications.

8. Education and Training

The annotated notebook documents are immensely valuable for teaching programming and data science topics. Many courses use Jupyter notebooks for assignments and tutorials.


9. Rapid Prototyping

Notebook provides a workflow promoting quick iterative development crucial for rapid prototyping – tweak code, rerun, update, repeat.

10. Collaboration

Notebooks can be easily shared allowing teams to collaborate with context provided via code, visualizations, Markdown narrative etc.

According to Kaggle‘s 2021 State of Data Science report, over 60% of data scientists and ML practitioners use Jupyter Notebook daily. It ranks as the 2nd most popular development tool behind only Google Colab.

Notebooks are used extensively across data exploration, machine learning model development, visualization, engineering applications, scientific computing, and many other domains. Its immense popularity stems from enabling an interactive coding environment seamlessly combined with rich outputs.

Next, let‘s go through the steps to get started with Jupyter Notebook.

Setting Up Jupyter Notebook

Jupyter Notebook is simple and quick to install. Here is how to set it up:


First, ensure Python 3.6 or above is installed. Then install the Jupyter package using pip:

pip install jupyter

This will install Jupyter Notebook and its dependencies like iPython and IPython.

Running Jupyter Notebook

Launch Jupyter Notebook by running:

jupyter notebook

This will start the Jupyter Notebook server and launch the web application:

Jupyter Dashboard

That‘s it! You are ready to create your first notebook.

Installation Options

Some other installation options for Jupyter Notebook include:

  • anaconda: Install Jupyter via the Anaconda Python distribution
  • pipenv: pipenv install jupyter to install in a virtual environment
  • Docker: docker run jupyter/notebookruns a container with Jupyter

Configuring Kernels

Over 100 Jupyter kernels for different languages are available:


You can install additional kernels like:

pip install ijavascript  # JS kernel
pip install jupyter_R_kernel # R kernel

And set the default kernel in Notebook config.

This allows Jupyter Notebooks to support many languages beyond Python.

How to Use Jupyter Notebook

Now let‘s explore the key components of the Jupyter Notebook web interface and how to work with notebooks:

Notebook Files

Notebook files have the .ipynb extension and are stored in JSON format containing text, code, metadata and output. Notebooks can be exported to other formats like HTML, Markdown, PDF etc.

Notebook Dashboard

The Jupyter dashboard lets you create new notebooks, manage existing ones, work with kernels, and see the filesystem:


Notebook UI

The notebook UI presents notebook documents and allows interacting with them:



Notebooks contain a sequence of cells that can contain code or Markdown text. Each cell outputs results independently when run.

Code Cells

Code cells allow writing and executing source code in a kernel language:

Code cell

Markdown Cells

Markdown cells contain formatted narrative text using Markdown syntax:


Cell Shortcuts

Handy shortcuts improve Notebook productivity:

  • Shift + Enter – Run cell
  • Alt + Enter – Run cell in-place
  • Esc + A – Insert cell above
  • Esc + B – Insert cell below
  • Esc + D + D – Delete cell

Kernel Management

Kernels execute code cells and introspect languages:


Switch or configure kernels via the Jupyter dashboard.

Static v/s Dynamic Content

  • Code cell outputs are dynamic and update on re-execution
  • Markdown content is static and does not change on re-run

This allows mixing static narrative with dynamic code and outputs.

Mixing Content

Notebook documents allow seamlessly mixing code, outputs, visualizations, Markdown narrative etc:


The annotated notebook captures the contextual thought process along with the code.


Notebook extensions add features like table of contents, code folding, auto-save etc:


Over 150 extensions provide deeper customization.

These features enable notebooks to provide an interactive environment for live computing. Next, let‘s see how to leverage notebooks for web scraping projects.

Using Jupyter Notebook for Web Scraping

Here is an overview of how Jupyter Notebook can be used for web scraping tasks:

Import Libraries

Import required Python libraries for scraping and data analysis:

from bs4 import BeautifulSoup
import requests
import pandas as pd

Send Requests

Use requests module to download web page HTML content:

url = ‘‘
response = requests.get(url)
html = response.text

Parse Content

Use BeautifulSoup to parse HTML and extract data into Python lists/dicts:

soup = BeautifulSoup(html, ‘html.parser‘)

names = []
for name in soup.find_all(‘span‘, class_=‘name‘):

data = {‘name‘: names}
df = pd.DataFrame(data)

Clean and Analyze

Clean extracted data and analyze using Pandas, Matplotlib etc:

# Data cleaning
df = df[df[‘name‘].str.len() > 3]

# Visualization

Export to File

Finally data can be exported to file formats like CSV for storage:

df.to_csv(‘data.csv‘, index=False)

Jupyter Notebook provides a handy tool to build scraping code iteratively with visible outputs. Code, explanations, visualizations can be combined into an integrated narrative.

However, once the scraping logic has been finalized, it is often better to restructure notebooks into Python modules and scripts for production use.

Notebooks strike the right balance of interactivity for scraping during research and learning phases.

Jupyter Notebook Alternatives

Some popular alternative notebook interfaces similar to Jupyter include:

  • Apache Zeppelin: Notebook for data-driven applications supporting SQL, Scala, Python, R, etc. Integrates with Spark.
  • Kaggle Notebooks: Managed Jupyter based notebooks for data science competitions and education.
  • Apache Spark Notebook: Based on Apache Zeppelin, for Spark clusters.
  • Azure Notebooks: Cloud-based Jupyter based notebooks for education and training.
  • Observable: Notebooks integrated with reactive JavaScript notebook interface.
  • Google Colab: Jupyter hosted notebooks on Google Cloud. Provides free GPUs.

While these offerings have their own benefits, Jupyter remains the most widely used open-source notebook solution.

Challenges with Jupyter Notebooks

While being immensely popular, Jupyter notebooks also come with some downsides:

  • Version Control: Notebooks are challenging to version control and merge changes. Difficult collaboration.
  • Reproducibility: Output depends on runtime environment. Hard to reproduce notebooks reliably.
  • Testing: No easy way to do unit testing of code blocks in an isolated manner.
  • Naming: Renaming functions across notebooks causes issues due to lack of modules.
  • Scale: UI overhead causes performance issues for large datasets or models.
  • Security: Notebooks allow arbitrary remote code execution on servers.
  • Data Management: Minimal support for connecting to enterprise data sources and BI tools.

While Jupyter notebooks provide quick interactivity, for large production deployments, it may be better to extract notebook code into Python modules, packages and scripts with proper refactoring.

Notebooks are an excellent prototyping tool but have limitations for complex projects and collaboration due to the monolithic document structure.


Jupyter Notebook pioneered the concept of literate programming and remains an immensely popular tool for data-driven development workflows. The ability to integrate executable code with text, visualizations and more in a document makes Jupyter Notebook uniquely suited for iterative data exploration and modeling.

Notebooks shine during prototyping and learning phases of analytics and data science projects allowing faster experimentation. They capture valuable contextual information along with code. Jupyter has sparked an entire ecosystem of tools for interactive computing across various languages.

However, Jupyter Notebooks have limitations when it comes to complex multi-user collaboration, production deployment, reproducibility and workflow management. Understanding its capabilities and shortcomings allows effectively leveraging Jupyter Notebooks in domains where it provides the most value.

Frequently Asked Questions

Here are some common questions about Jupyter Notebooks:

Q: Can multiple users edit the same Jupyter Notebook file simultaneously?

A: No, simultaneous editing of a notebook by multiple users is not directly supported. Notebooks follow a single-user on single-machine model. However, users can independently open notebooks and merge changes later. Some extensions attempt to provide multi-user capabilities but can be buggy.

Q: How do I export Jupyter Notebook to PDF?

A: There are two main options:

  1. HTML to PDF – Use jupyter nbconvert to export notebook to PDF via HTML
  2. LaTeX to PDF – First convert notebook to LaTeX with nbconvert, then compile LaTeX file to PDF

The LaTeX approach usually provides the best formatting for math equations.

Q: What is the difference between Jupyter Notebook and JupyterLab?

A: JupyterLab provides the next generation interface for Jupyter. It offers a flexible IDE-like experience with a modular UI, integrated terminals, extensibility via extensions, and support for multiple tabs/editors within the same window. JupyterLab is intended to eventually replace the classic notebook interface.

Q: Can I run Jupyter Notebook in the cloud?

A: Yes, Jupyter Notebook can be run on many cloud platforms like AWS, GCP, Azure, Databricks, SageMaker etc. Cloud execution allows leveraging scalable cloud infrastructure without local environment setup.

Q: Is Jupyter Notebook suitable for web scraping large sites?

A: For large scale production web scraping, standalone Python scripts would be better optimized for performance instead of notebooks. Notebooks are useful for experimentation but have UI overhead.

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