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Jupyter Notebook

Jupyter Notebook

Overview

What is Jupyter Notebook?

Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports…

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Pricing

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What is Jupyter Notebook?

Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine…

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  • No setup fee
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  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Product Demos

H2O TensorFlow Deep Learning Demo

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EXPORT JUPYTER to EXCEL | nb2xls | Demo & My Thoughts | Jupyter Notebook to Excel Spreadsheet

YouTube

Jupyter Notebook using Docker for Data Science (Demo)

YouTube

Lecture 11, Python Demo for Distribution

YouTube
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Features

Platform Connectivity

Ability to connect to a wide variety of data sources

8.5
Avg 8.3

Data Exploration

Ability to explore data and develop insights

9.6
Avg 8.5

Data Preparation

Ability to prepare data for analysis

9
Avg 8.2

Platform Data Modeling

Building predictive data models

8.9
Avg 8.4

Model Deployment

Tools for deploying models into production

8.8
Avg 8.5
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Product Details

What is Jupyter Notebook?

Jupyter Notebook Video

How to install and use Jupyter Notebooks, a step by step tutorial. Learn to when to use Jupyter Notebooks, and how to write and run code and markdown.

Jupyter Notebook Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for Jupyter notebooks, code, and data, with a configurable user interface that supports a wide range of workflows in data science, scientific computing, and machine learning.

Reviewers rate Visualization and Interactive Data Analysis highest, with a score of 9.6.

The most common users of Jupyter Notebook are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(128)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Jupyter Notebook has found a wide range of use cases across various industries and roles. Data analysts, scientists, and engineers have utilized Jupyter Notebook to handle different types of data, including csv, excel, and json files for efficient data analysis in banking and e-learning industries. The flexibility of the notebook allows users to organize workflows into manageable chunks, making them easier to replicate for future projects. Additionally, it has been widely adopted by Science and Analytics departments for tasks such as data wrangling, graph visualization, machine learning model creation, and suite of analyses to understand business landscapes. Jupyter Notebook has also been instrumental in training and fine-tuning machine learning models efficiently, as well as automating tasks like scraping data from PDFs. With its ability to integrate with cloud desktops and production systems, Jupyter Notebook is widely used by data scientists in organizations. Furthermore, it serves as a platform for collaboration and code sharing among teams, making it valuable for project management. Overall, Jupyter Notebook's interactive environment and flexibility have made it a versatile tool recommended for data analysts, managers, engineers, and data scientists seeking to explore, analyze, visualize, and deploy Python code efficiently.

Intuitive Organization: Users have found Jupyter Notebook's visually intuitive organization of code to be beneficial for understanding and navigating through different sections. This seamless workflow has increased productivity for many reviewers.

Static but Changeable Display: The static but changeable display of function outputs in Jupyter Notebook has been highly valued by users, allowing them to easily replicate notebooks or convert them into PDFs for documentation and sharing purposes. This feature provides a convenient way to showcase analysis results and collaborate with others.

Step-by-Step Output Viewing: Many users appreciate the ability to see the output after each step in Jupyter Notebook, enabling a step-by-step approach to data analysis and visualization. This functionality helps users identify and rectify errors or inconsistencies in their code efficiently.

Limited Features: Some users have expressed that Jupyter Notebook lacks certain features and functionality, such as limited markdown styling and the inability to handle multiple kernels. They feel that these limitations restrict their ability to fully utilize the software for their needs.

Difficult Code Styling and Navigation: A common concern among users is the difficulty in styling Python code within Jupyter Notebook and navigating through long notebooks. Several reviewers have suggested the addition of bookmarks or easier ways to navigate, as they find it time-consuming and cumbersome to work with large amounts of code.

Absence of Dark Mode Option: The absence of a dark mode option has been mentioned by several users as a helpful feature that could improve the user experience. They believe that having a dark mode would reduce eye strain during prolonged usage sessions and enhance overall readability.

Reviews

(1-22 of 22)
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Jupyter feels like you are on moon

Rating: 7 out of 10
April 29, 2022
Verified User
Vetted Review
Verified User
Jupyter Notebook
2 years of experience
Mostly I use Jupyter Notebook to test some python code that I work on.
  • Easy to use
  • Easy to understand
  • Faster
If I want test some code quickly then I just open up Jupyter Notebook because by using it I can run and check if my code runs correctly or not at every step. I can easily check if the conditions specified are working as expected or not within a matter of time.

Jupyter Notebook: A boon to present codes

Rating: 9 out of 10
December 16, 2021
Verified User
Vetted Review
Verified User
Jupyter Notebook
5 years of experience
Jupyter Notebook is the most friendly software for testing and running python codes of all types. Its simple to illustrate and visualise and test a code line by line. Also if not downloaded you can still use it as a google collab notebook. Its easy and simple to use for data models, statistical visualisation and machine learning programs. I have been using it constantly in my research works.
  • Coding and error correction line by line
  • Simple and Effectiveness
  • Easy to use for visualisation and presentation of code
  • Could be used at any place any time without hassle
Best for presentation of codes and results. However needs some standard libaries such as numpy, pandas, etc to be already imported.

Feedback - Jupyter Notebook

Rating: 10 out of 10
November 29, 2021
SP
Vetted Review
Verified User
Jupyter Notebook
1 year of experience
I do all my python projects with it every day and the amount of convenience it provides is unmatchable. It's interface is very easy and self expressive which is why I recommend it the most. It comes with the code sharing feature which i use when I get stuck somewhere or having some bug or needed any advice from the team lead.
  • Great interface.
  • Segments for codes.
  • Markdown for code explanation.
Many data science tutors use Jupyter Notebook to teach their students. It's an open source software with markdown to explain codes and code segments to make it more clean, error free and self explanatory. It is best IDE for the data science and python programming beginners because of it's interface and code segments where one can run their codes separately.

A Notebook for All

Rating: 10 out of 10
October 24, 2021
Verified User
Vetted Review
Verified User
Jupyter Notebook
6 years of experience
Jupyter Notebook is a coffee to me, a daily dose of it makes my day. I have been using Jupyter Notebook for various purposes from presenting a Proof of Concept to the client, to deploying it on the cloud, teaching new hires about their role, and explaining things to my team. Development is also super easy on Jupyter Notebook with its interactive environment where one can keep codes, comment, and plot images together on a single platform. It's readable and flexible. You can have host server-side also which is great and allows you to access your Notebook from anywhere around the globe. In my organization, Jupyter Notebook is used a lot for various reasons. It's presentable, powerful (not as powerful as IDE though but it has its own purpose), flexible, and most importantly its open source. [For] several years since the boom of Data Scientist fields Jupyter Notebook [has been] getting more and more popular among those who analyze data. I highly recommend Jupyter Notebook because it will make your life's work easy and fun, whether you are a manager who creates presentations for the clients, an engineer who develops code, or a Data Scientist/Analyst, Jupyter Notebook covers all.
  • Giving presentation to a client or explaining your code to a colleague
  • Developing code snippets for big or small projects
  • Easy to share your work with others
  • Highly recommended data analysis presentation
Jupyter Notebook is well suited for presentations, creating codes snippets, or analyzing big pieces of code in small chunks. It's highly recommended for [a] data analysis job in which one can plot the interactive and Live figures or graphs. Not recommended for [the] full development of [a] project without an IDE but only creating part of it which can help a lot for developing big projects.

Jupyter "THE ATTRACTIVE" Notebook

Rating: 10 out of 10
October 08, 2021
SR
Vetted Review
Verified User
Jupyter Notebook
3 years of experience
Jupyter Notebook is one of the most important applications for my personal use and for the organization purpose also. It helps to develop open source software and for interactive computing across different programing languages. I'm using this since my college time, it supports Julia, Python and R. It allow us to create and share documents that contains live code and graphs.
  • We can use it as a notebook and share the slide and also publish it online through GitHub.
  • Attractive programming environment.
  • Easy navigation platform.

Jupyter Pros and Cons

Rating: 10 out of 10
September 27, 2021
RL
Vetted Review
Verified User
Jupyter Notebook
5 years of experience
Jupyter notebooks are widely used by our Science and Analytics departments to analyze data, make forecasts, clean/wrangle data, graph visualizations, create machine learning models, and perform a suite of analyses to best understand our business landscape.
  • Sharing/showcasing work in a step by step manner
  • Exploratory data analysis/viewing code in-line
  • Data exploration/visualization
  • Switch between different coding languages
Jupyter notebooks are great for data science, especially if you want to clean and transform data, and explore outcomes/visualize/model in real-time. Once you have a successful logic built out, though, it's best to move the code away from a notebook for production.

Flexible, lightweight, and visually intuitive organization of your workflows

Rating: 10 out of 10
July 20, 2021
LV
Vetted Review
Verified User
Jupyter Notebook
5 years of experience
I use Jupiter Notebook to organize my workflows into manageable and readable chunks. The ability to streamline my work using markdown descriptions and title is extremely helpful for visually cueing transitions and reorienting the user to the pattern of the workflow outlined in the notebook. It also makes it easier to replicate the workflow for other projects.
  • Visually intuitive organization of code.
  • Static (but changeable) display of function outputs.
  • Easy replication of notebooks or into new notebooks, or into PDFs.
I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

Innovative & easy-to-use tool for ML & data visualization

Rating: 9 out of 10
June 14, 2021
Almost every large-scale enterprise today has started to have in-house ML models & my organization is no different. We have been making use of this wonderful tool for Data visualization purposes, consuming python libraries to get some insights from the larger datasets from different data sources. Also, we use it to train our ML models to make predictions & then use that to further fine-tune the models efficiently.
  • User-friendly UI.
  • Easy to debug at each code line.
  • Great support for Python Math libraries.
  • Advanced data visualization capabilities.
  • Notebook sharing feature.
Jupyter Notebook is well suited for someone who's trying to learn python programming with the aim of training the ML models & get some really insightful analysis out of it. It really is a very user-friendly tool for any beginner as they can check the output on any of the written code without having to wait for the whole code to be finished.

Python programming made easy with Jupyter!

Rating: 9 out of 10
June 01, 2021
RV
Vetted Review
Verified User
Jupyter Notebook
3 years of experience
Jupyter Notebook is a really dynamic tool, currently used by the Engineering team at my company. The main focus of using it is to incorporate python programming while writing code to forecast sales. Jupyter gave me the flexibility of writing code in a line-by-line manner and help me see the output at each step.
  • Simple and elegant code writing ability. Easier to understand the code that way.
  • The ability to see the output after each step.
  • The ability to use ton of library functions in Python.
  • Easy-user friendly interface.
I would rate it 9/10 while recommending Jupyter Notebook as it offers me a wide range of functionality to operate. It is very well suited for someone who is new to python programming as the user interface helps you build code line by line. I personally have written multiple programs in Python using Jupyter Notebook as it helps me organize long code by breaking it in a structure. Also the ability to write comments using '#' helps a lot to a reader understand the code.

Easy to maintain modularity and write clean and efficient code

Rating: 9 out of 10
May 29, 2021
HJ
Vetted Review
Verified User
Jupyter Notebook
3 years of experience
It is used mostly by our Python and R programmers for programming. It is very interactive and easy to manage the programs and present them. Coding in it is very easy and we can also easily debug. We can do many things--change the cell types, use shortcuts, and if stuck somewhere restart the kernel.
  • Interactive
  • Efficient
  • Quick
If you want to develop a project in Python or R then Jupyter Notebook is the perfect idle that you should use. It is easy to maintain modularity and write clean and efficient code. We can also use it for connecting to various databases like MongoDB from our Python script.

Jupyter makes life easier.

Rating: 8 out of 10
May 13, 2021
AJ
Vetted Review
Verified User
Jupyter Notebook
2 years of experience
We are an E-learning company. We use Jupyter Notebook in our lab's virtual environment.
  • Its one by one cell execution.
  • It is user friendly and easy to work on.
  • Only installing Anaconda can bring us to use this Jupyter Notebook easily.
  • Freely available.
It is well suited everywhere. In web development one cannot use Jupyter Notebook.

Jupyter Notebook - A solid choice for early stage data analysis!

Rating: 8 out of 10
May 13, 2021
Verified User
Vetted Review
Verified User
Jupyter Notebook
4 years of experience
Jupyter Notebook is used widely within our data science groups as a way to experiment with ML and other models. It's great for early stage data analysis as well as for training and sharing of new Python models within the group in a clear way. The interface is easy to use and onboard onto and has been a great way for members to easily share and onboard new associates onto their existing Python analysis scripts.
  • Markdown for comments/explanations.
  • Interactive programming.
  • Easy to use and share notebooks.
Well suited to exploratory or initial phase data analysis where you want to quickly and interactively explore different models and be able to communicate it easily to either other team members or even external partners without much hassle. Less appropriate for large scale deployments or production level software engineering.

Jupyter Notebook for quick in-depth analysis

Rating: 9 out of 10
May 02, 2021
SP
Vetted Review
Verified User
Jupyter Notebook
1 year of experience
We are using Jupyter Notebook for data visualization of JSON and excel data for a banking customer. We are running multiple python scripts on Jupyter Notebook for in-depth data analysis to develop machine learning models.
  • Easy to learn and use
  • Data [modeling]
  • Data analysis and reporting
  • Predefined visualizations models
Jupyter Notebook is best suited for scenarios where you have small to medium size JSON/ Excel data. It is not well suited for large amounts of data.

Great product for data modeling

Rating: 10 out of 10
May 02, 2021
Jupyter Notebook is used by the profiling department and we use it for data analysis and modeling.
  • Great visualizations
  • Easy to edit and track data flows
[Jupyter Notebook is] great for data analysis, [feature engineering,] and data modeling.

Jupyter Notebook is the leading open source tool for interactive and literate coding

Rating: 10 out of 10
May 01, 2021
Verified User
Vetted Review
Verified User
Jupyter Notebook
4 years of experience
Jupyter Notebook is used for interactive and literate programming for Python and R. It is deployed server side, and it is used across the organization to allow users to code through their web browser. It gives users access to Python and R programming, without needing the users to maintain it.
  • Literate programming
  • Interactive programming
  • Server side deployment supporting many users to code via web browser.
Jupyter Notebook is great for interactive and literate coding. This is great for data analysis type of tasks. For more programmatic coding, you still need an IDE.
Jupyter allows multi-users to access the same environments. Additional environments can be installed via Conda, but this can be complicated.
Jupyter can be deployed via Kubernetes for example to allow virtualized environments for each user.

Best to get started for data scientists

Rating: 9 out of 10
May 01, 2021
AR
Vetted Review
Verified User
Jupyter Notebook
1 year of experience
Jupyter Notebook was first used by me for a project for my Big Data Class to handle data in csv, excel, json, etc. I used python and a lot of flexible libraries in it to do data analysis on our banking information like documents, statements, reports etc. and draw visualizations on it.
  • Data Analytics
  • easy visualization
  • ML possibilities
It's user friendly and easy for beginners to get started on.

All in one workspace for Data Sciences

Rating: 9 out of 10
May 01, 2021
Verified User
Vetted Review
Verified User
Jupyter Notebook
4 years of experience
We currently use Juptyer Notebooks across our organization. Jupyter Notebooks are our go-to experimenting environments for data pre-processing, creation, training and evaluation of machine learning and deep learning models. We also heavily use Jupyter for data visualization and exploratory data analysis. It also provides a great interactive interface which can be used for story telling to our clients and consumers.
  • Easy and interactive Python environment.
  • Latex markdown for explanations.
  • Terminal access through cell itself.
  • Fast Intellisense.
  • Documentation access through cell commands.
  • Intuitive Key Bindings.
If you want to do exploratory data analysis, ML and DL model training, and evaluation with data pre-processing then Jupyter Notebook is the best. It has a good community support as well. If you want to develop Python API or scripting or backend development, other open source code editors are a better fit.

Jupyter Notebook for the win!

Rating: 10 out of 10
April 30, 2021
Jupyter Notebook is an amazing way to see the code work and plots displayed in an awesome in-line perspective. We really enjoy it especially when plotting data analysis and comparisons. It is used by many of us at my department.
  • Data plotting
  • Easy to switch settings and see the changes right away in the plots.
  • Easy user interface, and commands.
Bayesian estimation and data comparison.

Jupyter Notebook - For a better data analysis and visualizations

Rating: 8 out of 10
April 07, 2021
RK
Vetted Review
Verified User
Jupyter Notebook
1 year of experience
Jupyter Notebook is being used currently at my organization to handle data in any of the forms including csv, excel, json, etc. We use python and a lot of flexible libraries in it to do data analysis on our banking information like documents, statements, reports etc. and draw visualizations on it.
  • Data Analysis
  • Visualizations
  • Documentation
Jupyter Notebook is well suited if you want to do exploratory data analysis, draw observations and visualizations from it. Based on your analysis and visualization it can help you make better business decisions.
It is less appropriate for any kind of python development as I have mainly used it for documentation, data handling, cleaning or visualizations.

Jupyter Notebook, the perfect place to start

Rating: 10 out of 10
February 24, 2021
JJ
Vetted Review
Verified User
Jupyter Notebook
3 years of experience
I use Jupyter Notebook to run Python codes for data science and visualization. It is being used by the data analytics department and it allows the team to work on analytics projects and write insightful reports.
  • Python
  • Markdown presentation
  • Easy to learn
  • Good organization
If you want to start a data analytics project and want to have an organized way to write and execute codes.

Not so appropriate if you want to make codes that are constantly running.

Light user of Jupyter Notebook

Rating: 7 out of 10
January 26, 2021
YX
Vetted Review
Verified User
Jupyter Notebook
3 years of experience
I use Jupyter Notebook to run statistical analysis and develop machine learning models and deploy in production. Jupyter Notebook is widely adopted by data scientists in our organization. It's well integrated with our remote cloud desktop as well as production system. We use Jupyter Notebook to understand customer behaviors and how to improve customer experience.
  • Big data analysis on cloud desktop.
  • Exploratory analysis.
  • Common machine learning models.
I think Jupyter Notebook is well suited for scenarios below:
1) analyze big data above millions of records
2) develop machine learning codes that can be deployed in production system

I think Jupyter Notebook is less appropriate for scenarios below:
1) quick and easy statistical analysis
2) entry level users

Jupyter or nothing

Rating: 10 out of 10
October 22, 2020
Verified User
Vetted Review
Verified User
Jupyter Notebook
3 years of experience
I started using Jupyter to solve my everyday problems and to do things faster. I'm aware that there a few users already across the organization. I have been recommended to use it and I have also recommended this fantastic tool. With Jupyter, it's really easy to break the problem into small pieces and develop larger, more productive code. It's also easy to enhance without breaking the remaining part of code. I develop working code as and when time permits, integrate with different tools (through API, DB calls, etc), and even plot beautiful graphs all in one notebook. The best thing is that the results are saved when I reopen the notebook.
  • Saves the results until next time the notebook is reopened
  • Provides so many libraries
  • Allows to code on multiple platforms
  • Automatically creates checkpoints
  • Really lightweight
Juypter suits developers the most, who want to write small pieces of code and create larger modules. Jupyter is also suited when creating smaller integration scenarios. Jupyter enables creating neat graphical charts for analysts very quickly. It is particularly easy to create and share notebooks to who so ever. For Enterprise-grade scenarios and large scale implementations, Jupyter may not be the best fit.
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