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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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Recent Reviews

TrustRadius Insights

Jupyter Notebook has found a wide range of use cases across various industries and roles. Data analysts, scientists, and engineers have …
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A Notebook for All

10 out of 10
October 24, 2021
Incentivized
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 …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 16 features
  • Interactive Data Analysis (21)
    9.6
    96%
  • Visualization (21)
    9.6
    96%
  • Connect to Multiple Data Sources (21)
    9.0
    90%
  • Data Transformations (21)
    8.9
    89%
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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…

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visithttps://www.trustradius.com/buyer…

Offerings

  • 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

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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 8.5

Data Exploration

Ability to explore data and develop insights

9.6
Avg 8.4

Data Preparation

Ability to prepare data for analysis

9
Avg 8.2

Platform Data Modeling

Building predictive data models

8.9
Avg 8.5

Model Deployment

Tools for deploying models into production

8.8
Avg 8.6
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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

(123)

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.

Attribute Ratings

Reviews

(1-22 of 22)
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Score 7 out of 10
Vetted Review
Verified User
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.
Shailesh Pandey | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
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.
October 24, 2021

A Notebook for All

Score 10 out of 10
Vetted Review
Verified User
Incentivized
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.
September 27, 2021

Jupyter Pros and Cons

Score 10 out of 10
Vetted Review
Verified User
Incentivized
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.
Lindsay Veazey | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
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.
Ejaz Hussain | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
Hemant Jajoo | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
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.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
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.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
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.
Score 7 out of 10
Vetted Review
Verified User
Incentivized
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
October 22, 2020

Jupyter or nothing

Score 10 out of 10
Vetted Review
Verified User
Incentivized
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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