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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

(125)

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

(1-18 of 18)
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Score 7 out of 10
Vetted Review
Verified User
Jupyter is very easy to understand and easy to use. And can also be used by a student, freelancer, small industries, big industries. Jupyter also provides you a tool to work with machine learning and artificial intelligence.
Shivam Rai | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Jupyter Notebook is very attractive platform for new developers to code and to learn programming and perform tasks as compared to other IDE. It has very well and easy visualization, interactive programming and sharing the live code and slideshow is very easy as compare to others. online
Lindsay Veazey | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Jupyter Notebook is unique in that it offers a flexible, lightweight, easy-to-replicate way of organizing your code in a visually intuitive fashion that can be exported in a number of formats. I've found that the broad functionalities available within the notebooks suit a lot of needs I have for EDA, modeling, and data export that makes other software products fairly redundant.
Ejaz Hussain | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Well, so far Jupyter Notebook has been the better tool for me. It gives us more freedom & has more ability to train ML models & do the data visualization more efficiently. It's easier to operate & has a very simple-to-understand UI & with the support for taking data from multiple sources, it becomes even more powerful.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
I have used PyCharm as well as Jupyter Notebook and for me, Jupyter wins almost every time. I really like its user-friend interface for someone who is new to python programming. The ability to run a big chunk of code part by part is a big game-changer for me. One thing I would like is a night mode just like PyCharm as it helps the night owls like myself who work a big part during the night. I also like how Anaconda command prompt helps you download a library and just a simple import statement in the notebook helps you extract every functionality.
Hemant Jajoo | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
It should have cleaner support for multi-environment setup and should also increase the amount of features. Moreover, more support should be present for other programming languages. It should also have the option to set a specific location that opens up whenever I run command despite the need of me going to that location.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Jupyter is easier to handle and user friendly.

We have free access to it and its cell by cell executing feature is amazing.

Also, Jupyter allows to make changes in the previous code. We do not have to write the same code again and again if we want to run it.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Jupyter Notebook has a nicer interface than RStudio in our opinion and since most of our group is familiar with Jupyter Notebook it has made it a default choice. Overall the interactive programming as well as the easy visualizations, model deployment, and markdown made Jupyter Notebook a good choice although some people do still use RStudio for specific projects. Jupyter Notebook is a bit nicer as a communication tool so it has become a preferred method of sharing and getting feedback on early stage data analysis and modeling.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Jupyter Notebook is the core feature extended on by many commercial alternatives. The commercial alternatives have more feature integration with the rest of their portfolio. RStudio is another competitor for interactive and literate programming.

We deploy both Jupyter and RStudio in our organization. We use commercial platforms when we need specific features.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
An interesting thing is that Jupyter Notebook is run on browser environments which may or may not be a positive feature according to cases. VS Code on [the] other hand doesn't use any interface and can run Jupyter Notebooks too. Sometimes my browser consumes too much RAM due to which I shifted to VS Code for development purposes.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
I like Jupyter Notebook over the other two because it keeps my work more organized. It helps me to structure my workflow and the ability to run commands in chunks keeps me from being confused when coming back to the work after some time.
October 22, 2020

Jupyter or nothing

Score 10 out of 10
Vetted Review
Verified User
Incentivized
When I tried Zeppelin in 2017, it was still in initial versions, Jupyter was way ahead as of then. Zeppelin had limitations and I wasn't confident of it making progress as much as Jupyter.
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