Jupyter Notebook Description

The Jupyter Notebook is a web-based open-source tool that enables users to create and distribute documents featuring live code, visualizations, equations, and written explanations. Its applications are diverse and encompass tasks such as data cleaning and transformation, statistical modeling, numerical simulations, data visualization, machine learning, among others, showcasing its versatility in various fields. Additionally, it serves as an excellent platform for collaboration and sharing insights within the data science community.

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Easily switch between command and editor modes using just one keystroke while navigating through cells with arrow keys. Take advantage of all standard Jupyter shortcuts for a smoother experience. Experience fully interactive outputs positioned directly beneath the cell for enhanced visibility. When working within code cells, benefit from intelligent code suggestions, real-time error detection, quick-fix options, streamlined navigation, and many additional features. You can operate with local Jupyter notebooks or effortlessly connect to remote Jupyter, JupyterHub, or JupyterLab servers directly within the IDE. Execute Python scripts or any expressions interactively in a Python Console, observing outputs and variable states as they happen. Split your Python scripts into code cells using the #%% separator, allowing you to execute them one at a time like in a Jupyter notebook. Additionally, explore DataFrames and visual representations in situ through interactive controls, all while enjoying support for a wide range of popular Python scientific libraries, including Plotly, Bokeh, Altair, ipywidgets, and many others, for a comprehensive data analysis experience. This integration allows for a more efficient workflow and enhances productivity while coding.
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Kaggle provides a user-friendly, customizable environment for Jupyter Notebooks without any setup requirements. You can take advantage of free GPU resources along with an extensive collection of data and code shared by the community. Within the Kaggle platform, you will discover everything necessary to perform your data science tasks effectively. With access to more than 19,000 publicly available datasets and 200,000 notebooks created by users, you can efficiently tackle any analytical challenge you encounter. This wealth of resources empowers users to enhance their learning and productivity in the field of data science.
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Pricing

Free Version:
Yes

Integrations

View Integrations

Reviews - 3 Verified Reviews

Total
ease
features
design
support
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Company Details

Company:
Project Jupyter
Year Founded:
2014
Website:
jupyter.org

Media

Jupyter Notebook Screenshot 1
Jupyter Notebook Screenshot 1
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Product Details

Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support

Jupyter Notebook Features and Options

Application Development Software

Access Controls/Permissions
Code Assistance
Code Refactoring
Collaboration Tools
Compatibility Testing
Data Modeling
Debugging
Deployment Management
Graphical User Interface
Mobile Development
No-Code
Reporting/Analytics
Software Development
Source Control
Testing Management
Version Control
Web App Development

Data Science Software

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Jupyter Notebook User Reviews

Write a Review
  • Name: Anonymous (Verified)
    Job Title: Principal Software Engineer
    Length of product use: 6-12 Months
    Used How Often?: Daily
    Role: User
    Organization Size: 100 - 499
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Excellent cloud-based script editor and VM

    Date: Aug 09 2022

    Summary: Jupyter Notebook makes it possible to run your own machine learning programs without needing to invest in powerful computational hardware. Its cloud based editor makes it simple to work from anywhere.

    Positive: - makes it very easy to start developing
    - cloud based dev environment means you can write and run scripts from anywhere
    - compute tiers include GPUs so you can run very heavy data analysis operations in the cloud
    - low per-minute compute pricing

    Negative: - interface could be nicer looking
    - can be difficult to get started without video tutorials

    Read More...
  • Name: Shishir D.
    Job Title: Senior Analyst
    Length of product use: 1-2 Years
    Used How Often?: Weekly
    Role: User
    Organization Size: 20,000 or More
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Fantastic coding suite

    Date: Jun 15 2021

    Summary: Very positive experience using this. Jupyter is a fantastic coding tool for zero to low experienced coders.

    Positive: Step-by-step casual coders like me dream of such a simple software. I use the tool to execute simple Python scripts and Jupyter is far easier to code on that Visual Studio or even the Python shell itself. Keyboard shortcuts make it even easier to use.

    Negative: Would be better if installation is easier. I had to watch a few YouTube videos to understand how to start installation.

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  • Name: Anonymous (Verified)
    Job Title: Computer Science Engineering student
    Length of product use: Less than 6 months
    Used How Often?: Weekly
    Role: User
    Organization Size: 1,000 - 4,999
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Best notebook for Python

    Edited: Aug 26 2020

    Summary: I use Jupyter for practicing python codes for Machine Learning, and I love it so far. Apart from the initial load time, and the unimpressive interface, it is an excellent tool for writing and debugging code.

    Positive: • It is web-based, so saves you a lot of space.
    • Allows you to import your own code in many formats.
    • You can also work with visuals and charts.
    • I like that you can code line-by-line or all at once with ctrl + Enter shortcut, which is very useful for debugging.
    • Mostly, I like that it is also a great tool for teaching and practicing machine learning.

    Negative: • It gets buggy at times, and takes some time to load initially.
    • Can be slow even on high speed networks.
    • The interface is pretty old fashioned.

    Read More...
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