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…
Learn from top reviewers
Jupyter feels like you are on moon
Jupyter Notebook: A boon to present codes
Feedback - Jupyter Notebook
A Notebook for All
Jupyter "THE ATTRACTIVE" Notebook
Jupyter Pros and Cons
Flexible, lightweight, and visually intuitive organization of your workflows
Innovative & easy-to-use tool for ML & data visualization
Python programming made easy with Jupyter!
Easy to maintain modularity and write clean and efficient code
Jupyter makes life easier.
Jupyter Notebook - A solid choice for early stage data analysis!
Jupyter Notebook for quick in-depth analysis
Great product for data modeling
Jupyter Notebook is the leading open source tool for interactive and literate coding
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
Pricing
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
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
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Product Demos
H2O TensorFlow Deep Learning Demo
EXPORT JUPYTER to EXCEL | nb2xls | Demo & My Thoughts | Jupyter Notebook to Excel Spreadsheet
Jupyter Notebook using Docker for Data Science (Demo)
Lecture 11, Python Demo for Distribution
Features
Platform Connectivity
Ability to connect to a wide variety of data sources
- 9Connect to Multiple Data Sources(21) Ratings
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
- 9.2Extend Existing Data Sources(20) Ratings
Use R or Python to create custom connectors for any APIs or databases
- 8.5Automatic Data Format Detection(14) Ratings
Automatic detection of data formats and schemas
- 7.4MDM Integration(15) Ratings
Integration with MDM and metadata dictionaries
Data Exploration
Ability to explore data and develop insights
- 9.6Visualization(21) Ratings
The product’s support and tooling for analysis and visualization of data.
- 9.6Interactive Data Analysis(21) Ratings
Ability to analyze data interactively using Python or R Notebooks
Data Preparation
Ability to prepare data for analysis
- 9.3Interactive Data Cleaning and Enrichment(20) Ratings
Access to visual processors for data wrangling
- 8.9Data Transformations(21) Ratings
Use visual tools for standard transformations
- 8.5Data Encryption(14) Ratings
Data encryption to ensure data privacy
- 9.3Built-in Processors(14) Ratings
Library of processors for data quality checks
Platform Data Modeling
Building predictive data models
- 9Multiple Model Development Languages and Tools(20) Ratings
Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.
- 9.2Automated Machine Learning(18) Ratings
Tools to help automate algorithm development
- 9.3Single platform for multiple model development(21) Ratings
Single place to build, validate, deliver, and monitor many different models
- 8Self-Service Model Delivery(20) Ratings
Multiple model delivery modes to comply with existing workflows
Model Deployment
Tools for deploying models into production
- 8.8Flexible Model Publishing Options(19) Ratings
Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.
- 8.7Security, Governance, and Cost Controls(18) Ratings
Built-in controls to mitigate compliance and audit risk with user activity tracking
Product Details
- About
- Tech Details
- FAQs
What is Jupyter Notebook?
Jupyter Notebook Video
Jupyter Notebook Technical Details
Operating Systems | Unspecified |
---|---|
Mobile Application | No |
Frequently Asked Questions
Comparisons
Compare with
Reviews and Ratings
(126)Community Insights
- Business Problems Solved
- Pros
- Cons
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)Jupyter Notebook: A boon to present codes
Feedback - Jupyter Notebook
A Notebook for All
Jupyter "THE ATTRACTIVE" Notebook
Jupyter Pros and Cons
Flexible, lightweight, and visually intuitive organization of your workflows
Innovative & easy-to-use tool for ML & data visualization
Python programming made easy with Jupyter!
Easy to maintain modularity and write clean and efficient code
Jupyter makes life easier.
Jupyter Notebook - A solid choice for early stage data analysis!
Jupyter Notebook for quick in-depth analysis
Great product for data modeling
Jupyter Notebook is the leading open source tool for interactive and literate coding
Best to get started for data scientists
All in one workspace for Data Sciences
Jupyter Notebook for the win!
Jupyter Notebook - For a better data analysis and visualizations
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
Not so appropriate if you want to make codes that are constantly running.
Light user of Jupyter Notebook
1) analyze big data above millions of records
2) develop machine learning codes that can be deployed in production system
1) quick and easy statistical analysis
2) entry level users