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…
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
Popular Features
- Interactive Data Analysis (21)9.696%
- Visualization (21)9.696%
- Connect to Multiple Data Sources (21)9.090%
- Data Transformations (21)8.989%
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
- 9.2Single 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 |
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Mobile Application | No |
Frequently Asked Questions
Comparisons
Compare with
Reviews and Ratings
(124)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.
Attribute Ratings
Reviews
(1-22 of 22)Jupyter feels like you are on moon
- Easy to use
- Easy to understand
- Faster
- User Interface can be improved
- More features in online Jupyter lab can be added
- Good & free documentation
Jupyter Notebook: A boon to present codes
- 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
- could have some standard python libraries imported already
- compiled images and results should be alterable in size
- compiler runtime
Feedback - Jupyter Notebook
- Great interface.
- Segments for codes.
- Markdown for code explanation.
- Sometimes I have to restart it when I import any new python library.
- Should be available for more languages.
A Notebook for 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
- Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
- Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.
Jupyter "THE ATTRACTIVE" Notebook
- We can use it as a notebook and share the slide and also publish it online through GitHub.
- Attractive programming environment.
- Easy navigation platform.
- Should work on Configuration setup, it takes a lot of time.
- Work on code styling correction, sometimes it makes a major difference.
- There is no IDE integration.
- Should include more programming language.
Jupyter Pros and Cons
- Sharing/showcasing work in a step by step manner
- Exploratory data analysis/viewing code in-line
- Data exploration/visualization
- Switch between different coding languages
- No IDE integration/linting
- No testing integrations
- Difficult to view changes in GitHub
- Notebook harder to productionize than scripts
- Visually intuitive organization of code.
- Static (but changeable) display of function outputs.
- Easy replication of notebooks or into new notebooks, or into PDFs.
- I'd like to see bookmarks made available for easier scrolling through long notebooks.
- A dark mode option would be helpful, too.
- I wish that the display of graphics would be a little bit more customizable as a native function of Jupyter.
- User-friendly UI.
- Easy to debug at each code line.
- Great support for Python Math libraries.
- Advanced data visualization capabilities.
- Notebook sharing feature.
- Intellisense not up to the mark.
- Limited collaboration scope.
- No IDE integration supported.
- Can become sluggish at times when datasets are huge.
Python programming made easy with Jupyter!
- 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.
- The ability to use night mode background.
- The ability to select multiple lines of code to run and see the output.
- The ability to open .py extension file as well along with .ipynb
- Interactive
- Efficient
- Quick
- More languages support
- Enhanced GPU support
- Multi-environment support
Jupyter makes life easier.
- 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.
- Sometimes we face issues in executing the code because Jupyter Notebook hangs.
- Auto save option is not there which sometimes creates issues in saving your work.
- Markdown for comments/explanations.
- Interactive programming.
- Easy to use and share notebooks.
- Doesn't have some features that competitors have.
- Difficult to do direct collaboration on the same notebook.
- Doesn't provide great code style support/corrections.
Jupyter Notebook for quick in-depth analysis
- Easy to learn and use
- Data [modeling]
- Data analysis and reporting
- Predefined visualizations models
- Not much troubleshooting support available
- No support for IDE
- Deployment is complex for cloud environments
Great product for data modeling
- Great visualizations
- Easy to edit and track data flows
- Colaborative working
- Literate programming
- Interactive programming
- Server side deployment supporting many users to code via web browser.
- Lacks some features like autoindent code.
- Too many different instructions for deployment.
- Could have better environment isolation.
Best to get started for data scientists
- Data Analytics
- easy visualization
- ML possibilities
- slows down device sometimes
- can't identify syntax errors
- no collaboration
All in one workspace for Data Sciences
- Easy and interactive Python environment.
- Latex markdown for explanations.
- Terminal access through cell itself.
- Fast Intellisense.
- Documentation access through cell commands.
- Intuitive Key Bindings.
- Creating and installing a virtual environment can be tricky.
- Conda environment can be granular to work with.
Jupyter Notebook for the win!
- Data plotting
- Easy to switch settings and see the changes right away in the plots.
- Easy user interface, and commands.
- None that I can think of.
- Data Analysis
- Visualizations
- Documentation
- Limited Markdown Styling
- Complex to handle multiple kernels
- Difficult python code styling
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
- Python
- Markdown presentation
- Easy to learn
- Good organization
- Can be more visually appealing to users
- Easier ways to link projects to websites like Wix
- Ability to work collaboratively on the platform
Not so appropriate if you want to make codes that are constantly running.
Light user of Jupyter Notebook
- Big data analysis on cloud desktop.
- Exploratory analysis.
- Common machine learning models.
- Nicer output format for explanatory analysis.
- Easy update on packages.
- Better compatibility with AWS tools.
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
Jupyter or nothing
- 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
- Would like to see syntax errors highlighted while coding
- PDF integration should get better to print notebook results