Jupyter Notebook

Jupyter Notebook Reviews

Customer Verified
Top Rated

Do you work for this company? Learn how we help vendors

Ratings and Reviews
(1-25 of 78)

Companies can't remove reviews or game the system. Here's why
Lindsay Veazey | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
I use Jupiter Notebook to organize my workflows into manageable and readable chunks. The ability to streamline my work using markdown descriptions and title is extremely helpful for visually cueing transitions and reorienting the user to the pattern of the workflow outlined in the notebook. It also makes it easier to replicate the workflow for other projects.
  • 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.
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.
Aditya Kumar | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Python is the new technology that is being deployed across organisation for data science. For every language, one must use a user friendly IDE that makes the code more streamlined, easy to understand and with added built in functionalities. Jupyter Notebook takes care of all these and adds a lot of value to the data science projects.
  • Code presentation- with Jupyter Notebook you can deploy codes and markdowns which makes the code easy to read and understand.
  • User interface- the user interface of the Jupyter Notebook is very smooth, there are a lot of easy shortcuts as well the icons to make our work easier
  • Server hosting- with Jupyter Notebook server hosting is very easy which adds to the security feature
  • no code style correction- it doesn't have any functionality to auto correct the code style such as spaces.
  • sometimes while executing the script the system gets frozen.
  • no third party app integration
Jupyter Notebook is the best IDE to use if you are feeling started with the data science. You will notice a lot of tutors use Jupyter Notebook to present their code, the markdown make the code more presentable and can be exported to present. But in case you have to do heavy tasking then it's not advisable to use Jupyter Notebook.
September 27, 2021

Jupyter Pros and Cons

Score 10 out of 10
Vetted Review
Verified User
Review Source
Jupyter notebooks are widely used by our Science and Analytics departments to analyze data, make forecasts, clean/wrangle data, graph visualizations, create machine learning models, and perform a suite of analyses to best understand our business landscape.
  • 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
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.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Jupyter Notebook is a really dynamic tool, currently used by the Engineering team at my company. The main focus of using it is to incorporate python programming while writing code to forecast sales. Jupyter gave me the flexibility of writing code in a line-by-line manner and help me see the output at each step.
  • 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
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.
Kofi Joshua | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
As an organization that deals in data Science projects, we need programming environments that will allow us to work on datasets efficiently, visualize our models, implement our models and edit our models. Data analysis is something relevant to a company and an environment is needed to make it look easier.
  • Jupyter Notebooks are known much for its combination of markdowns and codes which makes easier to read a code
  • Jupyter has an easy navigation platform compared to others.
  • Jupyter makes python programming because of some compelling features like viewing details of bash executions.
  • Jupyter Notebooks can create a 2 minute tutorial sessions for new beginners.
  • Jupyter notebooks can innovate features that will enable programming other languages more efficient.
  • Jupyter Notebooks should help make using version control systems like git very easy.
Jupyter Notebook is well suited in an environment where data analysis and data visualization is mainly important to them and most importantly where they use the python programming language.
It is less appropriate to Jupyter Notebook when you're not in data science as you may not be able to use the full resources and also when you program in multiple languages other than R and python.
Shivam Rai | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Jupyter Notebook is one of the most important applications for my personal use and for the organization purpose also. It helps to develop open source software and for interactive computing across different programing languages. I'm using this since my college time, it supports Julia, Python and R. It allow us to create and share documents that contains live code and graphs.
  • 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.
Ejaz Hussain | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Almost every large-scale enterprise today has started to have in-house ML models & my organization is no different. We have been making use of this wonderful tool for Data visualization purposes, consuming python libraries to get some insights from the larger datasets from different data sources. Also, we use it to train our ML models to make predictions & then use that to further fine-tune the models efficiently.
  • 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.
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.
Nadyan Pscheidt | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
We mainly use Jupyter Notebook mainly in the data analysis and machine learning department, where we test and develop models for our data visualizations. With the Jupyter solution, we can run parts of the code and debug without the need to run all of the code. Also, the commenting feature is great to document the development process.
  • Run only some parts of the code.
  • Document the development process.
  • Organize the code for others to review and understand.
  • Could have cloud saving features like Google Colab.
  • Lack of personalized themes.
For data science development, Jupyter is a top-tier IDE. However, there are some other solutions that can match the quality and in some aspects be better, like Google Colab. With this, you can run your code on the cloud, which already has a lot of packets installed. On the other hand, running Jupyter locally can increase the processing performance, if you need big and fast processing.
Deepak Chhipa | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
As an organization, we are using Python for solving manual task creation by automatically doing this task. So for this we use the RPA technology, but for that we have to scrape the data from the PDF. So we use the Python library from which we read the data which in the form of image then create the CSV table by using the library.
  • Easy to use and handle as it takes less hardware requirements.
  • Helps [in] creating the data visualization.
  • Provide support for difficult language.
  • Needs more code styling.
  • The markdown is limited.
  • For the new user, there [should be] some intro videos or tutorial for that.
I recommend Jupyter Notebook to all [that] are just starting their programming journey and don't want to waste their computer resources. This tool also helps in data visualization, so if you are enthusiastic for the data things then Jupyter Notebook is also a great tool for [you]. Overall, it is a great tool to start the journey.
Score 8 out of 10
Vetted Review
Verified User
Review Source
We are an E-learning company. We use Jupyter Notebook in our lab's virtual environment.
  • 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.
It is well suited everywhere. In web development one cannot use Jupyter Notebook.
Score 9 out of 10
Vetted Review
Verified User
Review Source
We are using Jupyter Notebook for data visualization of JSON and excel data for a banking customer. We are running multiple python scripts on Jupyter Notebook for in-depth data analysis to develop machine learning models.
  • 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
Jupyter Notebook is best suited for scenarios where you have small to medium size JSON/ Excel data. It is not well suited for large amounts of data.
Hemant Jajoo | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
It is used mostly by our Python and R programmers for programming. It is very interactive and easy to manage the programs and present them. Coding in it is very easy and we can also easily debug. We can do many things--change the cell types, use shortcuts, and if stuck somewhere restart the kernel.
  • Interactive
  • Efficient
  • Quick
  • More languages support
  • Enhanced GPU support
  • Multi-environment support
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.
Rodrigo Pérez Romero | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Jupyter Notebook is used by the profiling department and we use it for data analysis and modeling.
  • Great visualizations
  • Easy to edit and track data flows
  • Colaborative working
[Jupyter Notebook is] great for data analysis, [feature engineering,] and data modeling.
Arpit Ranka | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Jupyter Notebook was first used by me for a project for my Big Data Class to handle data in csv, excel, json, etc. I used python and a lot of flexible libraries in it to do data analysis on our banking information like documents, statements, reports etc. and draw visualizations on it.
  • Data Analytics
  • easy visualization
  • ML possibilities
  • slows down device sometimes
  • can't identify syntax errors
  • no collaboration
It's user friendly and easy for beginners to get started on.
Irene Rodríguez Alegre | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Jupyter Notebook is an amazing way to see the code work and plots displayed in an awesome in-line perspective. We really enjoy it especially when plotting data analysis and comparisons. It is used by many of us at my department.
  • 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.
Bayesian estimation and data comparison.
October 24, 2021

A Notebook for All

Score 10 out of 10
Vetted Review
Verified User
Review Source
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 presenting a Proof of Concept to the client, to deploying it on the cloud, teaching new hires about their role, and explaining things to my team. Development is also super easy on Jupyter Notebook with its interactive environment where one can keep codes, comment, and plot images together on a single platform. It's readable and flexible. You can have host server-side also which is great and allows you to access your Notebook from anywhere around the globe. In my organization, Jupyter Notebook is used a lot for various reasons. It's presentable, powerful (not as powerful as IDE though but it has its own purpose), flexible, and most importantly its open source. [For] several years since the boom of Data Scientist fields Jupyter Notebook [has been] getting more and more popular among those who analyze data. I highly recommend Jupyter Notebook because it will make your life's work easy and fun, whether you are a manager who creates presentations for the clients, an engineer who develops code, or a Data Scientist/Analyst, Jupyter Notebook covers 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 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.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Juptyer Notebook provides a great starting point ranging from simple to many complex data science projects. It makes the life of a data scientist easier and keeping all the projects in one place. It allows us the ability to share folders with recipients directly or can serve the file to any programming language in the remote host. It enables cross collaboration. In addition, it gives the ability to easily import different ML libraries, experiment small functions and merge them with larger functions/algorithms. It helps the scientist to organize code with python modules, write tests, leverage standards, and remove dead code.
  • Ease to use
  • Ability to import different ML libraries and very flexible
  • Works in offline mode (no internet connection required)
  • Kernel problem (Stops unexpectedly)
  • Intellisense limitation
  • No built-in data viewer
  • You can run data science, machine learning, and python projects without necessary install of other packages, because many packages like numpy, pandas, scikit learn come with anaconda.
  • Jupyter Notebooks for everything from rapid model prototyping to data wrangling.
  • Simple navigation. Work the end to end process in a notebook
  • Ability to mix text with executable code to illustrate key concepts as well as inserting “Homework” problems
  • Cross browser support & support of graphs
Score 8 out of 10
Vetted Review
Verified User
Review Source
Jupyter Notebook is used widely within our data science groups as a way to experiment with ML and other models. It's great for early stage data analysis as well as for training and sharing of new Python models within the group in a clear way. The interface is easy to use and onboard onto and has been a great way for members to easily share and onboard new associates onto their existing Python analysis scripts.
  • 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.
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
Review Source
Jupyter Notebook is used for interactive and literate programming for Python and R. It is deployed server side, and it is used across the organization to allow users to code through their web browser. It gives users access to Python and R programming, without needing the users to maintain it.
  • 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.
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
Review Source
We currently use Juptyer Notebooks across our organization. Jupyter Notebooks are our go-to experimenting environments for data pre-processing, creation, training and evaluation of machine learning and deep learning models. We also heavily use Jupyter for data visualization and exploratory data analysis. It also provides a great interactive interface which can be used for story telling to our clients and consumers.
  • 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.
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.
May 24, 2021

Glimpse of Jupyter

Score 9 out of 10
Vetted Review
Verified User
Review Source
Jupyter Notebook is a open source IDE used by many organisations and as it run on many platforms and it also runs different coding language. I have personally used Jupyter Notebook with Anaconda and it works very well as our projects are mainly based on Python language and with the help of this IDE we used to run code for data searching, visualization and many other things.
  • It uses containers like Docker which makes deployment easier and simplifies software installation.
  • It provides different kernels which help in programming.
  • Robust.
  • Jupyter is bad for running long asynchronous tasks.
  • Debugging in Jupyter is quite difficult as it doesn't show the error like other IDE.
  • Non-liner workflow.
As Jupyter is an open source IDE, it is well suited for individuals or a small organisations but in the error handling and debugging process there are many other IDE which are good in showing actual errors and helping to debug them.
Score 8 out of 10
Vetted Review
Verified User
Review Source
Jupyter Notebook is being used currently at my organization to handle data in any of the forms including csv, excel, json, etc. We use python and a lot of flexible libraries in it to do data analysis on our banking information like documents, statements, reports etc. and draw visualizations on it.
  • Data Analysis
  • Visualizations
  • Documentation
  • Limited Markdown Styling
  • Complex to handle multiple kernels
  • Difficult python code styling
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 10 out of 10
Vetted Review
Verified User
Review Source
I use Jupyter Notebook to run Python codes for data science and visualization. It is being used by the data analytics department and it allows the team to work on analytics projects and write insightful reports.
  • 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
If you want to start a data analytics project and want to have an organized way to write and execute codes.

Not so appropriate if you want to make codes that are constantly running.
Score 7 out of 10
Vetted Review
Verified User
Review Source
I use Jupyter Notebook to run statistical analysis and develop machine learning models and deploy in production. Jupyter Notebook is widely adopted by data scientists in our organization. It's well integrated with our remote cloud desktop as well as production system. We use Jupyter Notebook to understand customer behaviors and how to improve customer experience.
  • 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.
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
Score 9 out of 10
Vetted Review
Verified User
Review Source
We have been using Jupyter Notebook to learn and implement programming using Python. It provides an interactive data science environment to run programs and study them. It is a simple tool that allows sequential execution of instructions. It makes learning easy. Jupyter is an open-source tool that can export or show data in different file formats.
  • Jupyter Notebook is easy to learn even for an inexperienced programmer.
  • Sequential execution of instructions enables a programmer to learn and have great programming experience and support.
  • Many experts use Jupyter Notebook (through in-built features ) in their courses.
  • Installation and usage is a little more difficult than with other IDEs.
  • Jupyter Notebook is a good open-source editor but not an IDE.
  • As we are writing the code in cells, unintended duplication of data sets is possible.
It is well suited for a well set up programming environment. For an inexperienced user, an IDE remains a better option. It is well suited for execution of instructions in cells. Most experienced users use Jupyter Notebook and deploy it in a cloud environment for convenience of use and uniformity.

Jupyter Notebook Scorecard Summary

Feature Scorecard Summary

Platform Connectivity (4)
83%
8.3
Connect to Multiple Data Sources (26)
84%
8.4
Extend Existing Data Sources (25)
85%
8.5
Automatic Data Format Detection (19)
85%
8.5
MDM Integration (21)
76%
7.6
Data Exploration (2)
91%
9.1
Visualization (25)
92%
9.2
Interactive Data Analysis (25)
90%
9.0
Data Preparation (4)
83%
8.3
Interactive Data Cleaning and Enrichment (25)
87%
8.7
Data Transformations (26)
86%
8.6
Data Encryption (19)
80%
8.0
Built-in Processors (17)
80%
8.0
Platform Data Modeling (4)
87%
8.7
Multiple Model Development Languages and Tools (25)
88%
8.8
Automated Machine Learning (23)
87%
8.7
Single platform for multiple model development (26)
87%
8.7
Self-Service Model Delivery (25)
85%
8.5
Model Deployment (2)
85%
8.5
Flexible Model Publishing Options (23)
87%
8.7
Security, Governance, and Cost Controls (22)
84%
8.4

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 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.

Jupyter Notebook Pricing

Jupyter Notebook Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

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 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.

What is Jupyter Notebook's best feature?

Reviewers rate Visualization highest, with a score of 9.2.

Who uses Jupyter Notebook?

The most common users of Jupyter Notebook are from Enterprises and the Information Technology & Services industry.