Jupyter Notebook Reviews and Ratings

Rating: 8.6 out of 10

Score

8.6 out of 10

Community insights

TrustRadius Insights for Jupyter Notebook are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

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.

Reviews

23 Reviews

Easy to maintain modularity and write clean and efficient code

Rating: 9 out of 10

Incentivized

May 29, 2021

Use Cases and Deployment Scope

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.

Pros

Interactive
Efficient
Quick

Cons

More languages support
Enhanced GPU support
Multi-environment support

Likelihood to Recommend

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.

Hemant Jajoo

Machine Learning Intern in Product Management at WedVyah (1-10 employees)

Vetted Review

3 years of experience

View profile

Jupyter makes life easier.

Rating: 8 out of 10

Incentivized

May 13, 2021

Use Cases and Deployment Scope

We are an E-learning company. We use Jupyter Notebook in our lab's virtual environment.

Pros

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.

Cons

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.

Likelihood to Recommend

It is well suited everywhere. In web development one cannot use Jupyter Notebook.

Ayushi Jain

Technical Specialist in Professional Services at Skill Up (51-200 employees)

Vetted Review

2 years of experience

Jupyter Notebook - A solid choice for early stage data analysis!

Rating: 8 out of 10

Incentivized

May 13, 2021

Use Cases and Deployment Scope

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.

Pros

Markdown for comments/explanations.
Interactive programming.
Easy to use and share notebooks.

Cons

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.

Likelihood to Recommend

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.

Verified User

Engineer in Research & Development (10,001+ employees)

Vetted Review

4 years of experience

Jupyter Notebook for quick in-depth analysis

Rating: 9 out of 10

Incentivized

May 2, 2021

Use Cases and Deployment Scope

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.

Pros

Easy to learn and use
Data [modeling]
Data analysis and reporting
Predefined visualizations models

Cons

Not much troubleshooting support available
No support for IDE
Deployment is complex for cloud environments

Likelihood to Recommend

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.

Shivani Pandey

Project Engineer in Information Technology at Wipro Limited (10,001+ employees)

Vetted Review

1 year of experience

View profile

Great product for data modeling

Rating: 10 out of 10

Incentivized

May 2, 2021

Use Cases and Deployment Scope

Jupyter Notebook is used by the profiling department and we use it for data analysis and modeling.

Pros

Great visualizations
Easy to edit and track data flows

Cons

Colaborative working

Likelihood to Recommend

[Jupyter Notebook is] great for data analysis, [feature engineering,] and data modeling.

Rodrigo Pérez Romero

Profiling Specialist at Tigo Guatemala (1001-5000 employees)

Vetted Review

2 years of experience

View profile

Jupyter Notebook is the leading open source tool for interactive and literate coding

Rating: 10 out of 10

Incentivized

May 1, 2021

Use Cases and Deployment Scope

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.

Pros

Literate programming
Interactive programming
Server side deployment supporting many users to code via web browser.

Cons

Lacks some features like autoindent code.
Too many different instructions for deployment.
Could have better environment isolation.

Likelihood to Recommend

<div>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. </div><div>Jupyter allows multi-users to access the same environments. Additional environments can be installed via Conda, but this can be complicated.</div><div>Jupyter can be deployed via Kubernetes for example to allow virtualized environments for each user.

</div>

Verified User

Professional in Research & Development (5001-10,000 employees)

Vetted Review

4 years of experience

Best to get started for data scientists

Rating: 9 out of 10

Incentivized

May 1, 2021

Use Cases and Deployment Scope

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.

Pros

Data Analytics
easy visualization
ML possibilities

Cons

slows down device sometimes
can't identify syntax errors
no collaboration

Likelihood to Recommend

It's user friendly and easy for beginners to get started on.

Arpit Ranka

Graduate Student in Information Technology at New York University (201-500 employees)

Vetted Review

1 year of experience

View profile

All in one workspace for Data Sciences

Rating: 9 out of 10

Incentivized

May 1, 2021

Use Cases and Deployment Scope

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.

Pros

Easy and interactive Python environment.
Latex markdown for explanations.
Terminal access through cell itself.
Fast Intellisense.
Documentation access through cell commands.
Intuitive Key Bindings.

Cons

Creating and installing a virtual environment can be tricky.
Conda environment can be granular to work with.

Likelihood to Recommend

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.

Verified User

Employee in Information Technology (1-10 employees)

Vetted Review

4 years of experience

Jupyter Notebook for the win!

Rating: 10 out of 10

Incentivized

April 30, 2021

Use Cases and Deployment Scope

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.

Pros

Data plotting
Easy to switch settings and see the changes right away in the plots.
Easy user interface, and commands.

Cons

None that I can think of.

Likelihood to Recommend

Bayesian estimation and data comparison.

Irene Rodríguez Alegre

Data Scientist - HF Radar in Engineering at CODAR Ocean Sensors, Ltd. (11-50 employees)

Vetted Review

4 years of experience

View profile

Jupyter Notebook - For a better data analysis and visualizations

Rating: 8 out of 10

Incentivized

March 11, 2021

Use Cases and Deployment Scope

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.

Pros

Data Analysis
Visualizations
Documentation

Cons

Limited Markdown Styling
Complex to handle multiple kernels
Difficult python code styling

Likelihood to Recommend

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.

Rahul Kapoor

Software Engineer in Product Management at Fidelity Information Services (5001-10,000 employees)

Vetted Review

1 year of experience

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