Jupyter Notebook vs. PyCharm

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Jupyter Notebook
Score 9.4 out of 10
N/A
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…N/A
PyCharm
Score 9.3 out of 10
N/A
PyCharm is an extensive Integrated Development Environment (IDE) for Python developers. Its arsenal includes intelligent code completion, error detection, and rapid problem-solving features, all of which aim to bolster efficiency. The product supports programmers in composing orderly and maintainable code by offering PEP8 checks, testing assistance, intelligent refactorings, and inspections. Moreover, it caters to web development frameworks like Django and Flask by providing framework…
$99
per year per user
Pricing
Jupyter NotebookPyCharm
Editions & Modules
No answers on this topic
For Individuals
$99
per year per user
All Products Pack for Organizations
$249
per year per user
All Products Pack for Individuals
$289
per year per user
For Organizations
$779
per year per user
Offerings
Pricing Offerings
Jupyter NotebookPyCharm
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Jupyter NotebookPyCharm
Features
Jupyter NotebookPyCharm
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Jupyter Notebook
9.0
Ratings
7% above category average
PyCharm
-
Ratings
Connect to Multiple Data Sources10.00 Ratings00 Ratings
Extend Existing Data Sources10.00 Ratings00 Ratings
Automatic Data Format Detection8.50 Ratings00 Ratings
MDM Integration7.40 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Jupyter Notebook
7.0
Ratings
18% below category average
PyCharm
-
Ratings
Visualization6.00 Ratings00 Ratings
Interactive Data Analysis8.00 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Jupyter Notebook
9.5
Ratings
15% above category average
PyCharm
-
Ratings
Interactive Data Cleaning and Enrichment10.00 Ratings00 Ratings
Data Transformations10.00 Ratings00 Ratings
Data Encryption8.50 Ratings00 Ratings
Built-in Processors9.30 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Jupyter Notebook
9.3
Ratings
10% above category average
PyCharm
-
Ratings
Multiple Model Development Languages and Tools10.00 Ratings00 Ratings
Automated Machine Learning9.20 Ratings00 Ratings
Single platform for multiple model development10.00 Ratings00 Ratings
Self-Service Model Delivery8.00 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Jupyter Notebook
10.0
Ratings
16% above category average
PyCharm
-
Ratings
Flexible Model Publishing Options10.00 Ratings00 Ratings
Security, Governance, and Cost Controls10.00 Ratings00 Ratings
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Jupyter NotebookPyCharm
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Score 10.0 out of 10
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Score 10.0 out of 10
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User Ratings
Jupyter NotebookPyCharm
Likelihood to Recommend
10.0
(0 ratings)
8.7
(0 ratings)
Likelihood to Renew
-
(0 ratings)
10.0
(0 ratings)
Usability
10.0
(0 ratings)
8.7
(0 ratings)
Support Rating
9.0
(0 ratings)
8.3
(0 ratings)
User Testimonials
Jupyter NotebookPyCharm
Likelihood to Recommend
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.
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It's easy to create virtual environments and install packages for different projects as we may need project-specific packages for doing our experiments, also it's easy to see what changes we have made and create pull requests faster. But sometimes we want some light python editor like Jupiter notebook as PyCharm is relatively heavier, also Jupiter notebooks are a good option when we need to run remote code on local machines.
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Pros
  • 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
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  • Git integration is really essential as it allows anyone to visually see the local and remote changes, compare revisions without the need for complex commands.
  • Complex debugging tools are basked into the IDE. Controls like break on exception are sometimes very helpful to identify errors quickly.
  • Multiple runtimes - Python, Flask, Django, Docker are native the to IDE. This makes development and debugging and even more seamless.
  • Integrates with Jupyter and Markdown files as well. Side by side rendering and editing makes it simple to develop such files.
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Cons
  • 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.
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  • PyCharm text editor automatically inserts whitespace at the end of each line which can cause issues when doing text comparisons.
  • The package requirement checker and installer does not work well all the time and can be improved
  • Integration with GitLab pipelines can be made better.
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Likelihood to Renew
No answers on this topic
It's perfect for our needs, cuts development time, is really helpful for newbies to understand projects structure
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Usability
Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.
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It's pretty easy to use, but if it's your first time using it, you need time to adapt. Nevertheless, it has a lot of options, and everything is pretty easy to find. The console has a lot of advantages and lets you accelerate your development from the first day.
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Support Rating
I haven't had a need to contact support. However, all required help is out there in public forums.
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I rate 10/10 because I have never needed a direct customer support from the JetBrains so far. Whenever and for whatever kind of problems I came across, I have been able to resolve it within the internet community, simply by Googling because turns out most of the time, it was me who lacked the proper information to use the IDE or simply make the proper configuration. I have never came across a bug in PyCharm either so it deserves 10/10 for overall support
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Alternatives Considered
Jupyter Notebook is unique in that it offers a flexible, lightweight, easy-to-replicate way of organizing your code in a visually intuitive fashion that can be exported in a number of formats. I've found that the broad functionalities available within the notebooks suit a lot of needs I have for EDA, modeling, and data export that makes other software products fairly redundant.
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It is more complete and can handle more projects at the same time. On the other hand, Visual Studio Code has better integration with LMS to help you code. PyCharm allows you to integrate with many external tools and external servers that Visual Studio Code has difficulties with.
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Return on Investment
  • Positive impact: flexible implementation on any OS, for many common software languages
  • Positive impact: straightforward duplication for adaptation of workflows for other projects
  • Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration
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  • Improved efficiency with coding assistance (templates, code completion, documentation), which helps us avoid 'reinventing the wheel' with new projects.
  • Extensive support for other packages/integrations: Docker support to test code, Git repo creation (for version control), and integration with different database systems (Postgres, MySQL).
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ScreenShots