PyCharm and Jupyter are two very popular environments among Python developers and data scientists. Both these environments offer their own set of advantages. But which between the two should a data scientist pick?
Both PyCharm and Jupyter have their advantages in data science. Jupyter is more suitable as a prototyping tool for prototyping models and doing a quick analysis of data. PyCharm is generally suitable for building complex multi-layered applications that can analyze large data sets.
In this article, we will explore this subject in detail. We will start off by looking at the key features of each of these platforms. Next, we will differentiate between them and then finally discuss which of them is the better choice for data science.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
PyCharm: Key Features
PyCharm is a Python IDE developed by JetBrains. It is one of the most popular Python IDEs among developers, and there are several reasons behind this.
First and foremost, PyCharm has one of the best code editors among all the Python IDEs. It features all the basic features like code analysis, quick fixes, syntax/error highlighting as well as additional features like code folder, auto-code generation, auto-indentation, etc. Navigating across your codebase is also incredibly easy with PyCharm thanks to features like an easy location for the usage of a symbol.
Additional features include a powerful debugger that comes with a graphical interface as well as integrated unit testing capability that presents the results graphically. A cool version control system is integrated within PyCharm, and it provides a unified interface for Git, CVS, Mercurial, Subversion, and Perforce. Other features include bookmarks and To-Dos that help keep track of your progress.
PyCharm is available in two different versions: The Community Edition and the Professional Edition. The Community Edition is free and open-source, whereas the Professional Edition is a paid version. You can try the Professional Edition first as part of the 30-day free trial before paying for it. You will have to buy a licence after that period.
Both versions offer features such as an amazing Python editor, graphical debugger and test runner, navigation and refactorings, code inspections, and VCS support. In addition to these, the Professional Edition also features scientific tools, web development features, Python web frameworks, Python profiler, remote development capabilities, and database & SQL support.
Jupyter: Key Features
Jupyter is another incredibly popular Python IDE. As a web application, Jupyter stands out from most other IDEs. It operates on a server-client structure, and you can use it directly from your web browser without the need to install anything. The name “Jupyter” refers to the three most popular programming languages supported by this platform: Julia, Python, and R.
Project Jupyter offers several different applications. For Python, the classic Jupyter Notebook is the more established application. Another Python application, JupyterLab, was launched in 2018 with a special focus on data science.
One of the coolest things about Jupyter is that it is incredibly easy to use for beginners. This is because it allows you to both write your code and then test it in the same interface. But this doesn’t mean Jupyter’s usability is limited to beginners.
One cool feature that even experts find helpful is the creation of notebooks, with explanations and visualizations. These notebooks can later be downloaded as pdf files, ‘.py’ files, or in other formats.
The interface is extremely intuitive. It features the terminal, text editor, file directory, and console all in a single layout. The interface can also be customized using features such as magic commands or notebook extensions.
A Zen mode is available to minimize the interface by removing as many distractions as possible. You can also choose to add a range of other features like autosave, auto-format, debugging, etc.
PyCharm vs. Jupyter: Key Differences
Now that we’ve looked at the key features of both PyCharm and Jupyter, let us compare these IDEs by analyzing their features side by side:
| PyCharm | Jupyter |
|---|---|
| PyCharm is a desktop IDE, primarily for Python. The paid Professional Edition also supports JavaScript, CoffeeScript, TypeScript, and CSS, among others. | Jupyter is a range of web applications for various languages, including Julia, Python, and R, among others. Rather than an IDE, these applications are often classified as a ‘Multi-Language Interactive Computing Environments.’ |
| The startup can be slower compared to Jupyter. | The startup is faster compared to PyCharm. |
| Integrated tools in PyCharm include Python, Django, Wakatime, Anaconda, etc. | Integrated tools in Jupyter include Python, GitHub, Dropbox, Scala, TensorFlow, etc. |
| It comes with smart-auto completion and intelligent code analysis. | It comes with support for in-line graphing and in-line code execution (using blocks). |
| It is available in two versions. The free and open-source Community Edition comes with a restricted feature set. The paid Professional Edition can be tried for free for a 30-day trial period. | Jupyter is an open-source tool that is free to use. |
| The Professional Edition of PyCharm is extremely popular among developers. It has been mentioned in 372 company stacks & 573 developer stacks on GitHub. | Jupyter is less popular among developers, having been mentioned only in 76 company stacks & 40 developer stacks on GitHub. It is, however, extremely well suited for data science. |
Which One Should You Use for Data Science?
Both PyCharm and Jupyter have their own set of advantages.
In short, Jupyter is the more suitable option when you have quick data processing and visualization tasks at hand. In other words, Jupyter can be great as a rapid prototyping and visualization tool.
On the other hand, PyCharm is more suited for complex data processing. Think about scenarios where you’re dealing with large and dynamic data sets that require long term analysis. You will want to develop a complex application for this, and PyCharm is much better at handling this.
As a professional data scientist, there will be plenty of instances where you’ll have to build a complex data analytics tool. There will also be plenty of instances where you’ll be required to do a quick analysis and data visualization. Thus both Jupyter and PyCharm will have their use cases. So it will help your career as a data scientist to learn and master both these tools.
If you are looking to work as an entry-level data scientist or you’re a student of data science, you can go ahead and start off by learning Jupyter. Jupyter offers interactive outputs, meaning you can write your code and then test it within the same interface. This is a very helpful feature for beginners. The overall interface is also easier to work with, featuring all the tools you will need as a data scientist under the same work environment.
Once you become a more established data scientist, you can start using PyCharm as well. Like we’ve mentioned previously, as a professional, you will be using both of these environments.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
There will be instances in a data scientist’s career where both PyCharm and Jypyter will have their advantages over the other. PyCharm is the IDE of choice for a majority of Python developers. As such, it is specifically advantageous for a data scientist looking to build a dynamic data analysis tool or application that can work with a large data set.
Jupyter is much more suitable for most of the basic prototyping of models and analysis of small data sets that a data scientist will be doing throughout his/her career. Furthermore, it offers the option to embed the code, text, and visualization graphics into web pages or share them as notebooks.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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