Coding in the python shell for simple tasks is easy, but it becomes a mess when you work with large projects. Using an IDE makes coding easier and more fun. IDE is an Integrated Development Environment, which provides all the necessary tools and interfaces for easy and efficient code development & code editing. A Python IDE is a specially designed environment for python development. One can easily write, test, debug, analyze, highlight or auto-complete your code seamlessly, using IDEs. Here, we shall look at the Best 10 Python IDEs to learn Data Science, based on the developer’s purpose or end goal.
Any fool can write code that a computer can understand. Good programmers write code that humans can understand.Anonymous
Think of a text editor, like Windows notepad, where you are coding. You must execute your code once it has been completed. In a text editor like this, you can’t run your program; instead, you must issue a prompt command. Wouldn’t it be better to have everything in one location rather than using two different programs? python IDEs are designed for that.
Here, we will help you understand and simplify the numerous IDEs options available for data science with python. We will outline the different options, and their pros and cons to aid your decision-making in choosing what’s best for your goals & process.
Table of contents
- Visual Studio Code
Confused after seeing a long list of the Best 10 python IDEs? Do not worry. We shall now dive into each one of the above and help you to make a better choice.
If you do not know using IDEs or installing code editors, there’s no need to worry about that part at all. If you’ve recently installed python on your system, then you’ll find a new application called IDLE installed in it. You may not have installed it manually, but it comes bundled along with python installation.
The default one.
IDLE is present to make your python coding journey easier, & to instantly start running code in that environment.
Still wondering, what is IDLE exactly?
Well, let us tell you!
IDLE (Integrated Development and Learning Environment) is a free, beginner-level editor that comes as a standard with Python. Python installations for Windows and Mac include Python IDLE.
Python IDLE should be easy to find and download if you run Linux thanks to your package manager. After installation, you can use Python IDLE as a file editor or an interactive interpreter. Python IDLE, which launches a Python shell as soon as it is opened, helps streamline your productivity.
It provides an interface with smart indentation & basic debugging.
However, the default one comes with its own set of cons. It’s limited to only a basic level of debugging, & makes accessibility a hard task. It has too simple functionality and gives runtime errors. This is not ideal for all developers, but maybe can be used at a beginner stage, to get an understanding of the basics of python for data science implementations.
✅Pros: Smart Indentation, Interactive UI, Searching multiple files
❌Cons: Accessibility, Basic Debugging, Runtime errors, too simplistic
You can download it from here: IDLE — Python 3.10.5 documentation
Are you new to python programming?
All you aim for is to get a good understanding of the programming, rather than all the cool additional features that IDEs have to offer. That’s why Thonny will be the right start for you.
This is The IDE for newbies.
Thonny is not like the other complex IDEs. It’s designed for newbies who want to understand the syntax & functionality of programming in the codes. Thonny supports this through running syntaxes with an internal code debugger, & hence proves itself to be very beginner-friendly.
Thonny IDE is ideal for beginners to teach or learn python programming. It supports different ways of stepping through the code, step-by-step expression evaluation, detailed visualization of the call stack, and a mode for explaining the concepts of references and heap.
Its simple interface with the internal debugger is supported by a feature called Variables view, where each variable & operation can be expanded to understand the functionality in-depth.
As discussed above, Thonny is only aimed at beginner developers & not useful for any kind of advanced development.
✅Pros: Simple interface, debugger, syntax error detection, variables view
❌Cons: Not for advanced development, too basic
You can download it from here: Thonny, Python IDE for beginners
If you are a Linux user we are pretty sure that you have heard about vim.
Well, This is the community-managed editor for Linux distributions mainly.
Community-developed text editor with a unique mode of operation for customizable python IDE usage. According to the Vim ethos, programmers are more productive when they keep their hands firmly on the keyboard. The developer’s thoughts should naturally translate into lines of code that appear on the screen.
Vim is another basic editor which is mainly focused on basic code editing, enabling a decent user experience on an interface that has strong community support. Developers who work with various file formats or operating systems can efficiently leverage this platform for its processing capabilities.
An important point to note here is that the Vim editor doesn’t have any cutting-edge features or extensions for efficient/advanced project implementations or coding capabilities.
In search of an editor with high customization capabilities & configurations for your workflow?
✅Pros: Feature-rich, Community support, Great User experience, file format handling
❌Cons: Not enough cutting-edge/unique features
You can download it from here: welcome home: vim online
Being a developer, you will have your most-used or needed workflow components or shortcuts while writing the code or designing the workflows. This requires an interface to efficiently input your customized user commands & modernize your workflow according to your needs.
4. Sublime Text
Sublime Text allows for this execution of customization, working through multiple extensions for project management capabilities.
It’s a highly modifiable, fast & popular code editor for python code editing. The code editor is well-known for its quickness, usability, and robust community support. Although it’s a fantastic editor out of the box, its true power comes from the ability to extend its capabilities using Package Control and setting up unique configurations.
Major flaws of Sublime text editor are the bad integration capabilities & its inability to handle multiple functionalities simultaneously. This can affect the projects and workflows from a macro view.
✅Pros: Custom User commands, efficient project management, extensions & packages, syntax highlighting.
❌Cons: Bad Integration, Inefficient multi-functionality or handling.
You can download it from here: Download – Sublime Text
If you’re a java-friendly coder who’s willing to utilize python programming for domains majorly focused on web development, then PyDev is the right platform for you.
The Eclipse extension for python.
PyDev is a plugin for the Eclipse IDE, majorly known for smooth integration with the Django framework and WebDev functionality, with good code debugging, and analysis. If you struggle with the simplicity & design of code, then you can leverage the code refactoring capability of PyDev.
Most preferred open-source IDE by the developers distributed as a third-party plugin for Eclipse IDE. It employs sophisticated type inference techniques, enabling features like code analysis and code completion in addition to a debugger, interactive terminal, refactoring, tokens browser, and integration with Django, among other things.
PyDev, however, consumes a lot of memory & provides slow run-times for execution.
✅Pros: Django integration, Good Python Web Development, Refactoring, debugging, code analysis
❌Cons: Slow with plugins, Memory consumption
You can download it from here: PyDev
What if you need to design & style a webpage with customized aesthetics & make it visually appealing. You can use python programming, but it’s hard to implement these workflows of web pages on all platforms, as they don’t support any extensions or customizable features, due to insufficient packages.
This is a truly integrated environment for development, with all pre-installed packages.
Atom IDE comes into the limelight here as it solves the above problem, by allowing coders to integrate programming/coding along with styling the web page as you wish, to make it visually appealing for your clients. Plugins & integrations for building applications are its core strengths.
Atom comes with multi-language support for coders with a smooth UI, & hence provides a wide range of additional/pre-installed packages for development. Being a developer, you can efficiently modernize your workflow with this IDEs support.
For Data Science developers/enthusiasts, you can use Atom in combination with Hydrogen, a package that lets you run your code directly in Atom using any Jupyter kernels you have installed.
Though there are numerous plugins available for customized development, many users often have to bear the curse of latency issues in the interface. This may be due to the poor structural capabilities of Atom.
✅Pros: Interface customization, syntax highlighting, and auto-completion, plugins
You can download it from here: Atom IDE
Looking for an interface that’s easy to navigate & beginner-friendly?
Imagine you have to present a market analysis statistics report to a client, but the client is non-technical & it’s hard for him to make sense of all the tables & numbers.
This is where Jupyter plays the savior. The most effective solution for this problem is to visualize the data at hand, & there’s no better tool than Jupiter notebooks, for visualizations.
Jupyter notebooks are quite handy & they open quickly and provide output equally quickly. They make data analysis, data cleaning, and plot development easier and faster.
It offers you a user-friendly, interactive data science environment for a variety of programming languages that may be used as a presentation or educational tool in addition to serving as an IDE & is ideal for those who are just getting started with data science.
It provides a great user experience – allowing developers to combine texts, codes, and visuals. You can access a large repository of Data Science libraries for computations & visualizations.
However, it has its major issues like bad code versioning & hard integration. It makes it difficult for developers to test in the environment & integrate the notebooks with other tools seamlessly.
✅Pros: Great user experience (text+code+visuals), Data Science libraries, ML workflow, Numerical computations
❌Cons: Bad code versioning, No integration, hard-to-test
You can download it from here: Project Jupyter | Home
As a data scientist, just having an integrated environment for development is not the only thing you would look for, but also having an interactive environment with easier coding abilities & quickly identifying errors, is equally important in domains such as engineering, scientific research & Data science.
Here’s the tool with multiple functionalities and great community support & documentation.
Spyder has important use cases when there are possibilities of unknown errors in your code, as its features allow you to identify or spot these errors in red.
Spyder is a commonly used IDE for scientific development, with Anaconda distribution. Spyder’s code execution is fluid, which develops a brand-new debugging ability. It may also be utilized to create an analysis based on data and for the goal of data analysis.
Hence the Spyder IDE allows us to easily complete code, that too without any errors, and easily leverage the libraries & integration capabilities that it provides.
A predominant liability of using this could be that there are a few additional dependencies to be installed for the execution/functionality in this interface.
✅Pros: Multiple charts, Data Science libraries integration, Auto Code completion, Community support, Documentation
❌Cons: Dependencies for execution
You can download it from here: Home — Spyder IDE (spyder-ide.org)
Being a developer is not an easy job. You have to juggle between multiple scripts & versions of codes while working on a single project. An ideal tool for you would be an IDE which can ease this process of projects & workflow through various features for accessibility & efficiency.
Pycharm is an IDE that has multiple features for ease of workflow & version control. You can use the ‘split’ feature in PyCharm and get an efficient outlook view of multiple tabs or segments, through which you can simultaneously access multiple scripts or code segment versions.
JetBrains’ extremely popular IDE was designed to make handling big Python development projects easier. By avoiding often navigating between windows, PyCharm allows you to manage your version control system, connect to a database, access the command line, and create virtual environments.
✅Pros: Multi-script integration, efficient refactoring, smart code navigation, smart fill & accessibility, plugins
❌Cons: More Space, Expensive professional version, Basic support in the community version
You can download it from here: PyCharm: the Python IDE for Professional Developers by JetBrains
Want a powerful, feature-rich platform to manage your data science projects?
You are a professional developer & you are put to work on a new project along with another developer. You both are based out in two different remote places. Now you both need to remotely implement this large project.
Visual Studio Code
Visual studio code is the best & most feature-rich platform that enables developers to implement large-scale projects seamlessly in a most efficient manner. It has numerous configurations, customizations, capabilities & features for the development of code. It even comes with a live co-programming feature that allows developers to remotely work on the same code in the same environment simultaneously
Visual Studio Code is Microsoft’s free, open-source IDE which is lightweight & powerful. Most widely used IDE for developers across the world. The python addon makes use of all of VS Code’s capabilities to offer linting, debugging, unit testing, auto-completion, IntelliSense, and the ability to quickly transition between different Python contexts, such as virtual and Conda environments.
After leveraging all the functional enhancements on the platform, developers can optimize their code completion & easily debug it. The codes can be versioned and maintained & workflows can be easily seamlessly integrated into Git.
The only liability to watch out for is that this is a very heavy application, with chances of glitches. This is due to extreme support/plugins/features. Avoid this only if your hardware isn’t compatible with the software, else this will be the best choice for professional code development in the data science domain.
✅Pros: Integrating Git, optimizing code completion, internal debugging, and useful enhancements
❌Cons: Heavy application, Unexpected freezes, Glitches
You can download it from here: Visual Studio Code – Code Editing. Redefined
Time to decide…
Now that you have seen and understood the best 10 Python IDEs to learn Data Science projects, you’re also now aware of various advantages/disadvantages of each of these best 10 python IDEs to learn data science.
Now, it’s your turn to decide upon what IDE to use, based on your level of knowledge, the end goals, and the purpose/intent of usage. Take the right choice that serves you well, & equip yourself with the best tools/interface for efficient & seamless code developments and project implementations. Using these modern tools will enable a thriving environment for development and integration.
If you are a beginner and looking for training come internship opportunity in Data Science, you can visit the following link: