Firstly, we should know why one should use an IDE(Integrated Development Environment). The reasons behind using an IDE are:
- It is a software environment used to write code. It gives us an ability to use tools like an editor and a compiler together, and it can prove to be a very handy tool while coding.
- IDEs have some added features like code insight is one of the most helpful tools that an IDE can provide. Code insight is the ability for the program to interpret what is typed out. Apart from code insights, an IDE can change the text color to represent different classes, functions, and variables.
- IDE has the ability to debug a program. IDEs will help one to recall a function or method's names, both of one's code and that of external packages.
But when we move to the problem of finding the best IDEs for programming, we should be aware of both the advantages and disadvantages of each IDE so that we can use the proper IDE in a proper way and in the proper place. So let's discuss the 5 best IDEs:
- PyCharm is highly recommended for Python beginners as it makes writing code more efficient, quick, and less prone to errors and typos.
- It offers an enhanced level of code comprehension and readability by means of clear color schemes for keywords, classes, and functions (syntax and error highlighting)
- PyCharm features an in-built Python debugger and integrated unit testing with line-by-line code coverage. It comes with great support for scientific libraries, such as Matplotlib and SciPy which helps a lot in Data Science and Machine Learning.
Note: PyCharm is Memory Intensive and its initial set-up can be time-consuming especially when you are using a slow computer.
Spyder is an open-source cross-platform IDE where Python is pre-installed within the IDE. For programmers who want interactive environments working in the fields of Data Science, engineering, and scientific research, Spyder is the best platform.
- Spyder gives customizable Syntax Highlighting.
- Spyder allows us to run the iPython console and can clear variables automatically.
- This IDE works efficiently in a multi-language editor with a function/class browser, code analysis tool, automatic code completion, etc.
- Breakpoints (debugging and conditional breakpoints) and Interactive execution which allows you to run line, file, cell, etc. are available in Spyder.
- It is specifically designed for data scientists; hence, it integrates well with data science libraries like NumPy.
Note: Spyder is not very customizable and it’s very basic than its counterparts (like PyCharm). Hence, it is recommended for beginners, to use an IDE which provides better options and coding experience.
3. Jupyter Notebook
Jupyter Notebook is an open-sourced web-based application that allows you to create and share documents containing live code, equations, visualizations, and narrative text.
- This notebook not only supports Python but also has support for over 40 programming languages.
- One of the best things of Jupyter Notebook is - it provides a perfect environment for a data science enthusiast who has just started his career in this field.
- This IDE supports markdown and enables you to add variety of HTML components from images to videos.
- This IDE also includes data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and many others.
Note: Jupyter Notebook is complex for running long asynchronous tasks and its installation process is a bit complex while working with remote servers.
The new version of JupyterLab provides a great development environment for Data Scientists. Beyond the beautiful layout, JupiterLab allows you to create your own extensions and add additional features.
- The JupyterLab extension for version control using git developed by the JupyterLab team is a great extension to push/pull/commit your code in your remote Github repo.
- JupyterLab extension shows currently used variables and their values and it provides a tool similar to the variable inspector in RStudio.
- It auto-generates a table of contents in the left area when you have a notebook or markdown document opened. The entries are clickable and you can scroll the document to the heading in question.
Note: JupyterLab does not provide a convenient file explorer view when developing code so, reading and writing files become clumsy. Testing and modularity are sometimes difficult to handle within JupyterLab.
5. VS Code(Virtual Studio Code)
Developed and maintained by the Microsoft, VS Code is one of the most stable IDEs. It has an IntelliSense which enables completion of codes based on variable types, functions, and imported modules.
- Visual Studio Code features a light-weighted fast source code editor, perfect for day-to-day use. With support for hundreds of languages, VS Code helps to be instantly productive with syntax highlighting, bracket-matching, auto-indentation, box-selection, snippets, and more. Intuitive keyboard shortcuts, easy customization, and community-contributed keyboard shortcut mappings allow a user to navigate their code with ease.
- VS Code also integrates with build and scripting tools to perform common tasks making everyday workflows faster. VS Code also has the support for Git.
- Architecturally, Visual Studio Code combines the best of the web, native, and language-specific technologies.
Note: Similar to all other electron apps, VSCode's memory, and battery usage is pretty bad. But overall, we can suggest VS Code is the most stable IDE among all.
There are a lot of other IDEs as well like Visual Studio Community, Pydev, etc. Some programmers also prefer to use basic code editors like Sublime, atom, or notepad++ along with their favorite set of plugins. Hope this article gave you a solid understanding of which IDE to use when and which IDE is good for you.
Have a great day and happy coding!