How would you feel if you get the freedom to design a layout, write the code, test, debug, build, etc., all at one place?

Isn’t that epic?

That is what an IDE is ready for!

Overview

• For an effortless coding experience, it is crucial to choose an IDE for Data Scientists, Analysts and Programmers.
• Top IDEs any Data Scientists should know.

Introduction

Coding for professional individuals like Data Scientists, Data Analysts, Programmers, is more like a personal experience rather than just some ‘work to be submitted’.

They develop code from scratch keeping an objective in mind, frame it, perform trial and error, and present it, which at last works as the backbone of the project undertaken. Hence a coding environment, or IDE, is very crucial!

Any professional individual whose work includes coding, must use a coding environment or an IDE to work with efficiency and ease. Well, of course a code can be written in various text editors available like - Windows Notepad, but, can you run your code in it? Will it help you identify where exactly you went wrong? Will it help you debugging? Can it provide you with the ease and environment you deserve while you code?

Don’t get disheartened! I have got the right category of products you deserve, one with a plethora of options available, one that keeps being improved to cater your needs - an IDE.

What is an IDE?

IDE stands for Integrated Development Environment. It’s a coding tool that allows you to write, test, and debug your code in an easier way.

The term IDEs is often used for some text editors as well. The main reason for that is, there are some text editors becoming as good as, or, even better than IDEs these days, for example - VS Code, Sublime text, etc.

A good IDE contains a certain amount of intelligence. For example, the IDE can suggest alternatives when you type the incorrect keyword, or it can simply tell you a certain line of code won't work. The more intelligence that an IDE contains, the less hard you have to work to write better code.

Now the question arises which IDE is best for you?

Well, you being the smarter person can only answer this question! While I can provide you all the data and insights that you’ll need to figure that out.

Segmentation

Before diving deep into the details it is always better to have an overview of things to avoid getting lost in between.

To help you with the same, I have tried to segment top IDEs based on certain parameters that everyone keeps in mind before choosing the ideal IDE for oneself.

What is Your Level?

What is Your Environment/OS?

Which hardware do you have?

Note : The above segmentation is based on limited terms and programming experience. Other factors such as previous programming knowledge, budget, teamwork also affects the decision.

Let’s dive in!

• PYCHARM

Pycharm, as the name goes - is a charming IDE for python developed by Jetbrains, the company responsible for one of the most famous Java IDE, the IntelliJ IDEA. There are two versions available - community and professional. It’s among the quickest IDEs to install and simplest to set up, hence preferred by data scientists.

Price - Free/Paid

Features -

1. Provides features like - code editor, errors highlighting and a power debugger.
2. It supports many other programming languages like Node.js, HTML/CSS, and many more.
3. Easily integrates tools like Matplotlib and NumPy, providing ease to those working with data science projects.
4. It consists of an integrated Version Control System that keeps tracks of the changes made to the file.

Download link - https://www.jetbrains.com/pycharm/

• SPYDER

Spyder, or Scientific Python Development Environment is an open sourced python IDE especially designed for data scientists and analysts. It is lightweight and free software available for Data Science and ML. It integrates essential libraries for data science, such as, NumPy, SciPy, Pandas, IPython, etc.

Price - Free

Features -

1. It has text editor with syntax highlighting, code completion and variable exploring.
2. Execution flow of source code can be checked from Spyder GUI (Graphic User Interface) via Spyder debugger.
3. A basic Git version control system to commit or browse directory, files, or entire repository.

Download link - https://www.spyder-ide.org

• THONNY

Thonny is an IDE developed for learning and teaching programming. It was developed at the University of Tartu. It is a useful tools for beginners, as they can step through statements and expressions. The main purpose of Thonny is to give you a good understanding of how python works under the hood.

Price - Free

Features -

1. Thonny supports code completion and highlight syntax errors and provides a simple debugger which can run your program step-by-step.
2. The excellent GUI of Thonny makes the installation of third party packages much easier.
3. While editing a function, a separate windows opens with local variables and the code being shown separately from main window.

Download link - https://thonny.org/

• ATOM

Atom is an excellent choice for data scientists and ML enthusiasts which supports many different languages besides python, such as, C, C++, HTML, Java, etc. It is an open sourced IDE developed by GitHub. The developers call it 'hack-able text editor for 21st Century', you can customize the features and looks of the editor.

Price - Free

Features -

1. It has code collaboration with Teletype.
2. The best package you could install for a data scientist is Hydrogen. It allows you to possess the Jupyter Kernel power on Atom IDE.
3. It provides support for Microsoft SQL server, MySOL, and PostgreSQL.

Download link - https://atom.io/

• JUPYTER NOTEBOOK

Jupyter is a web application which is based on server-client structure, which was introduced in 2014 and is a successor to iPython. It is free, open-source and easy to use. Many data-scientists have worked with Jupyter notebooks at some point or other in their lives because of its functionalities and ease of use. However, it is getting a make-over with next generation JupyterLab launched in 2018.

Price - Free

Features -

1. Using visualization libraries like Matplotlib and Seaborn, the graphs can be plotted in the same document as the code is in.
2. Notebooks made in Jupyter can be downloaded in numerous formats like PDFs, .py files and even as slides for presentations.
3. It provides terminal, text editor, file directory view all in a single work area with a flexible layout.

Download link - https://www.anaconda.com/products/individual

Conclusion

Python IDE is like ice-cream flavour - everyone has their own favourite and none of them fails to impress you with their own unique combinations of characteristics.

Hence, there is no one-fit-for-all IDE that is 'the ideal one' to use, I have tried to deliver you the information that you need to choose your best pal!

My personal favourite is Jupyter notebook, I hope this article helps you to find your favorite IDE too.

References

• https://towardsdatascience.com/
• https://www.kdnuggets.com/2018/11/
• https://www.upgrad.com/blog/
• https://www.analyticsvidhya.com/blog/2020/