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Python Integrated Development Environment (IDEs) for Writing Data Science and Analytics Codes.

Coding is a personal experience for any programmer, since they create something from scratch that acts as the heart of any data science project. Integrated Development Environment brings all the different aspects of writing a code, such as: code editor; compiler/interpreter; and debugger, under the same roof. IDEs make it easier to programme new applications without having to set up different utilities and learn different tools to run a program.

The debugger tool inside IDEs helps us examine variables and inspect codes, which helps in isolating errors. IDEs also have intelligent auto-code completion recommendations that anticipates what a programmer is going to type next, while some provide the capability to unit test a code to ensure it runs in every scenario.

An IDE therefore plays a huge role in programming circles, as you will be using it for writing your Python code for the foreseeable future. That is the reason I saw it important for us to explore the various Python IDEs with their capabilities, in order to familiarize you with the tool. In this blog, we will explore 5 Python IDEs that are popularly used in the analytics and data science industry.

Python IDE 1 JupyterLab

This is an open source web application, based on a server-client structure that serves as a great starting place for data science beginners. Its name is a reference to three core programming languages: Julia, Python, and R that are supported by Jupyter.

Though Jupyter supports over 40 programming languages, it comes bundled with the Anaconda distribution available for Windows, Linux, and macOS. Jupyter notebooks are commonly used because of their functionalities and ease of use. The notebooks that you create with JupyterLab can be downloaded in a variety of formats ranging from pdf to .py files and even as slides for presentations.

A beginner student at writing and testing codes with Jupyter

If you are a beginner and just starting out your machine learning and data science journey like the above student, Jupyter provides an interactive output that allows you to write and then test your codes there and then. Another interesting feature is the fact that you can create great notebooks that contain visualizations and text explanations using the markdown editor which enables you to write Latex equations.

Jupyter brings the terminal, text editor, console, and file directory view all under the same roof in a single work area using a flexible layout. Using the various magic commands and notebook extensions, one can add features like auto-formatting, debugging, autosave, auto code completion, among others. Jupyter has a lot of interesting features that you should not miss out. Visit and you will have an exciting journey as a beginner.

Python IDE 2: Spyder (Scientific Python Development Environment)

It is a lightweight open-source Python IDE, designed exclusively for data scientists and analysts. Spyder has a basic interface as compared to other IDEs, but it has all the necessary components required in a coding environment.

Spyder contains a text editor, file explorer, variable explorer, and IPython console all in a single window. Furthermore, it has built-in integration with several scientific packages such as Pandas, IPython, NumPy, SciPy, among others. It has a code editor which has the capabilities of syntax highlighting, code completion, and real-time code analysis that highlights the potential problem or a syntax error in your code.

The variable editor shows the variables, functions, modules, etc. of the currently selected IPython Console session. It also has an interactive code execution that allows you to execute your code by line, cell block, or run the entire file in a single go. An IPython console in Spyder allows you to test out a few lines of code without wanting to disrupt your primary session

The Static code analysis feature of Spyder detects style issues, bad practices, potential bugs, and other quality problems in your code, without having to actually execute it. This is made possible using the very popular PyLint analyzer. When you are stuck, over an error you cannot solve, a Debugger allows breakpoints and the execution flow to be viewed and controlled right from the Spyder GUI. Our team at will help you understand this lightweight IDE more.

Python IDE 3: PyCharm

PyCharm is a charming Python IDE, most suitable for those developers who are working on a project containing multiple scripts interacting with each other. It is suited for any developer who wants to create software applications in Python, be it web applications, data science applications, or even just a simple Python script.

PyCharm has both a free community version and a paid Professional version, which is available for a free 30-day trial. It is a comfortable IDE to code with, due to its outstanding features like: its editor having syntax and error highlighting; code analysis; quick fixes for instantly improving the code; auto-code generation; auto-indentation; and code folder.

Data analysts having a training on Decision Making analytics at

Whenever a developer wants to start a new project in PyCharm, they can choose from different environments like Virtualenv, Pipenv, or Conda, which help keep dependencies required by different projects separate by creating isolated Python environments for them. PyCharm provides easy navigation capability and allows you to locate any usage for your symbol in the entire project, which is helpful when working on a big web development project with multiple scripts within the same project.

 PyCharm’s bookmarks and TODO capabilities enables one to leave remarks in their code that serve as a reminder to make the necessary amends the next time they navigate through them. It also has a powerful debugger with a graphical interface.

This charming Python IDE has an integrated Version Control System that keeps track of changes made to files and applications, by providing a unified user interface for CVS, Git, Mercurial, Perforce, and Subversion. Plugins can be used to add extra features like a new theme. Furthermore, it has an integrated unit testing where one can observe the results in a graphical manner. PyCharm uses unit test as the test runner but it supports other frameworks too. It has integrated unit testing where one can observe the results in a graphical manner. PyCharm uses unit test as the test runner by default, nonetheless it supports other frameworks too.

 PyCharm has a lot to offer and will help build your confidence in using this IDE  to tackle your development work related to Python applications confidently.  However, this IDE can be resource intensive as it requires plenty of memory and storage space. Moreover, it can be a bit intimidating for a beginner.

Python IDE 4: PyDev

This is an open source Python IDE for Eclipse that can be used in Python, Jython, and Iron Python development.

PyDev provides code completion capability to complete tokens and automatically import its module. On-the-fly code analysis finds errors like unused variables and duplicate signatures. Additionally, it has refactoring capability like renaming variables. The debugger offers capabilities like expression evaluation and the ability to view the variables in the current stack. It also has a remote debugging capability that debugs external programs

Django comes pre-installed with PyDev, which will enable the developer to have a smooth experience developing Django based web-applications. PyDev also provides unit testing capability through the unittest, nosetest or pytest formats available. If you have experience working with Java in Eclipse, then you will have an easier time using the PyDev IDE for Python development purposes. However if you have no experience as there is always the first time, we at will take you through the PyDev Journey.

Python IDE 5: Visual  Studio

Last but not least, we have yet another great open-source Python IDE developed by Microsoft, but only available for macOS and Windows users. It has a free Community version and paid Professional and Enterprise versions. Visual Studio is lightweight and comes with its own marketplace for extensions.

Visual Studio provides support for building Python web applications using Django and Flask, and Data Science applications with built-in Conda and IPython support. It is a unique IDE, as its code editor is guided by the IntelliSense syntax which provides, type hints for functions and classes, auto-completion capability, signature help, quick info tooltip, and code coloring. It also has code snippets for inserting code fragments into your file through shortcuts.

A training session on how to use Visual Studio Python IDE at

Apart from the default PEP-8 style formatting, VS IDE has a host of pre-configured formatting options. The neat refactor in this IDE, provides you with options like extracting methods, adding imports, and removing unused imports. In order to improve your coding standard, Microsoft has integrated PyLint into Visual Studio that checks for errors in Python code. VS also provides an interactive REPL (read-evaluate-print loop) window which lets you enter arbitrary Python code and see immediate results, which is useful when experimenting with a new API or library.

The debugger adds breakpoints to the code and allows the developer to inspect and modify variable values, or insert arbitrary Python expressions then view its result. The richer Python Debug Interactive window can also be used as it provides an interactive REPL experience for debugging

Visual Studio can be used to build web applications using Flask or Django. It’s easy to download these libraries and other dependencies with Visual Studio’s virtual environment support without having to write a single code line. Its however very necessary for you to get trainings  at for you to confidently use VS. This IDE also provides integration with local Git repositories and remote repositories on GitHub and Azure Repos. This enables you to clone a repo, commit changes, and manage branches with these integrations.

Visual Studio will be useful for anyone who wishes to create great applications for devices, cloud, or anything in between, since it allows you to work with other tools like SQL, Unity, .NET and Node.js.

Summing up

Which Python IDE has the best features? Clearly, there is really no general answer to this question since it all depends on one’s needs and requirements. Nevertheless, if you have just started your data science training or you intend to work on individual scripts, then you will have to settle on Jupyter or Spyder.

On the other hand, for the advanced users who are seeking to include more interrelated scripts for larger projects, PyCharm, PyDev, or Visual Studio will be a better choice for them. Nonetheless, I would specifically recommend PyDev for those developers that are familiar with the Eclipse environment.

However, it’s important for a developer to try out different IDEs, before settling on the most comfortable one. Evidently there is no one size fits them all Python IDE since all of them offer great features and plugins that can simplify the development process and enhance the coding experience.

We at are always ready to help both beginners and experts settle on the most comfortable Python IDE, so as to become excellent Data Scientists and Analysts through professional training and coaching.


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