Best Python IDE for Data Science To Get You Started

So, which Python IDE is best for data science?

What is the top python IDE for data science and why Python?

Well, python is the programming language of choice for Data Science, Machine Learning and Artificial Intelligence. Apart from this, user-friendliness, simplicity, vast libraries and APIs, compatibility, faster execution and many other features make Python impressive.

Clearly, code editors and IDEs play a very important role in programming. Code editors are used for programming in different languages, but they provide very few features like editing, formatting and syntax highlighting etc. Furthermore, we cannot execute the program in Code editors. In windows, if the program is written in a code editor, a command prompt is needed to run that code. One platform solution for code editors is IDE(Integrated Development Environment).

It should be noted that the best python IDE for data science is somewhat subjective and will depend on your needs, experience level and personality. Above all, carefully consider the advantages and disadvantage of each IDE.

What is an IDE?

First of all, IDE stands for Integrated Development Environment. IDE’s provide a full-fledged development environment for programming, execution, debugging and testing. Without a doubt, it makes a programmers job easier and saves time. Below are some of the features that developers look for in an IDE:

• Code formatting and indentation
• Autocorrection of code
• Code Compilation
• Debugging support
• Version Control
• Light-weight
• Support for integration with external tools
• Code or Syntax Highlighting

Best Python IDEs

Let’s understand various features like OS support, category, price, pros and cons of some most used python IDEs to figure out the best.

1. Jupyter Notebook

Jupyter is an open-source IDE that is widely used by Data Scientist and Machine Learning Engineers across the globe. Jupyter provides supports for over 40 programming languages,  including Python. Jupyter Notebooks offers easily sharable feature as well as can be downloaded as HTML, Pdf, python, LaTeX, and other custom types.

Furthermore, since Jupyter is a web-based IDE it is easy to run from local or cloud machines. In closing, code formatting, editing and highlighting of syntax is easy with Jupyter.

Category – Web-based IDE

Platform Compatibility – Linux, macOS, Windows

First Release Date – February 2015

Official Website – https://jupyter.org/

Installation link– https://jupyter.org/install.html

Price – Free

Browser Compatibility – Chrome, Firefox, Safari

Screenshot :

Advantages:

• The user-friendly interface makes it easy to use.
• Notebooks can be shared easily.
• The output of code snippets are very interactive
• Notebooks can be saved as HTML, PDF and many other formats
• Perfect for quick data analysis and data visualizations.
• Integrated data science libraries (matplotlib, NumPy, Pandas)

Disadvantages:

• Might be difficult for the beginners initially.
• The Tensorflow and GPU compatibility setup is tricky and complicated.

2. PyCharm

PyCharm is a specialized IDE for Python. In particular, it is designed by programmers for programmers as to make the programmers jobs easier. In addition, it provides all the needed tools and a productive environment for Data Scientists.

Furthermore, it provides formatting, editing, compilation and debugging features. Lastly, custom plugins can be created using PyCharm’s API and it has both free and full-featured paid versions.

Category – IDE

Platform Compatibility – Linux, macOS, Windows

First Release Date – 2010

Official Website – https://www.jetbrains.com/pycharm/

Installation link– https://www.jetbrains.com/pycharm/download/

Price – Free Version with limited features and paid full-featured professional version

Screenshot:

Advantages:

• Quickly to install and setup
• Easy to use for beginners as well
• Edit, run and debug code easily
• Integrates with Python libraries very well
• Supports version control feature
• Highly active community support

Disadvantages:

• Sometimes, PyCharm is slow to load
• Need to change default settings while working with existing projects

3. Spyder

Spyder is Scientific Python Development Environment, an open-source IDE designed by and for scientists, engineers and data analysts. It provides editing, automatic code completion, debugging, and plotting functionalities. Moreover, data exploration, interactive code execution, deep introspection, and beautiful visualization capabilities make it a complete development tool.

Category –IDE

Platform Compatibility – Linux, macOS, Windows

First Release Date – 2009

Official Website – https://www.spyder-ide.org/

Installation link– https://www.spyder-ide.org/

Price – Free

Screenshot:

Advantages:

• Easy to use, run and debug interface
• Beautiful visualization capabilities
• Interactive console for introspection
• Active community support
• Third-party plugin support
• Works well for data science projects

Disadvantages:

• Set-up and execution is challenging for first-time users
• Not much support for non-data science projects

4. Visual Studio code

Visual Studio Code is developed by Microsoft and a cross-platform code editor that is highly customizable and allows development in several programming languages, including Python, C/C++, C#, HTML, Java, Javascript etc.

Visual Studio Code makes it easier to work with Git and other SCM providers. Additionally, it offers an option to install extensions to add new languages, themes, debuggers, and additional services.

Category – IDE

Platform Compatibility – Linux, macOS, Windows

First Release Date – 2015

Official Website – https://code.visualstudio.com/

Installation link– https://code.visualstudio.com/

Price – Free

Screenshot:

Advantages:

• Built-in Git feature and other SCM support
• Smart autocompletion of code
• An interactive console to make debugging enjoyable with breakpoints and call stacks
• Visual Studio Code is lightweight and comes with very powerful features that only some of the paid IDEs offer

5. Atom

Atom is a cross-platform code editor developed by GitHub. It supports approx. 34 programming language including Python, HTML and Java. Additionally, it is an open-source code editor which offers a fast, smart and flexible code autocomplete feature.

Furthermore, its interface can be split into multiple panes to compare and edit code across files. Additionally, it it pre-installed with four different user interfaces and eight syntax themes in various dark and light colors. Finally, Atom offers over 8000 features and user experience-extending packages which are customizable.

Category – Code Editor

Platform Compatibility – Linux, macOS, Windows

First Release Date – 2014

Official Website – https://atom.io/

Installation link– https://atom.io/

Price – Free

Screenshot:

Advantages:

• Light-weight code editor with over 8000 features
• Interactive community support
• Easy integration with Git
• Multiple projects can be managed easily
• Easy to customize and style look and feel of Atom UI

Disadvantages:

• RAM usage has increased with recent updates
• Some tweaking required in settings before use
• Might experience performance issues on older CPUs
• Suffers from migration issues

 

6. Eclipse + Pydev

Eclipse is one of the top IDEs available and is primarily developed for Java programming. Over time Eclipse has added support to allow for additional programming languages, including Python. PyDev is a Python IDE for Eclipse, which is used for Python development by Data Scientists and Machine Learning Engineers.

Pydev can be installed as a plugin in an existing Eclipse installation or as a stand-alone install as LiClipse. Apart from an interactive console, it offers many goodies such as code analysis, syntax highlighting, remote debugging and auto-completion of code using auto-import.

Unquestionably, Eclipse PyDev is a stable IDE that provides good performance for the setup python project life cycle.

Category – IDE

Platform Compatibility – Linux, macOS, Windows

First Release Date – Eclipse 2001, Pydev-2003

Official Website – https://www.pydev.org/

Installation link– https://www.pydev.org/download.html

Price – Free

Screenshot:

Advantages:

• Supports Django, Pylint, and unit test integration
• Interactive console
• Syntax highlighting and auto-completion of code with auto import
• Remote debugging feature
• Programmer friendly and easy to use.
• A very stable IDE

Disadvantages:

• A complex user interface makes it difficult to work with for first-timers.
• Set-up of Eclipse is challenging for beginners

 

7. IDLE

IDLE is Integrated Development and Learning Environment. It is a default IDE bundled with Python for more than 20 years. Apart from this, it is a cross-platform IDE and offers a basic set of IDE features that keep it light-weighted. Finally, it is a python shell window that offers colorized input, output code and colored error messages.

Category –IDE

Platform Compatibility – Linux, macOS, Windows

First Release Date – 1998

Official Website – https://docs.python.org/3/library/idle.html

Installation link– https://www.python.org/downloads/

Price – Free

Screenshot:

Advantages:

• Best for beginners level developers
• Python shell window provides python code colorizing feature
• Interactive interpreter with a multi-window text editor
• Supports code formatting and autocompletion of code
• Support easy code debugging

Disadvantages:

• Lack of advanced IDE features
• Not suitable for high-level and complex projects.

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Summary – How to Choose the Best Python IDE for Data Science?

Ultimately, only you can decide which IDE suits you. Some basic recommendations will help you choose the IDE that works best for you:

 

• Based on programmer knowledge: For new Python developers, it is best to choose an IDE with less customization. Obviously, a less complex IDE makes things less complex for beginners. On the other hand, for an intermediate or advanced level programmer, feel free to switch to a fully-featured IDE.

 

• Previous IDE experience: If a programmer is familiar with an IDE like Eclipse or Visual Studio Code for any language, it’s better to add Python plugins or toolset to existing IDE for Data Science projects rather than opting for a new unknown one.

 

• Hardware Basis: Choose your IDE based on the hardware of your system. Besides, you need to check hardware compatibility and support for IDE before opting for one.

 

• Price: Choose a paid IDE only if you can afford it and need paid features.

 

• Experience: If you only have experience with text editors, it’s better to choose one of the code editor solutions or a simple IDE.

 

Above all, choose your IDE wisely. Without a doubt, your enthusiasm may wane if you pick the wrong one.

With our elaborate comparison of these widely used IDE’s, you should be able to choose the perfect IDE for yourself.

Author: Nidhi Bansal

Nidhi is a Data Scientist in both Python and R. She has been working on various machine learning algorithms for about 3 years. Worked on Natural Language Processing(BOW, TF-IDF, W2Vec, Glove Embeddings), LSTMs and various Computer Vision techniques in Python like Faster R-CNN, Mask-RCNN.