12 Best Python IDE For Python Programming

Being a programmer, you are probably familiar with Python. Those who use Python know how frustrating the tools can get to work on large programming projects. You can use the default IDLE or Python shell for most of your work. But, a good IDE will make your coding experience fun. In this article, we will tell you about the top 12 best Python IDEs as a way to make your coding journey hassle-free. But first, let's start with:

Key Features to Consider When Choosing a Python IDE

Ease of Use: Consider if the IDE is beginner-friendly or more suited for experienced developers.
Language Support: Does the IDE support other languages or just Python?
Debugging and Testing: Look for strong debugging and testing tools to improve productivity.
Customization: Can you customize the IDE to fit your workflow with plugins and extensions?

IDE vs Code Editor

IDE (Integrated Development Environment): Provides a comprehensive environment with tools for coding, debugging, testing, and deployment.
Code Editor: Lighter weight with fewer features, focused on writing and editing code.

1. IDLE

Key Features:

Very accessible to a new developer.
Interactive Interpreter with Syntax Highlighting.
Inbuilt debugger with stepping and breakpoints.
Auto-completion and call tips.

Pros and Cons:

Pros: Excellent for beginners, lightweight, and pre-installed with Python.
Cons: Very few advanced development features, basic interface.

Some Use Cases:

Best For: learning basics of Python programming.
Good For: Small scripting tasks and educational purposes.

2. PyCharm

Key Features:

Smart code navigation and refactoring.
Integrated Debugger and Test Runner.
Web Frameworks are supported, such as Django, and Flask.
Database tools, SQL support.

Pros and Cons:

Pros: High customizability, great web development support, and many plugins available.
Cons: It's resource-heavy, and has premium features that are only available on the paid version.

Some Use Cases:

Best for professional development with Python.
Good for full-stack web development with Django or Flask.

3. Pyzo

Key Features:

Simple and intuitive interface.
Integrated shell for interactive computing.
Lightweight and fast.

Pros and Cons:

Pros: Easy to install and use, ideal for educational purposes.
Cons: Lacks advanced features for professional development.

Some Use Cases:

It is best for students and educators needing a simple, easy-to-use environment.

4. Spyder

Key Features:

Integration with NumPy, SciPy, and Matplotlib scientific libraries.
Variable explorer with data frame viewer.
Integrated IPython console
Debugging tools and profiler integration.

Pros and Cons:

Pros: Great for data science, not too resource-heavy, supports multiple scientific libraries.
Cons: Not as feature-complete compared to other IDEs, not suitable for web development.

Some Use Cases:

Data Science and Machine Learning projects.
Scientific computing and research.

5. Atom

Key Features:

Cross-platform editing and interface customization.
Integrated package manager for plugins.
Smart autocomplete and multiple panes.
Command Palette for running commands.

Pros and Cons:

Pros: High customizability, open source, good community support.
Cons: Slow on big projects, non-Python specific.

Some Use Cases:

General programming in a light and customizable environment.
Suitable for small to medium-sized Python projects.

6. PyDev

Key Features:

Python code completion and debugging.
Integration with Django.
Support of development with IronPython and Jython.
Interactive console and refactoring tools.

Pros and Cons:

Pros: Strong and powerful IDE with plenty of plugins. Great for big-sized projects.
Cons: Difficult to learn. Resource-hungry.

Some Use Cases:

Enterprise-scale large applications.
Development of web applications using Django and other frameworks.

7. Jupyter

Key Features:

Interactive notebooks with support for live code, text, and visualizations.
Supporting over 40 different programming languages.
Compatible with data science libraries.
Notebooks sharing and Data collaboration utilities.

Pros and Cons:

Pros: Great for data exploration and interactive plotting in live coding sessions. Also good for teaching.
Cons: Less suitable for standalone applications, a little overcomplicated for more mere visualization.

Some Use Cases:

Data science and exploratory data analysis.
Teaching and presentations with live coding.

8. Sublime Text

Key Features:

Lightweight and fast with syntax highlighting.
Command Palette for quick access to functions.
Multiple selection and split editing.
A rich ecosystem of packages and themes.

Pros and Cons:

Pros: Speedy and responsive, easy to extend, cross-platform.
Cons: Requires additional setup for full Python development, lacks advanced IDE features.

Some Use Cases:

Quick scripting and small projects.
Editing across multiple languages with plugins.

9. Thonny

Key Features:

Simple debugger and syntax error highlighting.
Expression evaluation and variable tracing.
Easy-to-use interface for beginners.
Dedicated windows for function calls and variable tracking.

Pros and Cons:

Pros: Excellent for beginners. The interface is very simple. It's excellent for learning.
Cons: fewer advanced programming features, very basic interface.

Some Use Cases:

Education and Programming Learning.
Easy Python scripting.

10. Visual Studio Code

Key Features:

Smart code completions and debugging tools.
A rich ecosystem of extensions and integrations.
Built-in Git integration and terminal.
Multi-language support.

Pros and Cons:

Pros: Highly customizable, excellent for debugging, free and open-source.
Cons: Can be resource-intensive with many extensions, not a full-fledged IDE.

Some Use Cases:

Full-stack development with Python.
General-purpose programming with various languages.

11. Eric

Key Features:

Integrated Debugger and source code documentation tools.
Embedded project management and unit testing support.
Syntax highlighting and code folding.
Integrated refactoring and task management.

Pros and Cons:

Pros: Comprehensive features for professional development, strong debugging tools.
Cons: Steep learning curve, less user-friendly than some alternatives.

Some Use Cases:

Professional Python development projects.
Python-based software engineering.

12. Wing IDE

Key Features:

Advanced Debugging Tools and Code Intelligence.
Support for Remote Development and Unit Testing.
Customizable Interface and Smart Code Navigation.
Pythonic design with context-appropriate help.

Pros and Cons:

Pros: Very good to debug and test with a high degree of personalization, extremely useful in these regards.
Cons: Costly if a fully-featured version is required; oriented toward professional developers.

Some Use Cases:

Difficult Python applications with high debugging demands.
Distributed or remote development.

Free vs. Paid Python IDEs

What to Expect from Free Python IDEs:

Basic Features: Usually offer fundamental coding, debugging, and testing features.
Community Support: Often maintained by an open-source community with frequent updates.
Limited Advanced Tools: Might lack the full suite of tools needed for complex projects.

Advantages of Paid Python IDEs:

Professional Support: Access to dedicated customer support and regular updates.
Advanced Tools: Includes features like remote development, advanced debugging, and enterprise-level integrations.
Customization: Often more customizable to fit specific project needs.

FAQs

What is an IDE, and why is it important for Python programming?

An Integrated Development Environment is necessary for all-in-one gears for writing, testing, and debugging code for better productivity and efficiency.

What is the best Python IDE for data science and machine learning?

One of the best IDEs for Data Science would be Jupyter. This is because it has an Interactive Notebook feature and provides an integration with Data Science Libraries.

Can I use multiple Python IDEs for different projects?

Yes, most developers have multiple IDEs at their disposal for various projects. Some use PyCharm for web development and Jupyter for data science.

Which Python IDE has the best debugging tools?

PyCharm and Wing IDE are used the most for debugging due to the availability of advanced debugging tools, which are pretty helpful in large and complex projects.

How does PyCharm compare to Visual Studio Code for Python programming?

While PyCharm provides a more fully-fledged environment and is filled with advanced tools for Python, Visual Studio Code offers lightness. The spectrum of its extensions will support almost any language.

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