Top 6 Python IDEs: Choosing the Best IDE for Your Python Development Needs

Did You Know? Some Python IDEs support remote development, allowing you to write and debug code running on a remote server or container. This means you can work on projects hosted on powerful cloud machines or specialized environments without leaving your local IDE, improving flexibility and resource usage.

Choosing the right Python IDE can totally change how you code. Picture debugging a tricky script without any help—no code suggestions, no easy way to step through errors. It’s a nightmare. That’s why tools like PyCharm are game-changers, offering smart autocomplete and built-in debugging that save you hours.

With 85% of developers using Python as their main language, picking an IDE that fits your style matters. Whether you’re just starting out or managing big projects, this guide breaks down the best Python IDEs, what makes them stand out, and how to get started quickly. 

Let’s find the perfect IDE to make your coding smoother and more fun!

Improve your understanding of different programming languages like Python with our online software development courses. Learn how to choose the right programming language for your specific requirements! 

What are Python IDEs and Why are They Important in Programming?

An Integrated Development Environment (IDE) is a programmer's best companion. It’s more than just a text editor—it's a powerhouse that combines tools like code editors, debuggers, and compilers into one cohesive platform. 

An IDE makes a big difference for anything from a simple Python script to advanced data analysis, helping you work more efficiently and accurately!

Why does it matter so much in programming? Imagine driving a car without a dashboard. You’d have no idea about your speed, fuel level, or engine health. Similarly, coding without an IDE means losing visibility into errors, code structure, and performance. 

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Key Features of an IDE

A great IDE does more than execute your code. It transforms your coding experience with convenience and precision.

The following features are essential in the best Python IDEs.

• Code Editing and Completion: Offers real-time suggestions and auto-completes code snippets.
• Debugging Tools: Identifies errors and suggests fixes during execution.
• Version Control Integration: Simplifies managing repositories with tools like Git.
• Customizable Environment: This lets you tweak themes, fonts, and shortcuts.
• Support for Frameworks: Enhances productivity by seamlessly integrating Django, Flask, or TensorFlow.
• Interactive Shell: Runs code in parts for easier testing and debugging.

You’ll notice these features are what set IDEs apart from basic text editors. The next section highlights this difference.

IDE vs. Text Editors

Choosing between an IDE and a text editor often depends on your needs. The table compares these options to clarify the differences.

Aspect	IDEs	Text Editors
Features	Comprehensive, with debugging and integration	Lightweight, focused on text manipulation
Ease of Use	Requires setup and a learning curve	Simple and easy to use
Customization	Highly customizable	Customizable but limited functionality
Performance	Slower for small tasks due to feature overload	Faster for quick tasks
Best For	Large-scale or framework-heavy projects	Simple scripts or quick fixes

If you value depth and efficiency, IDEs are the way to go. But text editors win in speed and simplicity for short-term tasks.

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Also Read: Python Cheat Sheet: From Fundamentals to Advanced Concepts for 2025

You have seen what exactly are Python IDEs. But, which IDE is best for Python? Find out as the next section dives into the best free Python IDEs to simplify your development experience.

What are the Best Free Python IDEs for Data Science and Machine Learning?

Choosing the right Python IDE can feel overwhelming, but free options make the decision less daunting. The following IDEs stand out for their exceptional features and versatility, catering to different programming needs without costing a penny.

IDEs provide programmers with a much-needed dashboard. They boost productivity, catch errors early, and even suggest improvements.

One of the most popular programming languages, Python, relies on robust IDEs to make life easier. But which IDE is best for Python? With so many options, it's like standing in a candy store, overwhelmed by the choices. 

To help you decide, the strengths and weaknesses of the Python IDEs mentioned below have been summarized.

IDE	Key Features	Performance	Compatibility
PyCharm	Advanced debugging, smart code navigation, built-in tools for testing	Highly optimized for large projects	Excellent for Python-specific tasks on multiple OS
Visual Studio Code	Lightweight, highly customizable with extensions, and Git integration	Fast, even with extensions	Supports Python and other languages across platforms
Jupyter Notebook	Interactive outputs, ideal for data analysis and visualization	Great for smaller projects	Web-based; runs on almost any environment
Sublime Text	Minimalistic interface, extremely fast, powerful plugin support	Blazing fast for quick tasks	Supports Python with plugins; multi-OS support
Spyder	Scientific tools integration, variable explorer, perfect for data science	Adequate for data-heavy projects	Primarily designed for Python; cross-platform
Atom	Open-source, hackable, GitHub integration, and a user-friendly package manager	Lightweight, smooth performance	Supports Python via packages; runs on all major OS

To help you, the section below dives into the details of these free Python IDEs.

1. PyCharm

PyCharm, developed by JetBrains, is a powerhouse for Python developers. It offers features like intelligent code completion, advanced debugging, and seamless integration with frameworks like Django and Flask. Its ability to handle large-scale projects while maintaining speed makes it ideal for professional-grade development. 

If you want automation, PyCharm's refactoring tools will save you hours. PyCharm's community edition provides an impressive suite of tools for free, but its charm lies in its ability to adapt to your coding style.

Use Case: PyCharm

You’re developing a complex Python web application for handling financial transactions at your startup. With PyCharm, you can easily debug your code and catch errors early with its powerful tools like code completion and real-time error checking. It helps you build secure, scalable systems quickly and efficiently, making it a go-to IDE for large projects.

Now, let’s see how another popular IDE holds up.

Also Read: Top 36+ Python Projects for Beginners and Students to Explore in 2025

2. Visual Studio Code

Visual Studio Code (VS Code) combines simplicity with power. Lightweight yet feature-packed, it supports Python through extensions. Its Git integration makes it perfect for collaborative projects, while its marketplace offers endless customization. Whether you’re developing APIs or debugging scripts, VS Code doesn’t disappoint. 

Its speed and multi-language support give it an edge for developers who must manage multiple technologies. You can also tweak it to your heart’s content, adding plugins for Python-specific workflows.

Use Case: Visual Studio Code

You’re a freelance developer working on automating data extraction for a client in the real estate industry. Visual Studio Code is perfect for you with its fast performance, Python extensions, and Git integration. You use it to write, test, and maintain your scripts, ensuring your code is clean and ready for deployment across different platforms.

But if interactivity is your style, the next IDE might suit you better.

Also Read: Top 15 Python Challenges for Beginners with Examples

3. Jupyter Notebook

Jupyter Notebook shines in data analysis and visualization. Its notebook-style interface allows you to write, test, and execute Python code alongside documentation. Ideal for machine learning and scientific applications, it supports interactive plots and real-time collaboration. 

Whether you’re analyzing datasets or explaining algorithms, Jupyter turns complex tasks into visual stories. Its ease of use and compatibility with most browsers make it a top choice for analysts and researchers.

Use Case: Jupyter Notebook

You’re a data scientist working at a healthcare company, building machine learning models to predict patient outcomes. With Jupyter Notebook, you can write and run code interactively, visualize datasets, and document your analysis, all in one place. It helps you communicate your findings clearly with stakeholders and allows for quick iteration on models.

You can get a better understanding of Python integration with upGrad’s Learn Python Libraries: NumPy, Matplotlib & Pandas. Learn how to manipulate data using NumPy, visualize insights with Matplotlib, and analyze datasets with Pandas.

Switching gears, the next IDE focuses on speed and simplicity.

4. Sublime Text

Sublime Text is like a ninja in the coding world—fast, silent, and incredibly efficient. While not strictly an IDE, it transforms into one with Python-specific plugins. Its distraction-free mode and blazing speed make it a favorite for quick edits and scripting. 

The built-in package manager opens the door to countless enhancements. Whether you're creating a script or fixing bugs, Sublime Text ensures you don’t miss a beat.

Use Case: Sublime Text

You’re a junior developer working on a simple Python automation script for managing daily reports. Sublime Text is your tool of choice because it’s fast and lightweight. It allows you to write and test your scripts quickly without the overhead of a full IDE, keeping your focus on the task at hand.

Also Read: 50 Python Project Ideas With Source Code [2025 Guide]

From minimalistic speed, let’s move to something scientific.

5. Spyder

Spyder is a scientist’s IDE. It is tailored for data-heavy tasks and offers tools such as a variable explorer, advanced plotting capabilities, and integration with Python libraries like NumPy and pandas. 

Spyder's focus on reproducibility makes it a favorite in academic and research circles. It simplifies working with large datasets while allowing you to visualize and debug effortlessly. If your Python work involves analysis, this IDE is worth exploring.

Use Case: Spyder

You’re a researcher at a university analyzing large sets of scientific data in climate change studies. Spyder provides you with an interactive console and integrates well with libraries like NumPy and Matplotlib. You can manipulate and visualize data seamlessly, speeding up your analysis and experiments.

Also Read: Everything you need to know about data structure & algorithms in Python

Curious about open-source freedom? The next IDE might catch your attention.

6. Atom

Atom, the hackable text editor from GitHub, is a haven for developers who love customization. Its simple interface hides powerful features, including Python support via plugins. 

With community-driven packages and Git integration, Atom adapts to your projects effortlessly. It’s lightweight yet versatile, making it a great companion for developers exploring multiple Python frameworks. If tweaking your development environment excites you, Atom won’t disappoint.

Use Case: Atom

You’re a product developer working on a Python-based desktop application for a small business. Atom suits your needs with its lightweight interface and customizable plugins. You use the Python package and GitHub integration to stay organized, collaborate with your team remotely, and manage your code efficiently.

You can also build relevant programming skills with upGrad’s Master of Design in User Experience. You’ll learn to create user-centered designs that are innovative, efficient, and aligned with current industry trends, ensuring that your designs are not only functional but also impactful.

Also Read: Most Asked Python Interview Questions & Answers 

Now, let’s classify these IDEs based on their best-suited applications.

Classifying Top Python IDEs for Different Applications

Choosing the right Python IDE depends on the specific needs of your project. Whether you're building web applications, data science models, or automating tasks, the ideal IDE can significantly improve your productivity and code quality. 

This table categorizes the top Python IDEs based on their performance across various development scenarios:

Use Case	Recommended IDEs	Why?
Data Analysis, Machine Learning, AI	Jupyter Notebook, Spyder	Interactive outputs, scientific tools, and visualization capabilities.
Python Web Frameworks (Django, Flask)	PyCharm, Visual Studio Code	Framework-specific tools, robust debugging, and easy deployment.
Scientific and Engineering Applications	Spyder, Jupyter Notebook	Handles large datasets, advanced plotting, and library integration.
Quick Python Projects for Beginners	Sublime Text, Atom	Lightweight, fast, and distraction-free environments ideal for rapid prototyping.
Cross-OS Compatibility	Visual Studio Code, Atom	Performs seamlessly across Windows, macOS, and Linux platforms.
Cloud IDEs for Collaboration	Codeanywhere, Jupyter Notebook (Cloud)	Supports remote access, real-time collaboration, and cloud-based project sharing.

The above table underscores how specific IDEs excel in different tasks, making it easier to identify which IDE is best for Python in your projects.

Also Read: Reasons Why Python is So Popular With Developers

From classifications, you might now wonder how to make the final choice. The next section covers exactly that.

How to Pick the Best Python IDEs for Machine Learning and Data Science Projects ?

Choosing the right IDE can make or break your coding workflow. The following section guides you in selecting the perfect IDE tailored to your specific needs.

The factors mentioned below will simplify your decision-making process.

Consider Your Experience Level

Your experience level directly impacts which IDE is best for Python in your case. Beginners often find Jupyter Notebook or Spyder more user-friendly. Their intuitive interfaces and interactive environments make coding more manageable. 

On the other hand, advanced developers might lean toward PyCharm or Visual Studio Code for their powerful debugging and extensive feature sets.

If you’re just starting out, avoid IDEs with steep learning curves. You don’t want to spend hours configuring tools instead of coding. For seasoned developers, flexibility and customizability matter more, so opt for IDEs that adapt to your workflows.

Also Read: Reasons Why Python is So Popular With Developers

Now that you’ve considered your skillset, think about the type of project you’re tackling.

Project Type and Size

Large-scale projects often require robust IDEs. PyCharm and Visual Studio Code excel here, offering features like integrated version control and advanced refactoring tools. For quick scripts or small utilities, lightweight options like Sublime Text or Atom are ideal. These IDEs launch instantly and focus on speed.

Data-heavy projects benefit from Jupyter Notebook or Spyder. Their focus on scientific computing ensures the smooth handling of libraries like pandas and NumPy. Web development? PyCharm and Visual Studio Code shine with their seamless Django and Flask integrations.

Also Read: Top 10 Real Time Python Projects [Beginners to Advanced]

With the project type sorted, the focus shifts to a crucial factor—your preferences and work style.

Personal Preferences and Work Style

Your coding environment should reflect how you work. Do you love a clean, distraction-free workspace? Sublime Text or Atom might feel like home. Prefer feature-packed environments with everything in one place? PyCharm or VS Code will keep you productive.

Some developers thrive on customization. Atom offers near-unlimited possibilities to tweak themes, plugins, and key bindings. Others want simplicity, where Spyder and Jupyter take the lead.

Ultimately, the best Python IDE balances functionality with how you like to work. You’ll enjoy coding more when your IDE feels natural and intuitive.

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Also Read: Essential Python Developer Skills | How to Become a Python Developer

Now that you know how to choose the right IDE, the next step is understanding installation and setup.

How to Install and Set Up Python IDEs for DS and ML Projects?

Setting up the right Python Integrated Development Environment (IDE) is essential for efficient Data Science (DS) and Machine Learning (ML) development. Each IDE offers distinct features that cater to different needs, whether it's for complex projects, quick prototyping, or smooth debugging. Below is a comprehensive guide for installing and configuring the most popular Python IDEs used in data science and ML, followed by a summary table for quick reference.

IDE	Installation Process	Configuration & Setup
PyCharm	1. Download the free Community Edition from JetBrains. 2. Run the installer and select Python options.	1. Go to "File > Settings > Project Interpreter" to add your Python interpreter. 2. Customize themes, shortcuts, and install useful plugins.
Visual Studio Code (VS Code)	1. Download VS Code from the official website. 2. Install the Python extension from the Extensions Marketplace.	1. Set your Python interpreter via the bottom-left corner menu. 2. Install extensions like Pylint or Black for better code formatting.
Jupyter Notebook	1. Install Python and pip if not installed. 2. Run pip install notebook in your terminal.	1. Launch Jupyter by typing jupyter notebook in the terminal. 2. Set up virtual environments for different projects.
Sublime Text	1. Download and install Sublime Text from the official website. 2. Install the SublimeREPL package via Package Control.	1. Configure Python build systems in "Tools > Build System > New Build System". 2. Add Python-specific configurations.
Spyder	1. Download and install as part of the Anaconda distribution or standalone. 2. Install and launch Spyder.	1. Configure Python interpreter under "Preferences > Python Interpreter". 2. Adjust layouts, add plugins, and use debugging tools like variable explorer.
Atom	1. Download Atom from the official website. 2. Install the "ide-python" package.	1. Configure Python interpreter paths in package settings. 2. Use the "script" package to execute Python files directly.

Also Read: Top 25 NLP Libraries for Python for Effective Text Analysis

Each IDE has unique steps but offers flexibility to customize your development environment. With installation out of the way, it’s time to explore what makes an IDE indispensable.

How Python IDEs Improve Your Coding Workflow

Python IDEs are more than just editors—they're powerful tools designed to boost your productivity and streamline your coding process. With the right IDE, you can turn frustration into efficiency, whether you're tackling a complex algorithm or diving deep into machine learning projects. 

Here’s how Python IDEs can significantly enhance your coding workflow:

1. Simplified Debugging

When you’re working on a challenging piece of code, debugging can often be the most time-consuming part. IDEs like PyCharm and VS Code allow you to see errors in real time as you type. These IDEs come with built-in debuggers that let you step through your code line by line, inspect variables, and identify exactly where things are going wrong. This means you can fix bugs faster, without the need for print statements or complicated logging.

2. Integrated Framework Support

Setting up frameworks like Django or Flask can be tricky, especially if you're working with multiple dependencies. With IDEs, you don’t have to worry about complex setups. They often have dedicated tools and templates to help you quickly create projects, configure settings, and deploy your applications. For example, PyCharm’s project templates for Flask and Django allow you to get started with minimal hassle.

3. Efficient Code Management

As your project grows, keeping your codebase organized can become challenging. Fortunately, Python IDEs like PyCharm include features such as intelligent code navigation, search functionalities, and automatic refactoring. You can quickly search for functions, classes, or variables, making it easy to manage even large projects and ensure your code remains clean and maintainable.

4. Customizable Environment
Everyone has their preferred coding style. IDEs like Atom give you the freedom to customize themes, keyboard shortcuts, and extensions to match your workflow. Whether you want a dark theme for late-night coding sessions or specific shortcuts to speed up your tasks, these customizations ensure you’re working in an environment that feels natural and efficient.

5. Seamless Collaboration
Collaboration is a key part of most data science and machine learning projects, and IDEs like Jupyter Notebook make it easy to share your code and findings with others. You can live-share notebooks for real-time collaboration, allowing you to work together without worrying about compatibility issues. Cloud-based IDEs like Codeanywhere also enhance remote teamwork by offering access from any device.

6. Time-Saving Code Suggestions
Python IDEs can predict what you’re about to type with features like auto-completion. As you write your code, suggestions pop up, allowing you to complete lines or functions without manually typing everything. This not only saves time but also helps avoid typos and repetitive work, allowing you to stay focused and productive.

7. Integrated Tools for Better Productivity
One of the biggest advantages of Python IDEs is that they centralize everything you need in one place. From version control integration (like Git) to setting up virtual environments, you won’t need to switch between multiple apps. With everything at your fingertips, your workflow becomes smoother and more efficient.

Transitioning from workflow benefits, the next section dives deeper into why selecting the right IDE matters.

Why Choosing the Right IDE Matters?

Your choice of Python IDE can shape the trajectory of your coding journey. It’s not just about features; the right IDE impacts how you work, think, and achieve your development goals. 

Imagine trying to paint with dull brushes—it’s frustrating and limits your potential. Similarly, an ill-suited IDE stifles productivity, code quality, and long-term success.

The table highlights how Python IDEs influence key aspects of your development process.

Aspect	Impact on Productivity	Enhancing Code Quality	Long-Term Development Goals
Focus and Efficiency	Tools like intelligent navigation and auto-completion help you stay focused.	Syntax highlighting and real-time error checking ensure cleaner, error-free code.	Efficient tools support scalability, letting you maintain focus on larger project objectives.
Error Reduction	Real-time debugging and suggestions minimize time wasted on hunting bugs.	IDEs like PyCharm catch logical errors before execution, improving reliability.	Built-in testing tools align with long-term goals like maintainability and robust performance.
Speed	Time-saving features, such as reusable templates and shortcuts, accelerate development.	Code suggestions reduce redundancy, promoting concise and efficient programming practices.	Fast workflows let you scale projects or collaborate across teams with minimal bottlenecks.
Collaboration	IDEs like Jupyter Notebook enhance teamwork by allowing seamless code sharing.	Clean, readable code fosters better understanding among collaborators.	IDE ecosystems with plugins and cloud features support remote collaboration for growing teams.
Scalability	Powerful tools for version control simplify branching and merging in large projects.	Structured code ensures scalability, making it easier to adapt as projects evolve.	IDE frameworks like VS Code’s extension marketplace grow with your projects over time.

The points mentioned above prove why the right IDE isn’t just a convenience—it’s a necessity. It influences every step of your coding process, from writing the first line of code to scaling up for enterprise-level projects.

Also Read: Explore 12 Real-World Applications of Python [2025]

With the importance of IDE selection covered, the next section explores how to elevate your Python skills further.

Enhance Your Python Learning with upGrad

Choosing the right Python IDE is crucial for boosting productivity and streamlining your development process. Each IDE offers distinct features that cater to different skill levels and project needs, so knowing their strengths helps you code, debug, and manage projects more efficiently. 

Understanding the top Python IDEs ensures you select the best tool to accelerate your learning and build better applications faster. For this, you need an online learning platform that understands your ambitions and helps you reach them. upGrad, with over 10 million learners, 200+ courses, and 1,400+ hiring partners, is the perfect partner for your Python programming journey. Whether you’re just starting or looking to sharpen advanced skills, upGrad has something tailored for you.

Apart from the programs covered above, here are some additional courses that can help you learning journey:

• Introduction to Data Analysis using Excel
• Analyzing Patterns in Data and Storytelling
• Basics of Inferential Statistics
• Introduction to Tableau
• Case Study using Tableau, Python and SQL

For personalized career guidance, contact upGrad’s counselors or visit a nearby upGrad career center. With expert support and an industry-focused curriculum, you'll be prepared to tackle programming challenges and advance your career.