Why Is Python So Popular in 2025?

Data Science Web Development

While other programming languages come and go, Python has stood the test of time and firmly established itself as a top choice for developers of all levels, from beginners to seasoned professionals.

Whether you’re working on intelligent systems or data-driven workflows, Python has a pivotal role to play in how your software is built, scaled, and optimized.

Many surveys, including our upcoming Developer Ecosystem Survey 2025, confirm Python’s continued popularity. The real question is why developers keep choosing it, and that’s what we’ll explore. 

Whether you’re choosing your first language or building production-scale services, this post will walk you through why Python remains a top choice for developers.

How popular is Python in 2025?

In our Developer Ecosystem Survey 2025, Python ranks as the second most-used programming language in the last 12 months, with 57% of developers reporting that they use it.

More than a third (34%) said Python is their primary programming language. This places it ahead of JavaScript, Java, and TypeScript in terms of primary use. It’s also performing well despite fierce competition from newer systems and niche domain tools.

These stats tell a story of sustained relevance across diverse developer segments, from seasoned backend engineers to first-time data analysts.

This continued success is down to Python’s ability to grow with you. It doesn’t just serve as a first step; it continues adding value in advanced environments as you gain skills and experience throughout your career.

Let’s explore why Python remains a popular choice in 2025.

1. Dominance in AI and machine learning

Our recently released report, The State of Python 2025, shows that 41% of Python developers use the language specifically for machine learning.

This is because Python drives innovation in areas like natural language processing, computer vision, and recommendation systems.

Python’s strength in this area comes from the fact that it offers support at every stage of the process, from prototyping to production. It also integrates into machine learning operations (MLOps) pipelines with minimal friction and high flexibility.

One of the most significant reasons for Python’s popularity is its syntax, which is expressive, readable, and dynamic. This allows developers to write training loops, manipulate tensors, and orchestrate workflows without boilerplate friction. 

However, it’s Python’s ecosystem that makes it indispensable.

Core frameworks include:

• PyTorch – for research-oriented deep learning

• TensorFlow – for production deployment and scalability
• Keras – for rapid prototyping
• scikit-learn – for classical machine learning
• Hugging Face Transformers – for natural language processing and generative models

These frameworks are mature, well-documented, and interoperable, benefitting from rapid open-source development and extensive community contributions. They support everything from GPU acceleration and distributed training to model export and quantization.

Python also integrates cleanly across the machine learning (ML) pipeline, from data preprocessing with pandas and NumPy to model serving via FastAPI or Flask to inference serving for LLMs with vLLM.

It all comes together to provide a solution that allows you to deliver a working AI solution without ever really having to work outside Python.

2. Strength in data science and analytics

From analytics dashboards to ETL scripts, Python’s flexibility drives fast, interpretable insights across industries. It’s particularly adept at handling complex data, such as time-series analyses. 

The State of Python 2025 reveals that 51% of respondents are involved in data exploration and processing. This includes tasks like:

• Data extraction, transformation, and loading (ETL)
• Exploratory data analysis (EDA)
• Statistical and predictive modeling
• Visualization and reporting
• Real-time data analysis
• Communication of insights

Core libraries such as pandas, NumPy, Matplotlib, Plotly, and Jupyter Notebook form a mature ecosystem that’s supported by strong documentation and active community development.

Python offers a unique balance. It’s accessible enough for non-engineers, but powerful enough for production-grade pipelines. It also integrates with cloud platforms, supports multiple data formats, and works seamlessly with SQL and NoSQL data stores.

3. Syntax that’s simple and scalable

Python’s most visible strength remains its readability. Developers routinely cite Python’s low barrier to entry and clean syntax as reasons for initial adoption and longer-term loyalty. In Python, even model training syntax reads like plain English:

def train(model): for item in model.data: model.learn(item)

Code snippets like this require no special decoding. That clarity isn’t just beginner-friendly; it also lowers maintenance costs, shortens onboarding time, and improves communication across mixed-skill teams.

This readability brings practical advantages. Teams spend less time deciphering logic and more time improving functionality. Bugs surface faster. Reviews run more smoothly. And non-developers can often read Python scripts without assistance.

The State of Python 2025 revealed that 50% of respondents had less than two years of total coding experience. Over a third (39%) had been coding in Python for two years or less, even in hobbyist or educational settings.

This is where Python really stands out. Though its simple syntax makes it an ideal entry point for new coders, it scales with users, which means retention rates remain high. As projects grow in complexity, Python’s simplicity becomes a strength, not a limitation.

Add to this the fact that Python supports multiple programming paradigms (procedural, object-oriented, and functional), and it becomes clear why readability is important. It’s what enables developers to move between approaches without friction.

4. A mature and versatile ecosystem

Python’s power lies in its vast network of libraries that span nearly every domain of modern software development.

Our survey shows that developers rely on Python for everything from web applications and API integration to data science, automation, and testing. 

Its deep, actively maintained toolset means you can use Python at all stages of production.

Here’s a snapshot of Python’s core domains and the main libraries developers reach for:

Domain	Popular Libraries
Web development	Django, Flask, FastAPI
AI and ML	TensorFlow, PyTorch, scikit-learn, Keras
Testing	pytest, unittest, Hypothesis
Automation	Click, APScheduler, Rich
Data science	pandas, NumPy, Plotly, Matplotlib

This breadth translates to real-world agility. Developers can move between back-end APIs and machine learning pipelines without changing language or tooling. They can prototype with high-level wrappers and drop to lower-level control when needed.

Critically, Python’s packaging and dependency management systems like pip, conda, and poetry support modular development and reproducible environments. Combined with frameworks like FastAPI for APIs, pytest for testing, and pandas for data handling, Python offers unrivaled scalability.

5. Community support and shared knowledge

Python’s enduring popularity owes much to its global, engaged developer community.

From individual learners to enterprise teams, Python users benefit from open forums, high-quality tutorials, and a strong culture of mentorship. The community isn’t just helpful, it’s fast-moving and inclusive, fostering a welcoming environment for developers of all levels.

Key pillars include:

• The Python Software Foundation, which supports education, events, and outreach.
• High activity on Stack Overflow, ensuring quick answers to real-world problems, and active participation in open-source projects and local user groups.
• A rich landscape of resources (Real Python, Talk Python, and PyCon), serving both beginners and professionals.

This network doesn’t just solve problems; it also shapes the language’s evolution. Python’s ecosystem is sustained by collaboration, continual refinement, and shared best practices.

When you choose Python, you tap into a knowledge base that grows with the language and with you over time.

6. Cross-domain versatility

Python’s reach is not limited to AI and ML or data science and analytics. It’s equally at home in automation, scripting, web APIs, data workflows, and systems engineering. Its ability to move seamlessly across platforms, domains, and deployment targets makes it the default language for multipurpose development.

The State of Python 2025 shows just how broadly developers rely on Python:

Functionality	Percentage of Python users
Data analysis	48%
Web development	46%
Machine learning	41%
Data engineering	31%
Academic research	27%
DevOps and systems administration	26%

That spread illustrates Python’s domain elasticity. The same language that powers model training can also automate payroll tasks, control scientific instruments, or serve REST endpoints. Developers can consolidate tools, reduce context-switching, and streamline team workflows.

Python’s platform independence (Windows, Linux, MacOS, cloud, and browser) reinforces this versatility. Add in a robust packaging ecosystem and consistent cross-library standards, and the result is a language equally suited to both rapid prototyping and enterprise production.

Few languages match Python’s reach, and fewer still offer such seamless continuity. From frontend interfaces to backend logic, Python gives developers one cohesive environment to build and ship full solutions.

That completeness is part of the reason people stick with it. Once you’re in, you rarely need to reach for anything else.

Python in the age of intelligent development

As software becomes more adaptive, predictive, and intelligent, Python is strongly positioned to retain its popularity. 

Its abilities in areas like AI, ML, and data handling, as well as its mature libraries, make it a strong choice for systems that evolve over time.

Python’s popularity comes from its ability to easily scale across your projects and platforms. It continues to be a great choice for developers of all experience levels and across projects of all sizes, from casual automation scripts to enterprise AI platforms.

And when working with PyCharm, Python is an intelligent, fast, and clean option.

For a deeper dive, check out The State of Python 2025 by Michael Kennedy, Python expert and host of the Talk Python to Me podcast. 

Michael analyzed over 30,000 responses from our Python Developers Survey 2024, uncovering fascinating insights and identifying the latest trends.

Whether you’re a beginner or seasoned developer, The State of Python 2025 will give you the inside track on where the language is now and where it’s headed. 

As tools like Astral’s uv show, Python’s evolution is far from over, despite its relative maturity. With a growing ecosystem and proven staying power, it’s well-positioned to remain a popular choice for developers for years to come.