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Python remains the main language for 86% of respondents in the 2024 Python Developers Survey analysis, but that does not mean every part of Python development feels effortless. The practical route to a better experience is to tune the workflow—typing, editor support, documentation, learning habits, and performance decisions—to the project rather than expect one tool or language to solve everything.
Is Python still worth learning?
For many developers, yes. In Michael Kennedy’s 2025 analysis of the 2024 Python Developers Survey, 86% of respondents said Python was their main language. The survey also found a substantial population of newer programmers: 50% had fewer than two years of professional coding experience, and 39% had fewer than two years of Python experience. These are survey findings, not a census of developers or a guarantee that Python is right for every role. Kennedy’s analysis draws on a community survey whose recruitment and methodology are described by the Python Software Foundation and JetBrains.
Python’s appeal is not only that people use it; the surrounding learning ecosystem also matters. Documentation and embedded tutorials ranked among leading learning resources in the Python survey analysis. That can make it easier to move from a small script to a larger project, although it does not eliminate the effort of understanding a particular library or codebase.
What can make Python development frustrating?
Typing can improve feedback but add overhead
Python type annotations are optional, which lets teams introduce them gradually. They can help editors offer completion and navigation, make interfaces easier to understand, and flag some mistakes before code runs. In a separate 2024 typing survey, 88% of respondents said they always or often use types; 59% selected better IDE support as a useful feature, 49.8% bug prevention, and 49.2% documentation. These are reported preferences and perceived benefits, not measured proof that types prevent bugs or improve productivity in every project. The survey’s authors also caution that a typing-focused survey may attract people especially interested in typing and is not representative of the entire Python community. Meta Engineering’s report gives the context.
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The same report illustrates the trade-off: its freeform feedback included 29 responses about complexity expressing dynamic features, 22 about slow type-checker performance, and 21 about inconsistent behavior across checkers. Those are counts of comments, not community-wide percentages. Type information can become a burden when annotations are hard to understand, checks slow down the edit-run cycle, or dependencies lack useful typing information.
Documentation can be hard to discover
Having abundant documentation is not the same as finding the right explanation at the moment it is needed. The typing survey included concerns about discoverability and documentation for advanced features. For a team, vague conventions and examples that cannot be run make onboarding harder even when a library’s reference material is comprehensive.
Runtime speed depends on the workload
Whether Python feels slow depends on what a program does and where time is spent. The available survey evidence does not establish a general speed comparison against other languages or say how often runtime is the deciding limitation. Measure the actual workload before rewriting code: time the slow operation, distinguish computation from network or disk waiting, and optimize only the part that profiling identifies as important.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsSome Python projects use C, C++, or Rust for binary modules while keeping orchestration and application logic in Python. That is an architectural option, not a guaranteed speedup. Cheuk Ting Ho, PSF Board Member and Developer Advocate at JetBrains, describes the rationale this way: “Python and Rust complement each other well. Python is a high-level language and fast to code, while Rust is compiled and fast to execute. PyO3 brings the two together so developers can have the best of both worlds.” This is commentary, not an independent benchmark. The survey results page provides its context.
How can you make Python work feel smoother?
Choose a type checker by trying it on your project
The typing survey reported MyPy as the most-used checker among its respondents, with Pyright also in use. That does not make either the best choice for every codebase. Compare candidates against the work your team actually does:
- Editor integration: Check completion, navigation, refactoring, and how clearly diagnostics appear in the editor.
- Responsiveness: Run the checker on a representative project and assess whether feedback arrives quickly enough for normal editing.
- Expressiveness: See whether it handles the project’s patterns without forcing annotations that are difficult to maintain.
- Dependency coverage: Check whether important libraries provide useful annotations or stubs.
- Team onboarding: Make sure developers can understand the rules and resolve common diagnostics from examples or concise documentation.
Because the survey reports trade-offs rather than a universal winner, test a candidate in a small, representative part of the codebase before expanding it.
Adopt annotations incrementally
Start where types answer real questions: public interfaces, shared data structures, or areas where unclear inputs and outputs cause recurring mistakes. Use a consistent baseline the team can explain, and expand it as the benefit becomes clear. If annotations are making routine changes harder, revisit the conventions or scope rather than treating maximum coverage as the goal.
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Make project documentation runnable and specific
Pair the library reference with examples that show how your project uses it. A short setup guide, a working example, and a note about local conventions can reduce the time spent rediscovering decisions. Keep examples close to the relevant code and update them when behavior changes. This complements, rather than replaces, the broader documentation and embedded tutorials developers already use to learn Python.
Optimize measured bottlenecks, not assumptions
When an application is too slow, first reproduce the delay and profile the relevant workload. If the delay is in a small computational hotspot, consider a more efficient algorithm or a compiled component while leaving the rest of the application in Python. If the delay comes from waiting on a remote service or storage, changing languages may not address its cause. Compare the operational cost of a mixed-language boundary—building, testing, and maintaining it—with the measured benefit.
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In Kennedy’s analysis of the Python survey, AI tools as a learning source rose from 19% to 27% year over year. Separately, JetBrains’ broad 2025 developer ecosystem survey reports that 85% of its respondents regularly use AI tools for coding and development, while 15% had not adopted them in daily work. The second figure is about a cross-section of developers, not Python developers alone; the two surveys should not be treated as one population. The State of Developer Ecosystem 2025 describes the broader survey.
AI can be one way to explore an unfamiliar API, request an explanation, or draft an example. Treat its output as a starting point: verify claims against documentation, run the code, and review it for correctness and security. The survey adoption numbers do not show that AI universally improves productivity or makes development more satisfying.
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What should a team change first?
- Identify the friction: Decide whether the recurring problem is slow feedback, unclear interfaces, hard-to-find guidance, onboarding, or runtime performance.
- Make one targeted adjustment: Trial editor or checker settings, add a concise project example, introduce annotations in a bounded area, or profile a slow operation.
- Check the effect in normal work: Look for clearer diagnostics, less repeated explanation, easier changes, or a measured improvement in the relevant workload.
- Keep or revise the change: Retain practices that help the team and remove conventions whose complexity costs more than their practical benefit.
The underlying Python survey drew more than 30,000 developers and enthusiasts from almost 200 countries and regions; its published filtered dataset contains more than 25,000 responses after duplicate and unreliable responses were removed. Recruitment used Python-related and community channels, so the results are a broad snapshot of participating developers, not a probability sample of all programmers. The Python Software Foundation’s summary also covers the survey.
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