Python is a popular programming language, but many developers criticize it. This article will look into why some programmers dislike Python. We’ll talk about issues like slow performance, tricky dynamic typing, and problems with multithreading. By looking at discussions on the Python subreddit on Reddit, we’ll show you what’s behind the negative views on this language.
Key Takeaways
- Python’s rapid growth and widespread adoption have led to a mixed reaction from developers, with some expressing discontent with the language’s limitations and drawbacks.
- Common complaints include performance issues, dynamic typing challenges, and difficulties with multithreading, which can hinder productivity and scalability in certain scenarios.
- The Python community has had long-standing discussions on the language’s shortcomings, with developers sharing their perspectives and concerns on forums like Reddit.
- Understanding the criticisms and controversies surrounding Python can help developers make informed decisions about when and how to utilize the language in their projects.
- Exploring the nuances of Python’s limitations can lead to a more balanced and informed understanding of the language’s strengths and weaknesses.
The Rise of Python’s Popularity and Criticism
Python has become a top choice for developers in the last ten years. It’s ranked first by IEEE Spectrum since 2017. Python is known for its strong libraries in artificial intelligence and machine learning. This makes it a key tool in the tech world.
Its easy-to-read syntax and ability to work on different platforms have made it popular. Python’s use grew by 2.22% from September 2018 to September 2019, as seen in the Tiobe index. This shows how more people are using it.
Python’s Growth and Admiration
Millions use Python every day for web development, data science, and more. Big names like Netflix, Instagram, and NASA use it too. This shows how versatile and important Python is in many fields.
Guido van Rossum created Python in the late 1980s for a simpler programming language. His goal was to make a language that was easy to use yet powerful. This vision has made Python a favorite among developers.
Developers’ Mixed Reactions to Python
Not all developers love Python as much as others do. While it’s very popular, it’s not perfect for everything. For example, it’s not great for making desktop or mobile apps. Also, running Python in a web browser can be tricky.
Python is now more focused on machine learning and data science. This means it needs to be faster, sometimes requiring special hardware. These challenges have made some developers unsure about Python.
“Python is ranked first among programming languages by IEEE Spectrum since 2017.”
| Language | Ranking Score |
|---|---|
| Python | 100 |
| Java | 96.3 |
| C | 94.4 |
| C++ | 87.5 |
| R | 81.5 |
Common Complaints and Frustrations
Python is getting more popular, but it’s not without its critics. Many developers talk about performance bottlenecks. They say Python is slow because it’s interpreted and dynamically typed. This is unlike languages like C++ that are faster.
Another big issue is the quirks of Python’s dynamic typing. Without strong typing, it’s hard to check code before running it. This can lead to errors and make maintenance harder. The dynamic typing can also cause unexpected problems and errors.
Performance Bottlenecks
Python isn’t great for tasks that need quick processing or work across many machines fast. The Global Interpreter Lock (GIL) means it can only do one thing at a time. This hurts its performance for tasks that use a lot of CPU in systems with many cores.
Dynamic Typing Quirks
Python’s dynamic typing has its ups and downs. It makes coding easier but can cause problems. Without type safety, making reliable software is harder, especially in big projects. Languages with type-checking are safer and better for large systems.
Multithreading Limitations
The GIL makes it hard for Python to work well with many tasks at once. This is a big issue for developers who need their programs to run fast and in parallel.
Even with these problems, Python is still loved and used a lot. It has a big community that keeps improving it. Knowing what Python is good and bad at helps developers choose the right tool for their projects.
Debating Contentious Opinions on Python
Python has sparked many debates among developers. They argue about invisible characters in Python and how to handle null references. These topics have led to heated discussions, with developers sharing their thoughts on the language’s good and bad points.
Some think invisible characters like whitespace make Python code easier to read and keep up with. Others say they can cause problems and make debugging hard. This debate focuses on the balance between easy syntax and possible confusion.
Handling null references is another big topic. Some say not having a special null type can lead to errors and bugs. But, Python fans believe using None, a special object for nothing, is a smart choice that fits the language well.
“The debate around Python’s pros and cons has been a long-standing one, with developers presenting compelling arguments on both sides. While some see the language’s features as intuitive and productive, others argue that they can introduce unnecessary complexity and challenges. The key is to understand the tradeoffs and find the approach that best suits the specific needs of a project or development team.”
The talks on these topics have been exciting and full of new ideas. As Python keeps growing, the debate will keep going. New views and examples will keep shaping the conversation.
The debate on Python’s pros and cons might not end soon. But, sharing different opinions helps us understand Python better. By talking about these topics, developers can make better choices for their projects. They also help improve the Python language and its community.
Invisible Characters: A Syntactical Misstep?
Python is known for using invisible characters like whitespace as important parts of its syntax. This approach is often seen as clean and concise. Yet, some developers find it problematic, leading to bugs and making it hard for beginners.
There’s a lot of debate about using invisible characters for syntax in Python. Supporters say it helps keep code neat and easy to read and maintain. But, critics say it makes the language harder to understand and more error-prone.
“The use of whitespace for syntax is a double-edged sword. While it can lead to cleaner, more readable code, it also introduces the risk of subtle bugs that are difficult to diagnose, especially for inexperienced programmers.”
Some argue that using code formatting tools and standards would be better than invisible characters for syntax. They believe this would keep code consistent without the issues that come with whitespace.
| Language | Case Sensitivity | Identifier Rules |
|---|---|---|
| Java | Case-sensitive | Identifiers must start with a letter, dollar sign, or underscore, and can contain letters, digits, dollar signs, and underscores. |
| Common Lisp | Case-sensitive | Identifiers can contain letters, digits, and a limited set of special characters, but the case is significant. |
| C | Case-sensitive | Identifiers must start with a letter or underscore and can contain letters, digits, and underscores. |
The debate on invisible characters in Python is ongoing. Advocates on both sides have strong arguments. As Python evolves, it’s unclear if it will change its whitespace-sensitive syntax or stick with it.
why programmers hate python
Python has become very popular in the programming world. It’s easy to use and versatile, drawing in developers from all backgrounds. But, it’s not without its critics, especially from experienced programmers. They have raised several issues that have sparked debate on “why programmers hate Python.”
One big complaint is that Python is slow. Being interpreted and dynamically typed means it can run slower than languages like C or C++. This is a big problem for those needing fast performance or real-time work.
Another issue is with Python’s multithreading. It supports it, but the Global Interpreter Lock (GIL) limits true parallelism. This makes it hard to use modern processors fully, frustrating those needing efficient processing.
Python’s large and varied ecosystem is both a blessing and a curse. With over 400 ways to handle strings and 26 “magic” attributes, it can be inconsistent and hard to learn for beginners.
“Python stands out by lacking a reliable way to catch all errors and their messages in one statement.”
Some don’t like Python’s “Pythonic” style, which focuses on readability and a single solution. Those who prefer more flexibility find it too restrictive. This leads to frustration and makes them hesitant to use Python.
Despite these issues, Python remains a favorite for many. It’s especially popular in data science, machine learning, and web development. The debate on “why programmers hate Python” shows its strengths and weaknesses. It offers insights for developers and those designing languages.
The Null Reference Controversy
Handling null references has been a big debate in programming, especially in languages like Python. Some say null references cause a lot of problems, leading to errors and unexpected behavior. Others believe Python’s “None” system is a good way to handle missing data.
Nulls and Object-Oriented Languages
In languages like Python, null references can lead to issues like python null references and crashes. Critics argue that Python’s type system doesn’t offer enough help for null values. They suggest a stronger type system could prevent these problems.
Some suggest using object-oriented null handling, like in F#. F# gets rid of null references by using “option types” to show if a value is there or not. This method is thought to lower the chance of null errors and give better type information.
F#’s Approach to Handling Nulls
F#’s way of dealing with F# null handling is popular among those who dislike Python’s method. By removing null references and using option types, F# offers a clear way to handle missing data. This can make programs run smoother and with fewer errors.
| Language | Null Handling Approach | Advantages | Disadvantages |
|---|---|---|---|
| Python | Uses “None” to represent null values | Simple and intuitive for beginners | Potential for null reference errors, less explicit type safety |
| F# | Eliminates null references, uses option types | Reduces risk of null reference exceptions, provides more type safety | Steeper learning curve for developers unfamiliar with option types |
The debate on null handling in languages like Python is ongoing. While Python’s “None” is easy for beginners, F#’s option types offer a more detailed way to handle missing data. This shows the ongoing discussion on the best approach to null references.
Overengineering and the “Program Everything” Mindset
Some developers are upset about the trend to over-engineer solutions in Python. They say the “program everything” mindset is too common in the Python world. They believe not every issue needs a complex program. Sometimes, a simple pen and paper can be more effective and easier to manage.
One main complaint is that this “program everything” approach adds too much complexity and size to software projects. Python’s ease and quick development can make developers overdo it. They might make solutions too complex, which makes the code hard to keep up with, debug, and improve. This goes against what drew developers to Python in the first place.
“The tendency to over-engineer solutions and the ‘program everything’ mindset that is prevalent in the Python community is a source of frustration for many developers. Not every problem needs a complex computer-based solution – sometimes a simple pen and paper approach can be more efficient and less maintenance-intensive.”
Supporters of a simpler way of programming say the “program everything” mindset can distract from what really matters. They believe focusing on the basics and keeping things simple leads to better software. This approach makes the software more reliable and easier to keep up with, which benefits users more.
This debate shows the ongoing struggle between wanting flexibility and needing discipline in making software. Python’s flexibility and ease have made it popular, but the “program everything” mindset might be holding it back in some areas. As Python evolves, finding a balance between new ideas and practicality is key. This balance will help keep Python a strong tool for developers.
The Importance of Self-Learning and Googling
In today’s fast-paced software development world, knowing how to learn by yourself and use online tools like Google is key. This skill is vital for programmers, no matter what programming language they work with. Some might think that relying on self-learning and online help shows weakness. But, many experts believe it’s a crucial part of learning and growing in the field.
The need for deep knowledge in programming languages is growing. Managers often ask “proxy questions” in interviews to check how well a candidate knows their languages. This shows how important it is to keep learning and improving your skills.
Python is becoming more popular in the industry. The author of this piece has talked to many people about Python. This shows that having good Python skills is in demand. Programmers need to be ready to use all the features and functions of this powerful language.
Working with programming languages can be tough. The author says it’s important to use online tools like Google to get past these challenges. This way of learning shows you’re eager to keep getting better and adapt to changes in software development.
But, relying on online help can also have its downsides. Some developers might feel left out or discouraged if their questions get downvoted or ignored. Yet, the community usually respects those who learn by themselves and bring new ideas to the table.
In conclusion, being able to learn by yourself and use tools like Google is crucial for python self-learning and googling in programming. By doing this, programmers can stay ahead, solve tough problems, and keep getting better. This helps them succeed in their projects.
| Key Insights | Statistics |
|---|---|
| Managers use “proxy questions” to assess a candidate’s depth of programming language knowledge Python is a highly popular language in the software engineering industry Programmers must overcome challenges and navigate the extensive features of modern programming languages Self-learning and utilizing online resources like Google are essential skills for programmers The software development community generally values self-learners and their contributions | 1 in 10,000 users are considered exceptions to the question-ban rule on Stack Overflow Some users may feel rejected by the community due to downvoted answers or closed questions Stack Overflow values self-learners, as evidenced by the acceptance of self-taught programmers within the community The community makes it clear that it does not prioritize validating emotions of users seeking technical assistance Acculturation to the Stack Overflow culture may encounter bumps or challenges, but can be a valuable learning experience |
“The ability to self-learn and effectively utilize online resources like Google has become an increasingly important skill for programmers, regardless of the language they use.”
Singletons: Necessary Evil or Design Pattern?
The debate on singletons in programming has reached the Python community. Some see them as a needed design pattern, like for loggers or message pumps. Others call them an anti-pattern to avoid. They discuss the good and bad of singletons and if they belong in good software.
One big worry with python singletons is they can make code hard to test and maintain. They bring in global state and hidden dependencies. Also, they can be tricky in a multithreaded setup, not always giving the expected single instance.
But, supporters of the singleton design pattern say it’s useful for managing resources. For example, a single log file or a shared config object. It ensures only one instance, preventing resource issues and offering a central access point.
“Some developers go to great lengths to avoid using singletons as they equate it with failure.”
The singleton debate sparks strong feelings, with some seeing them as an “anti-pattern” to avoid. Yet, others believe singletons can be valuable in specific situations, when used right.
Choosing to use singletons in Python needs careful thought. Consider the specific situation, the trade-offs, and the project’s design principles. Often, using dependency injection and inversion of control is better. They offer similar benefits but are easier to test and more flexible.
Understanding the pros and cons of design patterns is key. Don’t just follow a pattern without thinking. By looking at the project’s unique needs and how it affects code, developers can decide wisely on using singletons in their Python apps.
The Picture vs. Text Debate
Developers often debate whether to use pictures or text in programming documents. Some believe that “a picture is worth a thousand words” doesn’t always hold true. They say that clear, detailed text can better explain complex ideas. It’s crucial to pick the right tool for the task, as not every system fits well with pictures. Relying too much on visuals can make things unclear and less precise.
In April 2015, the “Picture vs. Text Debate” became a big topic in the Python world. People worried that type hints in Python could make the language hard for beginners to understand. They suggested keeping type hints for stub files to keep the code easy to read.
Many developers shared their thoughts on type hints, with different views on them. They talked about standardizing type hints in Python 3.5, as suggested in PEP484. Using stub files for type hints was seen as a way to keep the code readable and meet technical needs.
But, there were concerns about using stub files. They could get outdated, making things harder. Keeping annotations current was seen as key, with inline annotations preferred for easier upkeep. The debate covered when stub files might be useful or not.
The “Picture vs. Text Debate” in the Python community shows the ongoing challenge of balancing clear code with detailed documentation. As programming changes, developers must keep finding the best ways to share complex info. This ensures the effectiveness of python pictures vs text and programming documentation.
Object-Obsessed Programming: A Real Concern?
In programming, the debate about object-oriented programming (OOP) has been ongoing. Python’s popularity has grown, but some developers worry about “object-obsessed programming.” This is when programmers make classes for basic data types like numbers. They think this can make code too complex and shows a lack of understanding of programming.
OOP focuses on making code organized and reusable. It became key in making apps and websites. But, interest in functional programming languages like Haskell and Scala is growing. Big companies like Twitter are using Scala more, showing a shift in programming styles.
There’s a big debate about python object-oriented programming and object-obsessed programming. Developers worry about the complexity and reliability of OOP. They talk about how functional programming might be better for making code easier to change and test.
“The rise of functional programming languages like F#, Go, and Rust due to their perceived advantages in managing concurrency effectively has further fueled the debate around the merits of OOP.”
The debate on object-obsessed programming and OOP’s downsides is still going. Developers and companies need to weigh their options. They should choose the right mix of object-oriented and functional programming to make software that’s top-notch, easy to maintain, and can grow.
The Art of Code Removal
In the world of software development, many experienced programmers believe that code removal is as crucial as adding new code. They think that programming isn’t just about writing new lines. It’s about managing code well and reducing it when possible.
Advocates say that writing less code and removing unnecessary complexity makes software better. By always checking their code and cutting out what’s not needed, developers make software easier to handle, fix, and update.
The idea that “the best code is no code at all” suggests that extra code adds to the trouble of debugging and supporting the system. Developers who follow this idea should write new code reluctantly, under duress, and only after exhausting all other options.
Removing code means using techniques like finding and removing duplicate functions, combining similar code, and making complex parts simpler. This makes it easier for the team to work with the code over time.
| Metric | Before Code Removal | After Code Removal |
|---|---|---|
| Lines of Code | 10,000 | 7,500 |
| Function Count | 500 | 350 |
| Complexity Score | 80 | 60 |
Removing code has big benefits. It makes the software simpler, run faster, and more reliable. This leads to a better user experience. It also encourages a mindful and disciplined way of thinking among developers.
In conclusion, the art of code removal is key to making software better. It challenges the idea that programming is all about adding code. By adopting this approach, developers can make their software more maintainable, efficient, and high-quality, which benefits their users.
Optimization: A Premature Evil or Necessity?
The debate about optimization in software development has reached the Python community. Some say “premature optimization is the root of all evil.” But others believe it’s key to think about optimization early on. They argue that planning for future optimization can make refactoring easier without changing the API too much.
Donald Knuth once said, “Premature optimization is the root of all evil.” He meant that focusing too early on small improvements can be a waste. Knuth believed that we should ignore small efficiencies most of the time. This approach can make debugging and maintaining code harder.
But the discussion on python optimization and premature optimization is complex. Spending extra time to shave off a few milliseconds in simple applications might not be justified. Yet, in crucial areas like gaming engines or high-speed trading, optimization is crucial.
| Application | Optimization Necessity |
|---|---|
| Gaming Engines | High |
| Desktop Accounting Programs | Low |
| High-Frequency Trading Models | High |
| Machine Learning Prototyping | Low |
There’s a divide between those who believe in premature optimization and those who prefer to plan for optimization during design. While the “premature optimization is the root of all evil” idea applies to many projects, some situations call for early optimization.
The ongoing debate in the Python community shows the importance of balancing optimization. Developers must consider the benefits and risks of optimizing their code. The goal is to focus on optimizing key parts of the application without over-engineering or optimizing too early.
Conclusion
The debate about Python’s strengths and weaknesses shows how complex programming languages can be. It also shows the different views within the developer world. Python’s rise in popularity and growth are clear, but the “why programmers hate Python” debate points out the challenges some face.
This article looked into the specific complaints and opinions from the Python community. It aimed to give a deeper look at what drives the debate around this important language. Issues like performance, dynamic typing, syntax, and overengineering have been raised.
Choosing to use or avoid Python depends on personal preferences, project needs, and the developer’s goals and experience. Python has its good points and bad, like any language. It’s up to the community to keep improving and addressing the issues critics raise. By talking openly and constructively, developers can help shape Python’s future and keep it relevant in software development.
FAQ
What are some common complaints and frustrations that developers have with Python?
Developers often mention performance issues, quirks with dynamic typing, and challenges with multithreading. Python’s interpreted nature and dynamic typing can make it slower than compiled languages. Multithreading issues also limit its use of modern hardware.
What are the controversial opinions surrounding Python’s use of invisible characters for syntax?
Some developers find Python’s use of whitespace and invisible characters distracting. They say it causes bugs for beginners and was an unnecessary feature. They suggest tools for coding standards could have been a better choice.
How do developers feel about the handling of null references in Python’s object-oriented programming?
Some developers think null references lead to many bugs. They point out that F# uses option types instead, which is safer. This approach helps avoid null reference errors and makes the type system more informative.
What concerns do developers have about the “program everything” mindset in the Python community?
Some developers dislike the trend of over-engineering and the “program everything” approach in Python. They believe not every problem needs a complex solution. Sometimes, simple methods like using pen and paper are more efficient.
How do developers view the importance of self-learning and utilizing online resources like Google for programming?
Some developers see self-learning and using online tools like Google as key skills. They believe these tools help with learning and solving problems, showing strength not weakness in today’s software development.
What is the debate around the use of singletons in Python programming?
The debate on singletons in Python programming is ongoing. Some see them as useful for certain tasks like logging. Others consider them an anti-pattern to avoid. The discussion focuses on the pros and cons of singletons in software design.
What is the debate around the use of pictures versus text in programming documentation and specifications?
Some argue that “a picture is worth a thousand words” doesn’t always apply in programming. They believe clear text can better explain complex ideas. They advocate for choosing the right tool, as not all systems benefit from visuals, to avoid confusion.
What concerns do developers have about “object-obsessed programming” in Python?
Some developers worry about “object-obsessed programming,” where programmers overuse classes for basic data types. They see this as adding unnecessary complexity and question the programmer’s grasp of programming principles.
What is the debate around the importance of code removal in Python development?
Some developers push for focusing on code removal over addition. They believe writing less code and simplifying projects leads to better software. This approach challenges the idea that programming is just about adding new code.
How do developers view the role of optimization in Python software development?
The debate on optimization in Python development is ongoing. While “premature optimization is the root of all evil” is often cited, some argue optimization should be considered early on. They suggest designing with optimization in mind makes future refactoring easier.
