Limitations of Python Programming Language

Personally, I love Python Programming Language, and there are countless reasons that you should love python programming language as well. Python is simple and easy to learn. Python Programming has a big and supportive community, and they are continuously struggling to create new Python libraries and modules to make it easier and easier for us.

Upon till now, you might not have seen someone talking about the downside of the Python programming language. Though there are some of our old chums, who hate Python Programming language for only one reason, and that reason is the simplicity of the Python Programming language. They think one should get into programming language by the hard way as they were doing in the fast. They think you should start from scratch, like to start our journey from being an assembly language programmer, and to some extent, I agree with them.

Major Limitations Of Python Programming Language:-

To be honest with you, these limitations can be removed from the Python programming language but these limitations might be for a reason. I do not want to spread hate for the python programming language and as previously mentioned I am python addicted. Every Programming language has some limitations and they are for a purpose and that's why we have so many programming languages. As a Famous quote “Everybody is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid.” 


So get ready and let's start with some of the major Limitations of Python Programming language for which python Programming language is criticized.

  1. Jack of all trades

    Python is a Jack of all trades, master of none. Best language I've ever found for prototyping or sketching out a concept, but for the final implementation… yeesh. It just doesn't have that last polish, of performance, of security, of parallel/concurrent ability, of hardware level functionality, whatever. Just that last mile or last whatever is lacking. But then nothing is perfect. I think someone around here compared programming languages to cars. Python is your classic Japanese 4 cylinder sedan. Not even a make or model. 4 doors, seats 5ish, nice sized trunk, will most likely get you where you want to go, unless you want to get there RIGHT NOW, in the middle of Alaska, with a baseball team, pulling a horse trailer, etc.
  2. Lack of optional static typing

    Static Typing stop certain bugs. In Python Programming language there's a “typing” library that does static typing, but do not think if python compiler really treats the types declared there the way C or java compiler treats their respective types. There is a third-party tool called MyPy which does do static types analysis as much as it can on the basis of type annotations, but that also have some limitations. Type annotations as most likely be a documentation at this point. Python is strongly typed (there are no implicit type conversions), just not statically typed. JavaScript, Perl and C are examples of weakly typed languages with implicit casts. Strong but dynamically typed languages are those like Python and Ruby, which don’t do implicit casting except from int to float in mathematical operations.
  3. Performance issues

    Python is slower than C and C++. Most think that there are another version of Python known as CPython, but CPython is slow, and all the options for making it faster also fails. Cython is basically just Python with some inline C, and that is weird.And as you know C has enough problems by itself. Another important think about Jumping back and forth between the highest level language and the lowest level language in the same source file is something that is not effective. Being an intrepreted language the time taken to execute a task by python is more than compared to C and C++ but python is a high-level, unlike C or C++. Another reason for the performance issue is due to the Global interpreter lock (GIL). Python is designed so that it is easy to read and learn. Solution to this Problem may be is if you absolutely need every last ounce of performance, don’t use Python.
  4. Threads and Mulitprocessing

    Python thread are okay for small scale I/O Bound tasks. But it fails in large scale multithreading and i have faced it so many times. Thread are just a lie in Python.

    Multiprocessing in Python is pretty fantastic, but there is a limitation on the number of Processes that you can create. you can only have so many processes on one machine before you need another one. Sharing data between them means you’re serializing data and shooting it through a pipe or socket to another process which has to deserialize it and send results back to the parent in the same way.

    We have async and await for that now. They are also actually pretty good for some stuff, and you can get great density for I/O-bound stuff, but it feels more hacked on than baked in. Like, you can call a regular function in an async function, but you can’t await an async function in a regular function, so you’re either all-in or all-out. Significant portions of the Python multiverse are being reimplemented for async. This is good, but it basically means writing everything twice. It would be a lot nicer if the language had a built-in scheduler and event loop, and it would just yield to the loop every time you hit I/O or slept.

  5. Indentation as syntax

    The problem is that indentational syntax can cause hard-to-detect bugs if you make the slightest accidental misalignment. This syntax also profoundly constrains your freedom to format your code as you like. I find this ironic, given Python’s claim of flexibility. Indentational syntax can also present issues with deeply nested code, and with line continuations.
  6. excessive memory usage

    Pythonhave excessive Memory usage issues. It use a lot of menmory and hence is not a good option to use it in situation where the memory is important for us. The execeive memory usage issue is due to being a high level programming language. Thats why Python is not good for High graphic 3D game take lot of CPU.
  7. interpreting language

    it’s an interpreting language, which means that it will run uninhibitedly slower than the same code written in a natively compiling language. it’s an interpreter, which lends itself for a lot of quick hacking without spending time of properly designing any non-trivial program
  8. Dynamic typing

    Dynamic typing implementation makes Python slow. Even slower than Emacs Lisp. Common Lisp implementations prove that dynamically typed languages could be faster.
  9. Platform Dependent

    (Windows specific, tell me if there is anything like this in Mac/Linux…) the lack of the ability to register/use a COM exposed library (DLL) or ActiveX control (OCX). For example, in Excel or any MS Office product that supports VBA macros, you can click Tools > References, and you see a list of every COM exposed library on the computer (you can also Browse to a file if it’s not registered). If you put a check box next to one, you can instantiate and use that library inside your program. You can view all its properties/methods in the object browser. In the editor when you declare a variable of that type, you get full IntelliSense of all its members. This makes it VERY easy to be productive. With this I can use VBA to automate pretty much any application installed on my computer (like Microsoft Windows Media Player, Quicktime, Crystal Reports, Outlook, Word) or use custom controls in my own forms, from Excel. I know that IntelliSense and object browsers are more the IDE than language, but under the hood the language might be designed to make those easier to implement.
  10. Problem with Package system

    The package system is horrible. Pip does okay, but it’s a totally PITA if you aren’t always on the bleeding edge. pip is the most popular tool for Python packaging, but has a lot of problems in practice. pip can break your system libraries if you type “sudo pip install”. conda being the alternatives to pip, solve some of these problems. conda is better than pip in many ways, but it still has the “install latest version of packages by default” problem, your environment and maintain every downstream dependency yourself, making it very difficult upgrade dependencies. conda also has performance issues, and takes up much more disk space than pip for an equivalent set of packages. It’s painful to get a proper pip files setup and running if you plan to share the project. Last time I used pip, you couldn’t update all the plugins without writing a script, and that’s weird.
  11. No native GUI support

    There is no native GUI support. The IDE shows you all the available events & methods in a dropdown. Like any other programming language, you can create GUI with python. But it is hard to create GUI with python by manually coding all the things. If you can look at Java where you can have a drag and drop environment that automatically creates a Graphical user interface for you. There are some library in python which can help us create a Graphical user interface more easily.
  12. Dependencies

    This issue is mostely realted to the package managemnt but any way this is another problem with python programmig language. Although python community is continously strunggling to create new libararies and packages for us and they have already crated tons of packages and most of them are on top of eachother. as we know python is not a backward compatible and there are some deprecated packages and funtions. so once a package is created in the past and if they are using another python library. so if that library got updated then there is a big problem for you to install that dependent library and you have to recreate the library.

Final Thoughts:-

I do like Python, and I routinely use it for small, scripty things. It’s easy. I don’t find too many surprising things, though there are certainly some, and that’s important when you want to build something quickly — a need-to-use-now script or a prototype. Dynamic typing can help when playing with things. There are tons of libraries that are useful. I don’t mind the indentation-as-semantics, though I have a friend who is vehement that he will “never use a language in which cut and paste changes semantics.” Honestly, though, I’ve only found myself moaning about it a few times. You get used to it. If you are interested in other python tutorials please visit my youtube channel Code with Ali.

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