5 Major Disadvantages of Python:
There are good reasons to choose a programming language. We all love Python and use it because it is the easiest, popular, and powerful programming language out there in the programming world. But it is not perfect. Python has both pros and cons compared to other programming languages. We all know the pros of Python, don`t we? Today, let’s take a look at some of the shortcomings of Python that you may not know about. What are the main drawbacks of Python? The main drawbacks of Python are its slow running speed, difficulty switching to another programming language, weaknesses in mobile application development, high memory consumption, and low popularity in the field of enterprise development. .. Let’s take a closer look at each in
1.Slow Runtime
Python is measurably slower at runtime compared to other programming languages like Java, C++, PHP, Java script, Swift, etc. This is a major concern for programmers when they are writing giant programs with lots of lines of code. Python is not closer to hardware because it is a high level programming language, unlike C or C++. Python code execution takes place with the help of an interpreter instead of the compiler. The interpreter executes the code line by line, which causes it to slow down. Python is a dynamically typed language. That means, it executes many common programming behaviors at runtime that static programming languages perform during compilation. In dynamically typed languages, we don`t need to specify variable data types while assigning variables. The data type is assigned to the variable during runtime. You can read this article to learn more about the differences between statically typed and dynamically typed languages. Hence, every time the variable is read, written, or referenced, its data type is checked and the memory is allocated accordingly. That is one of the reasons why statically typed languages like C, C++, and Java execute faster at runtime. But, even dynamic languages like Java script and PHP are faster than Python during runtime. Why? One of the main reasons for the slowness of Python is the presence of Global Interpreter Lock (GIL). GIL allows only one thread to execute at a time even in a multithreaded architecture with more than one CPU core. As a result, multithreaded CPU bound programs may be slower than single threaded ones. We need to implement multiprocessing programs instead of multithreaded ones to improve the speed. But, there is a lot to be said about the simplicity of writing code. We all know how much development time is saved by using Python in our projects. Even though it is not the `fastest to run` programming language, it is the `fastest to write` programming language. Don`t forget that the servers are also getting faster.
Hence, it is reasonable to write your code in Python. Later, if your app becomes successful, then you can optimize the code using several methods. So, do not worry about the speed of the language. Focus on building beautiful apps.
2.Not Best for Mobile Application Development
3.Programmers Facing difficulty in Other Programming Language
Many Python programmers don’t enjoy programming in other programming languages. This is because they find it very difficult to use other languages. These can be used to program in Python, the simplest programming language in the world. If you are a hardcore Python fan, you may be facing this problem. As Pythonistas, we love Python because it’s simple, popular, and powerful. Of these, the main reason I love Python is its simplicity. Python is very easy to program when compared to other languages such as C ++ and Java. Python is close to human languages and I hate some other languages that are close to hardware. Most people who are new to programming prefer to learn Python over other languages because Python is simple. Many universities also use Python as an entry-level programming language to teach students. Simplicity is one of the main advantages of Python, but it can be a disadvantage for programmers, especially if you need to learn and code in other languages. You can’t do everything with Python. If you just need to program in Python while you’re working, you’re in luck. However, most of us may need to use other programming languages for specific needs. Now it’s a little more difficult. Switching from Python to a new language is difficult because programmers may find it difficult to adapt to new features. It will take some time to get used to the new language. I love Python, but it’s not religious. Don’t expect to use Python alone for your entire career. You need to be able to adapt to other programming languages and technologies. I know most Python programmers don’t like this, but if you work for a company, you may need to deal with other languages as well. You have more freedom when you work for yourself as a freelancer. Full stack web developers can’t do everything in Python. You may also need to learn java script to do cool frontend stuff. It may seem overwhelming at first, but it’s important to have an open mind about learning technology. Most concepts are similar in all programming languages. If you are familiar with Python, you can learn other programming languages. Therefore, be prepared to adapt to any technology as needed. It is always good to have “T-shaped knowledge” in software development. You need to know as much technology as you can. However, you need to be a specialist in a particular technology. So learn other programming languages, but become a Python expert.
4.High Memory Consumption
For memory intensive tasks, Python is not always the best choice. The memory consumption of Python is high due to the flexibility of the data types. Python has automatic garbage collection when objects go out of scope. Due to this functionality, Python intends to remove a lot of the complexity of memory management that languages like C and C++ involve. However, for large and long running systems developed using Python, dealing with memory management is difficult. In Python, the interpreter keeps reference counts to objects being used. When an object is no longer referenced, the garbage collector can free the object and reclaim its allocated memory by calling methods such as free () and delete (). If you’re looking for a memory leak in your code, one way is to use Heapy to find the object that is using the most memory. Heapy is a handy tool for debugging memory usage. So it would be great if you could get a better understanding of the space efficiency of your code and the underlying packages used before writing Python code for very memory-intensive tasks. However, there are many solutions that can help you identify memory problems and fix memory leaks.
5.Rarely use in Enterprise Development Sector
A powerful programming language that minimizes programmer worries, Python has made great strides in the field of large-scale web development. However, despite its popularity, Python has not been able to penetrate corporate development. One of the main reasons for this could be Python’s restrictions on database access. Compared to common technologies such as JDBC and ODBC, Python’s database access layer is a bit underdeveloped and primitive. Therefore, it usually does not apply to companies that require smooth interaction of complex legacy data. Also, the absence of GUI and teamwork tools, lack of true multiprocessor support, absence of a commercial support point, and the lack of availability of Python in mobile application development might be some potential weak points. Python is also very rarely used on the client side of a web application. It also has some design restrictions since it is dynamically typed. These might be some of the reasons why Python is not often used in the enterprise development sector, as compared to languages like Java.
