Why is Python so much of a pain for Enterprise Projects?

In this article, we will learn why python is not the most suitable for larger projects.

Python is preferred as a programming language by millions of developers, making it one of the most popular open-source programming languages with a large developer community. However, some engineers regard Python as a software development anomaly. They consider Python to be primarily a "glue" scripting language, more suited for routine system automation or integrating two applications. That is not correct; Python as a programming language is capable of producing industrial−strength software.

Under the hood, Python contains every feature that makes it ideal for a wide range of software development projects. It is open−source and uses an object-oriented approach. Additionally, hundreds of thousands of modules and libraries expand the scope of what Python can do.

However, Python continues to fail to penetrate one computing segment−enterprise development. Python is still considered primarily just as a scripting language for enterprise−level software development.

Absence of a Graphical User Interface (GUI) and teamwork tools

The fundamental reason Python may not be suitable for full−stack development at the enterprise level is that creating graphical user interfaces in Python is overly complicated.

Python comes with Tkinter, which is commonly used by Python developers to construct interfaces but is not powerful enough to suit the needs of large-scale applications. Modern software development is highly graphic, and Python suffers from a lack of a decent GUI tool.

Absence of teamwork tools

The other reason Python is not commonly used for large−scale full−stack development is a lack of tools for team collaboration. This shortcoming is unacceptable in corporate software development and is seen as a potentially fatal flaw. Because they provide dedicated capabilities to assist teams to cooperate while working on large-scale projects, programming languages like Java and C++ are chosen for enterprise−level full−stack development.

Working with several programmers and using a language with no such assistance tool can be problematic. While Python handles this issue by using modules and namespace resolution to assure that there are no conflicts in a multi−programmer environment, it is still not convincing enough for teams to use it in large-scale projects.

Limitations of the database access layer

Large−scale software development projects need a large amount of data. Terabytes of data are produced or consumed by modern applications, and such data management necessitates the use of well-established technologies such as ODBC and JDBC. Unfortunately, Python's database access layer lacks the robustness of these solutions. Python's database layer may be described as primitive and underdeveloped. This is yet another key problem that renders Python unsuitable for usage in enterprise−level applications by any full−stack developer.

Modern software development is highly agile, and corporations seek programming languages that can interact quickly and smoothly with complex legacy data, as well as SQL database access that is robust and fast. Python does not meet these requirements, and because there are no words on it shortly, it is not suited for large−scale development stacks.

Python is Slow at the Runtime

Another major reason to avoid Python in your stack is that it runs slower than other programming languages such as PHP, JavaScript, C++, or Java. While this may not be an issue with a few hundred lines of code, it becomes a major one when a full− stack developer is required to design massive applications with hundreds of thousands of lines of code.

As a high−level programming language, Python is not highly sensitive to hardware resources. Instead of a compiler, Python code requires an interpreter. Because an interpreter compiles the code line by line, it's easy to see how this can be an issue when working with a large code base. Python is also slow at runtime because it is a dynamically typed language. When we utilize a variable in a dynamically typed language, the variable data type is not defined. The data type is determined at runtime. As a result, every time a variable is read, written, or referenced, the interpreter checks the data type to allocate memory appropriately. It slows down Python code during execution.

Python Provides Inadequate Documentation

Assume you've worked on large−scale projects before. In that scenario, you understand how significant it is to have detailed documents available technology before integrating it into your development stack. Python suffers greatly from a lack of adequate documentation as compared to competing programming languages such as Java, Perl, and PHP.

Moreover, there aren't as many Python books available. In comparison, PHP has twice as many titles on the market as Python. While the online Python documentation is well organized and a nice place to start, it only serves as a sparse(limited) reference source. If your team lacks experienced coders, you may be stranded for a long time without enough learning resources for Python developers to assist you in troubleshooting.

Using Other Languages is Difficult

Many programmers dislike writing code in languages other than their own/native language. This is because they assume that learning other languages is far more difficult. They may be accustomed to programming with Python, the world's most user-friendly programming language. If you are a Python expert, you may be experiencing this problem. Pythonistas admire the language because it is easy to learn, widely utilized, and extremely powerful. One of the key reasons we like Python is that it is simple.


While Python is a robust high-level programming language, it has limits when it comes to large−scale full−stack development. Although the extremely active Python community and devoted development teams around the world are attempting to improve Python's restrictions so that it can be used for enterprise-level development, it is currently best suited for small−scale software development projects.