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The complexity of modern web apps lies far beyond creating eye-catching user interfaces with countless elements. To enable lag-free experience and effortless scalability, it’s important to pay due attention to the architecture design, which can be pretty challenging. Under the hood of a full-featured online app, different frameworks and libraries can peacefully coexist with different programming languages used to build software. Since the equation may contain so many variables, it’s essential to master your knowledge of each potential system component to know when and why to use them.
Python, in its turn, is a bit older. This general-purpose language was developed by Guido van Rossum in 1991. One of the main advantages of Python is that it’s very easy to learn and interpret. The code written with it is as easy to read as a book for those who have some experience with programming.
However, despite such simplicity, Python is not a “noob language” intended for people who just started learning how to code and want to switch to something more “serious” in the future. It supports functional, imperative, object-oriented, and procedural programming and is considered one of the best tools for Data Science. NumPy, Pandas, PyTorch, TensorFlow, and dozens of other frameworks and libraries can turn Python into a swiss knife when it comes to working with huge datasets and Machine Learning algorithms. Additionally, such popular server-side Python frameworks as Django, for example, make it a decent choice for any custom software development company delivering top-notch web apps.
Quickly learning new technologies and writing code in no time is a good thing with no doubt. But a fast development pace doesn’t necessarily mean that the final software system will work fast. Especially when we talk about interpreted programming languages since in this case programs aren’t compiled into native code.
Python, on the other hand, can’t handle heavy apps with such ease. Do you remember that we considered Python as one of the easiest programming languages to read? Well, unfortunately, this luxury comes with its price. A high level of abstractions requires a longer time for interpretation. Our language differs from those which computers speak. Therefore, the easier the code is for a human to understand, the more difficult it is for a computer to interpret and execute it. Luckily, there are such Python implementations as Jython and Cython whose main purpose is to increase the overall performance, but despite all the efforts of their developers, they can’t compete with the V8 engine.
For example, Python takes on one of the leading roles in the world of Machine Learning. Making a software system learn from the available data using complex algorithms is a challenging task by itself. Python’s easy-to-understand syntax allows avoiding additional complexities associated with the need to understand poorly readable code. As a result, developers can focus on experimenting and make changes as quickly as possible.
Since Python is an interpreted language, you can forget about compatibility issues and run code on different platforms with no effort. Additionally, such full-featured libraries like TensorFlow and Keras enable developers to improve their efficiency when it comes to deep learning.
Data Science is another example of the area where Python can be considered a “dominant force.” As we already mentioned Pandas and NumPy can help with data analysis and mathematical functions while Matplotlib can take care of creating 2D charts.
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