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Python pros and cons: Why use Python for building your next project

Jan 17, 2021 · 5 mins de lecture

Python ranks among the top three programming languages in different popularity indexes like TIOBE and PYPL. Why is Python so popular? That’s mostly because Python is widely used in the trendiest domains of computer science, such as machine learning, data science, and artificial intelligence.

With AI penetrating all industries, Python’s popularity will continue growing. However, being actively used and developed since the 1980s, Python has a lot to offer you even if your software has nothing to do with data science.

Considering Python for your next project? Let’s quickly go through the Python pros and cons to check if this programming language is a good fit for your business.

What can you do with Python?

Python is good for many programming tasks and is widely used in web development, game development, desktop apps creation, and IoT programming.

Fast prototyping for web apps, data science, and games

Being an interpreted language with a simple syntax, Python allows building app prototypes and iterating on changes very fast. It’s one of the main benefits of Python. The development process is even more productive due to the availability of libraries and third-party code packages.

Web development with well-supported frameworks

Django, Flask, Pyramid, and Falcon are the most used Python web frameworks. They give developers boilerplates for the fast development of web apps with both monolithic and microservice architecture. With Python, the development cycle of a sufficiently functional MVP is 3–5 times shorter than with Java.

Programming for IoT devices and microcontrollers

With the wide usage of MicroPython and Raspberry PI microcontrollers, Python becomes one of the most used languages in IoT programming. Frequent usage of connected devices in scientific circles extends the usage of Python, as this language is the most familiar to scientists dealing with big data.

Data science, machine learning, and artificial intelligence

Data scientists use Python because it has plenty of libraries for math, data manipulation, and data analysis. Python gives them ready-to-use machine-learning tools (TensorFlow, PyTorch, Theano, Gensim), numerical libraries (NumPy), statistical libraries (Statsmodels, SciPy), data visualization and plotting tools (Matplotib, Seaborn), as well as computer vision and image processing solutions.

Advantages of Python

Numerous benefits of Python make it stand out among other languages.

The clean and concise syntax for readable code

Python has a simple syntax that reads like English and usually requires writing fewer lines of code to achieve needed results compared to other languages. Simplicity makes Python easy to learn and fast to write and review. Python codebase takes developers less effort to navigate and understand, which simplifies its maintenance.

A large standard library working like a swiss army knife

Python developers can leverage an extensive cross-platform library. It contains lots of modules pre-written in Python and C that can be used anywhere from web development through game development to machine learning. They help developers complete standard programming tasks by reducing the volume of manual coding.

PyPi repository with over 280,000 code packages

Python Package Index (PyPi) contains an ever-growing collection of reusable code packages both paid and free-of-charge that was developed and shared by the Python community.

Extensibility with C/C++/Java programming languages

As Python is compatible with other languages, you can write performance-critical parts of your app in C or Java and integrate them into your Python app as extension modules. This feature makes Python a great glue language for integrating large parts of apps written in compiled languages.

Embeddability of Python scripts in other languages

You can enrich your C/C++/Java code by embedding Python scripts into it. You can do it to allow others to extend the functionality of your apps, for example, by creating macros and extensions for apps or mods for video games.

Сode running on different environments and platforms

Python code can run on different platforms using several implementations or runtime environments, such as IronPython (Python running on .NET), Jython (Python running on the Java Virtual Machine), MicroPython (Python running on microcontrollers).

Disadvantages of Python

Despite being continuously improved Python still has some limitations.

Low code execution speed in CPython interpreter

Apps built with Python can run up to 100 times slower than their analogies written in C, Go, or Java. Python code is executed line-by-line inside an interpreter, which slows it down. In large projects, Python’s low execution speed can become a bottleneck for users. However, this problem can be avoided if the project is built using PyPy implementation with a JIT compiler instead of the default CPython implementation.

High memory usage in Python processes

As Python’s interpreter performs memory management automatically using a garbage collector, developers have less control over memory consumption. It makes it more difficult for them to write the memory-efficient code and cope with memory leaks.

Fake multithreading in CPython implementation

Dynamic memory management in CPython is not thread-safe. In order to support multi-threaded Python programs, CPython provides a global interpreter lock (GIL). GIL doesn’t allow multiple threads to run Python code at the same time to prevent them from using the same resources simultaneously. As a result, a lot of the threads’ time is spent waiting for resources. However, this isn’t a problem in the alternative implementations (Jython or PyPY) as they manage memory in a different way.

Need a guru knowing Python pros and cons?

Python can bring a lot of value to businesses that choose it for developing software. To uncover the whole potential of Python, you should hire true Pythonistas who know all the strengths and weaknesses of this language.

On Proxify.io you’ll find programmers who have previously built Python projects of different complexity. Whether you need a quick MVP or a large and maintainable app, they’ll help you make the most out of Python. Reach out to us now, to have a perfect Python developer matched to your project within two weeks.

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