Why Python keeps growing, explained

Why Python keeps growing, explained | The GitHub Blog.
A deep dive into why more people are using Python than ever, its key use cases, and why it’s still so popular 30-plus years after it was first released.

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We using Python a lot more now because of NLP, with spaCy and prodigy. We also started using FastAPI for our APIs.

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We have the same scenario where I work. Some developers are undergoing training on data science and machine learning, and they use Python.

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Python is easier to read and easier learn.
AI/data science are the use case.

But other eco-systems usually use other languages.

For example

  • Go Lang for Kubernetes
  • Finance applications uses Java, C++ where speed is important
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Because now it’s widely used in open source. You can find many packages in your Linux distribution depending on Python and its libraries along with PyGTK and PyQt. You may be surprised how many, more or less known, packages uses Python. If remember correctly Gentoo and Funtoo main tools (like portage for example) uses Python as well. So there is a big chance that when contributing to your favorite distribution sooner or later you would know Python.

My personal example is trackma. I’m using it for managing anime list. This app uses CLI , ncurses, GTK+, Qt version 4 and 5 backends. Quite a lot, huh? It shows how well developed Python and its environment is.

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The reason behind the growth of Python can be understood by below points.

  1. Python is straightforward to learn, read and code.
  2. Cross platform compatibility is also another big reason behind growth of Python.
  3. Versatilities.
    Thanks
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Python is still very useful, especially in data science. And with Python 4.0 in the horizon, the future of the language holds tremendous promise.

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Python has a huge ecosystem, you can find a library for almost anything this is why it keeps growing. Also it is used in many schools now, so young coders are more likely to know Python these days.

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Interestingly Python is now one of the slowest programming languages in terms of performance.

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Wow, I didn’t know that. I hope that will improve it in Python 4.0.

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