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People also ask
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When should we not use Python?
Python might not be recommended for situations where low-level system programming or high-performance computing is required, as it's an interpreted language and can be slower than compiled languages like C or C++.
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What should you not use Python for?
Not ideal work environment One potential drawback of using Python in a work environment is that it may not be the best fit for all types of projects or teams. For example, if a project requires high performance or low-level system access, a language like C++ may be a better choice.
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What isn't Python good for?
Python might not be recommended for situations where low-level system programming or high-performance computing is required, as it's an interpreted language and can be slower than compiled languages like C or C++.
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Why I don't use Python?
2> Language limitations: Python's dynamic typing and interpreted nature can sometimes lead to unexpected behavior or performance issues in large-scale applications. Some developers prefer statically typed languages for better code safety and performance optimization.
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Why should you use Python?
Here are the 12 reasons why you should learn Python: its versatility, extensive libraries, data science capabilities, artificial intelligence (AI), machine learning (ML), web development, scripting, automation, cross-platform compatibility, open-source nature, ease of learning, high demand in the job market, and ...
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Is it worth learning Python in 2024?
The answer is yes, thanks to its easy learning and clear readability. Python lets developers solve problems without getting tangled in hard syntax.
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What is a reasonable reason not to use Python?
Speed limitations Python is an interpreted language, so you may find that it is slower than some other popular languages.
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What are the negatives of Python?
Some of the disadvantages of Python include its slow speed and heavy memory usage. It also lacks support for mobile environments, database access, and multi-threading. However, it is a good choice for rapid prototyping, and is widely used in data science, machine learning, and server-side web development.
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