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1.5 What's Python Not Good For?
To be fair again, some tasks are outside of Python's scope. Like all dynamic languages, Python (as currently implemented) isn't as fast or efficient as static, compiled languages like C. In many domains, the difference doesn't matter; for programs that spend most of their time interacting with users or transferring data over networks, Python is usually more than adequate to meet the performance needs of the entire application. But efficiency is still a priority in some domains.
Because it is interpreted today, Python alone usually isn't the best tool for delivery of performance-critical components. Instead, computationally intensive operations can be implemented as compiled extensions to Python, and coded in a low-level language like C. Python can't be used as the sole implementation language for such components, but it works well as a frontend scripting interface to them.
For example, numerical programming and image processing support has been added to Python by combining optimized extensions with a Python language interface. In such a system, once the optimized extensions have been developed, most of the programming occurs at the higher-level Python scripting level. The net result is a numerical programming tool that's both efficient and easy to use.
Moreover, Python can still serve as a prototyping tool in such domains. Systems may be implemented in Python first, and later moved in whole or piecemeal to a language like C for delivery. C and Python have distinct strengths and roles; a hybrid approach, using C for compute-intensive modules, and Python for prototyping and frontend interfaces, can leverage the benefits of both.
In some sense, Python solves the efficiency/flexibility tradeoff by not solving it at all. It provides a language optimized for ease of use, along with tools needed to integrate with other languages. By combining components written in Python and compiled languages like C and C++, developers may select an appropriate mix of usability and performance for each particular application. While it's unlikely that it will ever be as fast as C, Python's speed of development is at least as important as C's speed of execution in most modern software projects.
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