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Asked by Altaf Last Modified
๐น Example: Running a Python program for large-scale data processing will be slower than running a similar program in C++.
๐น Example: Running Python on IoT devices or mobile apps can lead to performance issues.
๐น Example: Popular mobile apps like WhatsApp or Instagram use other languages for better optimization.
๐น Example: Python struggles with high-performance multi-threaded applications like real-time video processing.
โ Solution: Use multiprocessing instead of multithreading in Python.
๐น Example: Python is great for game scripting (like in Blender), but not for building AAA games.
๐น Example: Python might not be the best choice for complex financial systems requiring rapid database transactions.
๐น Example: Python frameworks like Django and Flask handle the backend, but the frontend still needs JavaScript, React, or Vue.
๐น Example: Some legacy Python 2 applications need major updates to run in Python 3.
๐น Example: Python-based web apps require extra security layers to prevent SQL injection and other threats.
๐น Example: Banking and financial institutions prefer Java due to its security & scalability.
Despite these drawbacks, Python remains one of the most popular and versatile languages. Its ease of use, rich libraries, and strong community support make it ideal for web development, data science, AI, and automation.
Would you like recommendations on how to overcome these limitations? ๐
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