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Which Python Should You Use? 🧠 CPython, PyPy & More 44 || Python ka Pitara || DeathCode 🐍 Python ek language hai… par kya aap jaante ho uske multiple implementations hote hain? Is video me hum deeply samjhenge CPython, PyPy, Jython, aur IronPython — kaise ye sab internally kaam karte hain, kaha fast hain, kaha slow, aur kis use-case ke liye kaunsa best hai 🔍 Agar aap Python ko sirf likhna nahi, andar se samajhna chahte ho, to ye video aapke liye MUST-WATCH hai 💥 🎯 What You Will Learn: ✅ What is a Python Implementation? ✅ Why CPython is the default & most used ✅ How PyPy uses JIT compilation for speed ✅ Why Jython & IronPython exist ✅ GIL ka behavior in different implementations ✅ Memory management differences ✅ When to use CPython vs PyPy vs others ✅ Real-world trade-offs & limitations 📌 Concepts Covered: ✔ Python Language vs Implementation ✔ CPython Internals (Bytecode, PVM, GIL) ✔ PyPy JIT Compilation ✔ JVM & .NET based Python (Jython, IronPython) ✔ Performance, Compatibility & Ecosystem ✔ C-Extensions support differences 🧪 Real-World Comparisons You’ll See: CPython vs PyPy speed comparison Why NumPy prefers CPython Why PyPy struggles with C-extensions JVM/.NET integration via Jython & IronPython Choosing Python for enterprise vs performance Myths around “Python is slow” 📺 *Series:* Python ka Pitara – DeathCode 🧑💻 *Language:* Hinglish 🎓 *Level:* Intermediate → Advanced 🎬 *Channel:* DeathCode – Python Internals & Deep System Understanding 🔥 Complete Notes(With code): https://deathcode.in/courses/python-k... #PythonKaPitara #DeathCode #pip #PythonModules #VirtualEnv #IIT Python Ka Pitara: • Python Ka Pitara – Zero Se Expert Tak (Sab... Python ka Pitara Repo: https://github.com/DeathCodeYT/python... You can follow us at: ► Telegram: https://t.me/deathcodeyt ► Facebook: / deathcodeyt ► Twitter: / deathcodeyt ► Instagram: / deathcodeyt ► Reddit: / deathcodeyt ► Pinterest: / deathcodeyt ► Our blog: https://deathcodeyt.blogspot.com/ ► Youtube: / @deathcodeyt Keywords: IIT Course python tutorial python python implementations cpython pypy jython ironpython cpython explained pypy jit python jit compiler python interpreter python internals python execution model python bytecode python virtual machine python pvm python gil gil in cpython gil in pypy python memory management python garbage collection python language vs implementation python backend internals python advanced topic python deep dive python ka pitara deathcode python hindi tutorial python by iitian python performance python speed comparison cpython vs pypy pypy vs cpython python enterprise python jvm python dotnet jython explained ironpython explained python c extensions cpython c api why numpy uses cpython pypy limitations python ecosystem python standard implementation python interpreter types python runtime python internals explained python pro concepts python execution flow python code execution python compiled vs interpreted python jit vs interpreter python architecture python backend architecture python cpu performance python memory overhead python object model python dynamic typing internals python advanced course python masterclass python system level python under the hood python core concepts python execution pipeline python real world python python optimization python backend deep dive python software engineering python runtime choices python comparison video python internal architecture python performance myths python why python slow python interpreter explained python deep explanation python internals series deathcode python pitara python expert level python advanced understanding python how python works python language design python interpreter comparison python pypy use cases python production choice python interview internals python core python python high performance python python implementations, cpython vs pypy, python internals, pypy jit, jython python, ironpython python, python interpreter, python execution model, python performance, python ka pitara, deathcode, python hindi tutorial, python advanced, python backend internals