Python Code: Compiled or Interpreted? The Truth Behind Python Execution

One of the most widely used programming languages nowadays is Python, which is generally suggested for being easy to learn, adaptable, and simple. Especially in the field of Data Science, Python is a must-know programming language to excel in the domain. Because of its easy coding and readability, it is used to build ML models in an easy way.  However, the upcoming learners are mostly confused by the frequently asked question like  “Is Python code compiled or interpreted?”. The solution to this problem is not as simple as you may think, because producing effective and efficient code requires a grasp of how Python execution works. This blog post will unbox the real story of how Python executes your code, the functions of the Python interpreter and compiler, and what actually occurs when you hit the run button.

Question in our Brain: Is Python Compiled or Interpreted?

The short answer to the above question is that Python is both compiled and interpreted.

When you build a Python program, it is first compiled into bytecode, which the Python Virtual Machine (PVM) interprets. The advantages of compilation and interpretation are combined in this hybrid execution architecture.

Let’s examine it in detail.

Understanding the difference between Compiled and Interpreted Languages

Before we get to know about the specifics of Python, it is vital to figure out what “compiled” and “interpreted” languages are in programming.

Compiled Languages: Compiled languages like C or C++ use a compiler that converts the source code into machine code before the program runs. This machine code is customized for specific hardware, allowing the program to execute on its own from the original source code or the compiler.

Interpreted Languages: In this, early versions of BASIC or JavaScript are processed line-by-line by an interpreter. The interpreter reads the source code and executes the instructions directly, which results in slower performance.

How Python Executes Code: Compilation and Interpretation

1. Parsing the Source Code

The Python interpreter reads and breaks down the source code of a Python script (a.py file) before transforming it into an internal data structure called an Abstract Syntax Tree (AST) and examining it for syntax mistakes.

2. Bytecode Compilation

The AST is then converted into bytecode, a lower-level, platform-neutral representation of the code, by Python. This bytecode is a collection of instructions for the Python Virtual Machine (PVM), not machine code. Evidence of this step is frequently visible in the __pycache__ directory, where .pyc files are stored.

3. Bytecode Interpretation

Ultimately, the bytecode is interpreted and run instruction by instruction by the Python Virtual Machine. The PVM reads the bytecode, executes the operations, and controls memory, exceptions, and other runtime duties. This is where Python’s interpreted nature really shines.

Why Is This Hybrid Method Used in Python?

The execution model of Python has the following benefits:

1. Dynamic Typing

You don’t have to declare data types ahead of time. This speeds up development but requires careful coding practices.
Python variables don’t need explicit type declarations.

The type is determined at runtime →

x = 10     # int

x = "AI"   # str (type changes dynamically)

This makes Python flexible, but slower than statically typed compiled languages.

2. Platform Independence:

Python applications can run on any operating system with a suitable interpreter thanks to Bytecode’s platform independence.

3. Ease of Development:

Developers can write and run code without a separate compilation step, enabling rapid prototyping and interactive development.

4. Extensive Standard Library

Python has a comprehensive library that handles everything from file management to web development, minimizing the need for extra libraries for many common tasks. 

5. Object-Oriented and Functional Programming

There is support for both object-oriented programming and functional programming paradigms, giving developers flexibility in how they structure their code.

6. Late Binding

Function or method resolution happens at runtime, not compile time.

Example:

class A:

    def greet(self): print("Hello from A")

class B:

    def greet(self): print("Hello from B")

def call(obj):

    obj.greet()   # Late binding – resolved at runtime

This is powerful in polymorphism but can increase runtime errors if not handled carefully.

Compilers vs. Interpreters: A Quick Comparison

Feature	Compiled Languages	Interpreted Languages	Python’s Approach
Translation	The whole program at once	Line by line	Source to bytecode, then line-by-line execution
Output	Machine code	No intermediate output	Bytecode (.pyc files)
Speed	Fast execution	Slower execution	Moderate (can be optimized)
Platform Dependency	Platform-dependent	Platform-independent	Platform-independent bytecode
Error Reporting	All errors at once	Errors per line	Syntax errors at compile, runtime errors during execution

Common Misconceptions

• “Python is only interpreted.”

[ Not quite. Python always compiles code to bytecode before interpreting it.]

• “Python code runs directly on the CPU.”

[ No. Bytecode is executed by the Python Virtual Machine, not directly by the hardware. ]

• “Compiled code means no need for the interpreter.”

[ With Python, you still need the interpreter (PVM) to execute the bytecode]

Compilation in Python

When you run a Python program (.py file), the following compilation steps occur before execution:

1. Source Code (.py)
   You write high-level code in .py files. This is human-readable and platform-independent.
   
2. Lexical Analysis
   The Python interpreter scans your code character by character and converts it into tokens (identifiers, keywords, operators, literals).
   
   Example:

x = 5 + 2

Parsing
These tokens are arranged into an Abstract Syntax Tree (AST), a structured tree that represents the code’s grammar and logic.

Compilation to Bytecode (.pyc)
The AST is compiled into bytecode instructions, a low-level, platform-independent representation of the code.

• Stored in the __pycache__ folder as .pyc files.
  
• These .pyc files help Python run faster the next time you execute the program (no need to recompile).
  

Example of bytecode (using dis module):
import dis

def add(a, b): return a + b

dis.dis(add)

Output (simplified):

LOAD_FAST 0

LOAD_FAST 1

BINARY_ADD

RETURN_VALUE

So yes — Python compiles your code into bytecode before running it.

Interpretation in Python

Once compiled, the Python Virtual Machine (PVM) interprets the bytecode.

• The PVM is a stack-based virtual machine that executes bytecode instructions line by line.
  
• Unlike compiled C/C++ machine code (which runs directly on hardware), Python bytecode requires the PVM to translate and execute instructions at runtime.
  
• This is why Python is slower than fully compiled languages — every bytecode instruction is interpreted at runtime instead of being executed directly by the CPU.
  
  This is what makes Python an interpreted language — the final execution happens via interpretation, not direct binary execution.

Why Python is Called an Interpreted Language

Even though Python involves compilation to bytecode, we still categorize it as interpreted because:

• The compilation step is transparent to the user (you don’t manually run a compiler).
  
• Execution depends on the interpreter (PVM).
  
• Python code cannot run without the interpreter installed on the machine.

Thus, Python’s identity leans toward interpreted even though compilation plays a role.

Dynamic Typing and Late Binding

Another reason Python is strongly associated with interpretation is its dynamic typing system and late binding.

1. Dynamic Typing
   
   • Variables in Python don’t require type declarations.
     
   • Types are determined at runtime by the interpreter.

x = 5       # integer

x = "hello" # now a string

This flexibility is only possible because the interpreter manages variable types dynamically.

Late Binding

• The binding of method calls, attributes, and variable references happens at runtime, not at compile-time.
  
• Example:

def func(): return "Hello"

obj = func

print(obj())  # Bound at runtime

This runtime behavior reinforces Python’s interpreted nature.

Just-In-Time (JIT) Compilation (Optional with PyPy)

• CPython (default Python) → Compiles to bytecode + PVM interprets.
  
• PyPy → Uses JIT to further compile bytecode into machine code at runtime, improving performance.
  
• This shows Python is evolving from pure interpretation toward hybrid execution models.

Performance Implications

• Compiled languages (C, Rust) → Faster, machine code execution.
  
• Python (compiled + interpreted) → More flexible, portable, easier to debug, but slower.
  
• Workarounds:
  
  • Use JIT compilers (PyPy, Numba).
    
  • Interface with C/C++ (Cython, ctypes).
    
  • Use multiprocessing for CPU-bound tasks.

The Role of the Abstract Syntax Tree (AST)

• After parsing, Python converts your source into an AST, a tree structure that represents the syntactic structure of code.

Example:

x = 3 + 4

 → AST would have Assign node → BinOp(Add) → Num(3), Num(4).

• Why important?
  
  • Optimization opportunities (constant folding: 3+4 → 7 at compile time).
    
  • Tools like ast module allow introspection and code manipulation at this stage.

Python Bytecode & Instruction Set

• Python doesn’t directly compile to machine code → instead, bytecode for the PVM.
  
• Bytecode = low-level instructions like LOAD_FAST, STORE_NAME, BINARY_ADD.

Example:

a = 10

b = 20

print(a+b)

 Compiled Bytecode (using dis module):

0 LOAD_CONST     1 (10)

2 STORE_NAME     0 (a)

4 LOAD_CONST     2 (20)

6 STORE_NAME     1 (b)

8 LOAD_NAME      2 (print)

10 LOAD_NAME     0 (a)

12 LOAD_NAME     1 (b)

14 BINARY_ADD

16 CALL_FUNCTION 1

18 POP_TOP

20 LOAD_CONST    0 (None)

22 RETURN_VALUE

• Shows Python compilation is real, but target is PVM bytecode, not machine code.

Why Python is Slower than Purely Compiled Languages

• Each bytecode instruction requires interpretation by the C-based PVM loop.
  
• Overhead:
  
  • Dynamic Typing (type checks at runtime).
    
  • Boxing/unboxing of primitive values (int, float are objects).
    
  • Garbage Collection (GC) overhead.

The Interpreter Loop (Eval Loop)

• Inside CPython, the core execution is PyEval_EvalFrameEx.
  
• It’s essentially a big switch statement:
  
  • Fetch next bytecode instruction.
    
  • Dispatch execution.
    
  • Update program counter.
    
• This loop = reason Python is interpreted at runtime.

Caching & .pyc Files

• Python caches compiled bytecode in the __pycache__ directory.
  
• These .pyc files help skip re-compilation in subsequent runs.
  
• Still need PVM to interpret them → platform independent but not hardware-native.

Dynamic Typing in Depth

• Every value in Python is an object, even primitives (int, float).
  
• Variables are just references to objects, not memory slots.

Example:

x = 5     # x → object of type int

x = "AI"  # x now → object of type str

• The type & memory location are checked at runtime → slower but flexible.

Late Binding & Name Resolution

• Python uses LEGB Rule for scope resolution:
  
  • Local → Enclosing → Global → Built-in.

Example:

def outer():

    x = "Enclosing"

    def inner():

        print(x)  # resolved only at runtime

    return inner

f = outer()

f()  # prints “Enclosing”

• This late binding enables closures, decorators, dynamic function calls.

CPython vs. Alternatives

• CPython → Standard implementation (interpreted bytecode).
  
• PyPy → JIT compilation → translates hot paths to machine code.
  
• Cython → Transpiles Python to C → compiled to native machine code.
  
• Jython → Compiles Python to JVM bytecode.
  
• IronPython → Compiles Python to .NET CLR bytecode.

Performance Enhancements

• Python’s hybrid model sacrifices speed for flexibility.
  
• Advanced ways to optimize:
  
  • Numba → JIT compilation for scientific code.
    
  • Cython → Add type hints, compile to C.
    
  • Multiprocessing → bypass GIL for CPU-bound tasks.
    
  • Vectorization (NumPy/Pandas) → shift computation to C/Fortran backend.

Why Python is Both Compiled & Interpreted

• Compiled: Source → Bytecode (.pyc), platform-independent.
  
• Interpreted: Bytecode → Executed by PVM line-by-line at runtime.
  
• Dynamic: Types resolved at runtime, not compile time.
  
• Hybrid Advantage: Portability + developer productivity at the cost of raw speed.

Compilation vs. Interpretation in Python

Feature	Compilation in Python	Interpretation in Python
Stage	Source → Bytecode (.pyc)	Bytecode → Execution
Output	Bytecode files (platform-independent)	Runtime execution
Performance	Faster on repeat runs (cached .pyc)	Slower due to runtime interpretation
Dependency	Requires Python compiler (built-in)	Requires Python Virtual Machine (PVM)
Error Handling	Syntax errors detected at compile stage	Runtime errors appear during execution

Conclusion: The Real Story of How Python Works

So, is Python interpreted or compiled? In real terms, Python is both. After converting the source code to bytecode, it uses the Python Virtual Machine to interpret the bytecode. Python’s versatility, portability, and user-friendliness stem from its hybrid execution theory, which makes it a popular choice for both beginners and experts.

Developers may write more effective code, debug more successfully, and appreciate Python’s strength and clarity by discovering this execution mechanism. Understanding the reality of Python execution enables you to fully utilize this amazing language, regardless of your level of programming experience.

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